WorldWideScience

Sample records for behaviorally based predictions

  1. Extending Theory-Based Quantitative Predictions to New Health Behaviors.

    Science.gov (United States)

    Brick, Leslie Ann D; Velicer, Wayne F; Redding, Colleen A; Rossi, Joseph S; Prochaska, James O

    2016-04-01

    Traditional null hypothesis significance testing suffers many limitations and is poorly adapted to theory testing. A proposed alternative approach, called Testing Theory-based Quantitative Predictions, uses effect size estimates and confidence intervals to directly test predictions based on theory. This paper replicates findings from previous smoking studies and extends the approach to diet and sun protection behaviors using baseline data from a Transtheoretical Model behavioral intervention (N = 5407). Effect size predictions were developed using two methods: (1) applying refined effect size estimates from previous smoking research or (2) using predictions developed by an expert panel. Thirteen of 15 predictions were confirmed for smoking. For diet, 7 of 14 predictions were confirmed using smoking predictions and 6 of 16 using expert panel predictions. For sun protection, 3 of 11 predictions were confirmed using smoking predictions and 5 of 19 using expert panel predictions. Expert panel predictions and smoking-based predictions poorly predicted effect sizes for diet and sun protection constructs. Future studies should aim to use previous empirical data to generate predictions whenever possible. The best results occur when there have been several iterations of predictions for a behavior, such as with smoking, demonstrating that expected values begin to converge on the population effect size. Overall, the study supports necessity in strengthening and revising theory with empirical data.

  2. Mining Behavior Based Safety Data to Predict Safety Performance

    Energy Technology Data Exchange (ETDEWEB)

    Jeffrey C. Joe

    2010-06-01

    The Idaho National Laboratory (INL) operates a behavior based safety program called Safety Observations Achieve Results (SOAR). This peer-to-peer observation program encourages employees to perform in-field observations of each other's work practices and habits (i.e., behaviors). The underlying premise of conducting these observations is that more serious accidents are prevented from occurring because lower level “at risk” behaviors are identified and corrected before they can propagate into culturally accepted “unsafe” behaviors that result in injuries or fatalities. Although the approach increases employee involvement in safety, the premise of the program has not been subject to sufficient empirical evaluation. The INL now has a significant amount of SOAR data on these lower level “at risk” behaviors. This paper describes the use of data mining techniques to analyze these data to determine whether they can predict if and when a more serious accident will occur.

  3. Scanpath Based N-Gram Models for Predicting Reading Behavior

    DEFF Research Database (Denmark)

    Mishra, Abhijit; Bhattacharyya, Pushpak; Carl, Michael

    2013-01-01

    Predicting reading behavior is a difficult task. Reading behavior depends on various linguistic factors (e.g. sentence length, structural complexity etc.) and other factors (e.g individual's reading style, age etc.). Ideally, a reading model should be similar to a language model where the model i...

  4. Behavior-Based Budget Management Using Predictive Analytics

    Energy Technology Data Exchange (ETDEWEB)

    Troy Hiltbrand

    2013-03-01

    Historically, the mechanisms to perform forecasting have primarily used two common factors as a basis for future predictions: time and money. While time and money are very important aspects of determining future budgetary spend patterns, organizations represent a complex system of unique individuals with a myriad of associated behaviors and all of these behaviors have bearing on how budget is utilized. When looking to forecasted budgets, it becomes a guessing game about how budget managers will behave under a given set of conditions. This becomes relatively messy when human nature is introduced, as different managers will react very differently under similar circumstances. While one manager becomes ultra conservative during periods of financial austerity, another might be un-phased and continue to spend as they have in the past. Both might revert into a state of budgetary protectionism masking what is truly happening at a budget holder level, in order to keep as much budget and influence as possible while at the same time sacrificing the greater good of the organization. To more accurately predict future outcomes, the models should consider both time and money and other behavioral patterns that have been observed across the organization. The field of predictive analytics is poised to provide the tools and methodologies needed for organizations to do just this: capture and leverage behaviors of the past to predict the future.

  5. Predicting Study Abroad Intentions Based on the Theory of Planned Behavior

    Science.gov (United States)

    Schnusenberg, Oliver; de Jong, Pieter; Goel, Lakshmi

    2012-01-01

    The emphasis on study abroad programs is growing in the academic context as U.S. based universities seek to incorporate a global perspective in education. Using a model that has underpinnings in the theory of planned behavior (TPB), we predict students' intention to participate in short-term study abroad program. We use TPB to identify behavioral,…

  6. Predicting Factors of Worker Behavior for Proper Working Posture Based on Planed Behavior Theory

    Directory of Open Access Journals (Sweden)

    E Mohammadi Zeydi

    2008-12-01

    Introduction & Objective: Injuries resulting from ignoring proper working posture especially in employees who sitting at workplace for more than of working hours are costly, and create significant pain and discomfort. Decreasing of these injuries is most effectively accomplished through the application of ergonomic design principles. Sometimes, however, barriers (technical and economic preclude ergonomic improvement and, consequently, some organizations rely on the use of proper sitting techniques and maintaining proper working posture as a major control strategy during workday. The problem, however, is that these process performing is inconsistent and managers have a difficult time motivating use of these techniques. The main aim of this study was to understand the factors driving proper working posture among employees. Materials & Methods: This study used the theory of planned behavior to predict upright working posture maintenance among 222 of assembling, machinery and printing line’s employees at a Qazvin Alborz industrial town manufacturing organization. Structural equation modeling, explanatory and confirmatory factor analysis were employed to analyze relationships among constructs. Results: Results revealed that attitude (p< 0.05, β= 0.53 and intention (p< 0.05, β= 0.46 were the strongest predictors of proper working posture maintenance behavior. Perceived behavior control, to a lesser degree, were also important influences on intention (p< 0.05, β= 0.34 and behavior (p< 0.05, β= 0.28. Subjective norms did not surface as effective direct predictors of upright working posture maintenance, but did affect behavior and intent via mediating factors (attitudes subjective norms and perceived behavioral control. Finally, the TPB was supported as an effective model explaining upright working posture maintenance, and had potential application for many other safety-related behaviors. Conclusion: results of this study emphasis on considering factors such as

  7. Forecast for Artificial Muscle Tremor Behavior Based on Dynamic Additional Grey Catastrophe Prediction

    Directory of Open Access Journals (Sweden)

    Yu Fu

    2018-02-01

    Full Text Available Recently, bio-inspired artificial muscles based on ionic polymers have shown a bright perspective in engineering and medical research, but the inherent tremor behavior can cause instability of output response. In this paper, dynamic additional grey catastrophe prediction (DAGCP is proposed to forecast the occurrence time of tremor behavior, providing adequate preparation time for the suppression of the chitosan-based artificial muscles. DAGCP constructs various dimensions of time subsequence models under different starting points based on the threshold of tremor occurrence times and peak-to-peak values in unit time. Next, the appropriate subsequence is selected according to grey correlation degree and prediction accuracy, then it is updated with the newly generated values to achieve a real-time forecast of forthcoming tremor time. Compared with conventional grey catastrophe prediction (GCP, the proposed method has the following advantages: (1 the degradation of prediction accuracy caused by the immobilization of original parameters is prevented; (2 the dynamic input, real-time update and gradual forecast of time sequence are incorporated into the model. The experiment results show that the novel DAGCP can predict forthcoming tremor time earlier and more accurately than the conventional GCP. The generation mechanism of tremor behavior is illustrated as well.

  8. Predicting Sustainable Work Behavior

    DEFF Research Database (Denmark)

    Hald, Kim Sundtoft

    2013-01-01

    Sustainable work behavior is an important issue for operations managers – it has implications for most outcomes of OM. This research explores the antecedents of sustainable work behavior. It revisits and extends the sociotechnical model developed by Brown et al. (2000) on predicting safe behavior...

  9. Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

    Science.gov (United States)

    Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

    2018-03-01

    Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

  10. Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

    Science.gov (United States)

    Borchers, M R; Chang, Y M; Proudfoot, K L; Wadsworth, B A; Stone, A E; Bewley, J M

    2017-07-01

    The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (lying bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine-learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sensitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was

  11. Behavioral factors predicting response to employment-based reinforcement of cocaine abstinence in methadone patients

    OpenAIRE

    Holtyn, August F.; Washington, Wendy Donlin; Knealing, Todd W.; Wong, Conrad J.; Kolodner, Ken; Silverman, Kenneth

    2016-01-01

    We sought to identify behavioral factors associated with response to an employment-based intervention, in which participants had to provide drug-free urine samples to gain access to paid employment. The present secondary analysis included data from a randomized clinical trial. The trial evaluated whether employment-based reinforcement could decrease cocaine use in community methadone patients. Participants (N=56) in the trial worked in a model workplace for 4 hr every weekday and earned about...

  12. A new method to predict the metadynamic recrystallization behavior in a typical nickel-based superalloy

    International Nuclear Information System (INIS)

    Lin, Y.C.; Chen, Xiao-Min; Chen, Ming-Song; Wen, Dong-Xu; Zhou, Ying; He, Dao-Guang

    2016-01-01

    The metadynamic recrystallization (MDRX) behaviors of a typical nickel-based superalloy are investigated by two-pass hot compression tests and four conventional stress-based conventional approaches (offset stress method, back-extrapolation stress method, peak stress method, and mean stress method). It is found that the conventional stress-based methods are not suitable to evaluate the MDRX softening fractions for the studied superalloy. Therefore, a new approach, 'maximum stress method', is proposed to evaluate the MDRX softening fraction. Based on the proposed method, the effects of deformation temperature, strain rate, initial average grain size, and interpass time on MDRX behaviors are discussed in detail. Results show that MDRX softening fraction is sensitive to deformation parameters. The MDRX softening fraction rapidly increases with the increase of deformation temperature, strain rate, and interpass time. The MDRX softening fraction in the coarse-grain material is lower than that in the fine-grain material. Moreover, the observed microstructures indicate that the initial coarse grains can be effectively refined by MDRX. Based on the experimental results, the kinetics equations are established and validated to describe the MDRX behaviors of the studied superalloy. (orig.)

  13. Graph-based representation of behavior in detection and prediction of daily living activities.

    Science.gov (United States)

    Augustyniak, Piotr; Ślusarczyk, Grażyna

    2018-04-01

    Various surveillance systems capture signs of human activities of daily living (ADLs) and store multimodal information as time line behavioral records. In this paper, we present a novel approach to the analysis of a behavioral record used in a surveillance system designed for use in elderly smart homes. The description of a subject's activity is first decomposed into elementary poses - easily detectable by dedicated intelligent sensors - and represented by the share coefficients. Then, the activity is represented in the form of an attributed graph, where nodes correspond to elementary poses. As share coefficients of poses are expressed as attributes assigned to graph nodes, their change corresponding to a subject's action is represented by flow in graph edges. The behavioral record is thus a time series of graphs, which tiny size facilitates storage and management of long-term monitoring results. At the system learning stage, the contribution of elementary poses is accumulated, discretized and probability-ordered leading to a finite list representing the possible transitions between states. Such a list is independently built for each room in the supervised residence, and employed for assessment of the current action in the context of subject's habits and a room purpose. The proposed format of a behavioral record, applied to an adaptive surveillance system, is particularly advantageous for representing new activities not known at the setup stage, for providing a quantitative measure of transitions between poses and for expressing the difference between a predicted and actual action in a numerical way. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  14. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

    Science.gov (United States)

    Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C.

    2017-01-01

    Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages. PMID:28883801

  15. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle.

    Science.gov (United States)

    Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C

    2017-01-01

    Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs) . Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

  16. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

    Directory of Open Access Journals (Sweden)

    Rebeca Cerezo

    2017-08-01

    Full Text Available Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs. Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques.Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples.Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance.Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

  17. Longitudinal connectome-based predictive modeling for REM sleep behavior disorder from structural brain connectivity

    Science.gov (United States)

    Giancardo, Luca; Ellmore, Timothy M.; Suescun, Jessika; Ocasio, Laura; Kamali, Arash; Riascos-Castaneda, Roy; Schiess, Mya C.

    2018-02-01

    Methods to identify neuroplasticity patterns in human brains are of the utmost importance in understanding and potentially treating neurodegenerative diseases. Parkinson disease (PD) research will greatly benefit and advance from the discovery of biomarkers to quantify brain changes in the early stages of the disease, a prodromal period when subjects show no obvious clinical symptoms. Diffusion tensor imaging (DTI) allows for an in-vivo estimation of the structural connectome inside the brain and may serve to quantify the degenerative process before the appearance of clinical symptoms. In this work, we introduce a novel strategy to compute longitudinal structural connectomes in the context of a whole-brain data-driven pipeline. In these initial tests, we show that our predictive models are able to distinguish controls from asymptomatic subjects at high risk of developing PD (REM sleep behavior disorder, RBD) with an area under the receiving operating characteristic curve of 0.90 (pParkinson's Progression Markers Initiative. By analyzing the brain connections most relevant for the predictive ability of the best performing model, we find connections that are biologically relevant to the disease.

  18. Predicting competitive adsorption behavior of major toxic anionic elements onto activated alumina: A speciation-based approach

    International Nuclear Information System (INIS)

    Su Tingzhi; Guan Xiaohong; Tang Yulin; Gu Guowei; Wang Jianmin

    2010-01-01

    Toxic anionic elements such as arsenic, selenium, and vanadium often co-exist in groundwater. These elements may impact each other when adsorption methods are used to remove them. In this study, we investigated the competitive adsorption behavior of As(V), Se(IV), and V(V) onto activated alumina under different pH and surface loading conditions. Results indicated that these anionic elements interfered with each other during adsorption. A speciation-based model was developed to quantify the competitive adsorption behavior of these elements. This model could predict the adsorption data well over the pH range of 1.5-12 for various surface loading conditions, using the same set of adsorption constants obtained from single-sorbate systems. This model has great implications in accurately predicting the field capacity of activated alumina under various local water quality conditions when multiple competitive anionic elements are present.

  19. Predicting Online Purchasing Behavior

    OpenAIRE

    W.R BUCKINX; D. VAN DEN POEL

    2003-01-01

    This empirical study investigates the contribution of different types of predictors to the purchasing behaviour at an online store. We use logit modelling to predict whether or not a purchase is made during the next visit to the website using both forward and backward variable-selection techniques, as well as Furnival and Wilson’s global score search algorithm to find the best subset of predictors. We contribute to the literature by using variables from four different categories in predicting...

  20. Behavioral factors predicting response to employment-based reinforcement of cocaine abstinence in methadone patients.

    Science.gov (United States)

    Holtyn, August F; Washington, Wendy Donlin; Knealing, Todd W; Wong, Conrad J; Kolodner, Ken; Silverman, Kenneth

    2016-06-01

    We sought to identify behavioral factors associated with response to an employment-based intervention, in which participants had to provide drug-free urine samples to gain access to paid employment. The present secondary analysis included data from a randomized clinical trial. The trial evaluated whether employment-based reinforcement could decrease cocaine use in community methadone patients. Participants (N=56) in the trial worked in a model workplace for 4 hr every weekday and earned about $10 per hr. After a 4-week baseline, participants were randomly assigned to an Abstinence & Work (n = 28) or Work Only (n = 28) condition and could work for an additional 26 weeks. Abstinence & Work participants had to provide cocaine-negative urine samples to work and maintain maximum pay. Work Only participants only had to work to earn pay. For Work Only participants, cocaine abstinence during baseline and the intervention period were significantly ( r s = .72, p workplace attendance was marginally correlated ( r s = .32, p = .098) with cocaine abstinence during the intervention period. Furthermore, participants who provided over 60% cocaine-negative urine samples during the intervention period (i.e., responders) had significantly higher baseline rates of opiate abstinence ( p workplace attendance ( p = .042) than non-responders. Employment-based reinforcement of cocaine abstinence may be improved by increasing opiate abstinence and workplace attendance prior to initiating the cocaine-abstinence intervention.

  1. Current Hormonal Contraceptive Use Predicts Female Extra-Pair and Dyadic Sexual Behavior: Evidence Based on Czech National Survey Data

    Directory of Open Access Journals (Sweden)

    Kateřina Klapilová

    2014-01-01

    Full Text Available Data from 1155 Czech women (493 using oral contraception, 662 non-users, obtained from the Czech National Survey of Sexual Behavior, were used to investigate evolutionary-based hypotheses concerning the predictive value of current oral contraceptive (OC use on extra-pair and dyadic (in-pair sexual behavior of coupled women. Specifically, the aim was to determine whether current OC use was associated with lower extra-pair and higher in-pair sexual interest and behavior, because OC use suppresses cyclical shifts in mating psychology that occur in normally cycling women. Zero-inflated Poisson (ZIP regression and negative binomial models were used to test associations between OC use and these sexual measures, controlling for other relevant predictors (e.g., age, parity, in-pair sexual satisfaction, relationship length. The overall incidence of having had an extra-pair partner or one-night stand in the previous year was not related to current OC use (the majority of the sample had not. However, among the women who had engaged in extra-pair sexual behavior, OC users had fewer one-night stands than non-users, and tended to have fewer partners, than non-users. OC users also had more frequent dyadic intercourse than non-users, potentially indicating higher commitment to their current relationship. These results suggest that suppression of fertility through OC use may alter important aspects of female sexual behavior, with potential implications for relationship functioning and stability.

  2. Multiphase, multicomponent phase behavior prediction

    Science.gov (United States)

    Dadmohammadi, Younas

    Accurate prediction of phase behavior of fluid mixtures in the chemical industry is essential for designing and operating a multitude of processes. Reliable generalized predictions of phase equilibrium properties, such as pressure, temperature, and phase compositions offer an attractive alternative to costly and time consuming experimental measurements. The main purpose of this work was to assess the efficacy of recently generalized activity coefficient models based on binary experimental data to (a) predict binary and ternary vapor-liquid equilibrium systems, and (b) characterize liquid-liquid equilibrium systems. These studies were completed using a diverse binary VLE database consisting of 916 binary and 86 ternary systems involving 140 compounds belonging to 31 chemical classes. Specifically the following tasks were undertaken: First, a comprehensive assessment of the two common approaches (gamma-phi (gamma-ϕ) and phi-phi (ϕ-ϕ)) used for determining the phase behavior of vapor-liquid equilibrium systems is presented. Both the representation and predictive capabilities of these two approaches were examined, as delineated form internal and external consistency tests of 916 binary systems. For the purpose, the universal quasi-chemical (UNIQUAC) model and the Peng-Robinson (PR) equation of state (EOS) were used in this assessment. Second, the efficacy of recently developed generalized UNIQUAC and the nonrandom two-liquid (NRTL) for predicting multicomponent VLE systems were investigated. Third, the abilities of recently modified NRTL model (mNRTL2 and mNRTL1) to characterize liquid-liquid equilibria (LLE) phase conditions and attributes, including phase stability, miscibility, and consolute point coordinates, were assessed. The results of this work indicate that the ϕ-ϕ approach represents the binary VLE systems considered within three times the error of the gamma-ϕ approach. A similar trend was observed for the for the generalized model predictions using

  3. Behavioral change theories can inform the prediction of young adults' adoption of a plant-based diet.

    Science.gov (United States)

    Wyker, Brett A; Davison, Kirsten K

    2010-01-01

    Drawing on the Theory of Planned Behavior (TPB) and the Transtheoretical Model (TTM), this study (1) examines links between stages of change for following a plant-based diet (PBD) and consuming more fruits and vegetables (FV); (2) tests an integrated theoretical model predicting intention to follow a PBD; and (3) identifies associated salient beliefs. Cross-sectional. Large public university in the northeastern United States. 204 college students. TPB and TTM constructs were assessed using validated scales. Outcome, normative, and control beliefs were measured using open-ended questions. The overlap between stages of change for FV consumption and adopting a PBD was assessed using Spearman rank correlation analysis and cross-tab comparisons. The proposed model predicting adoption of a PBD was tested using structural equation modeling (SEM). Salient beliefs were coded using automatic response coding software. No association was found between stages of change for FV consumption and following a PBD. Results from SEM analyses provided support for the proposed model predicting intention to follow a PBD. Gender differences in salient beliefs for following a PBD were found. Results demonstrate the potential for effective theory-driven and stage-tailored public health interventions to promote PBDs. Copyright 2010 Society for Nutrition Education. Published by Elsevier Inc. All rights reserved.

  4. Delayed discounting and hedonic hunger in the prediction of lab-based eating behavior.

    Science.gov (United States)

    Ely, Alice V; Howard, Janna; Lowe, Michael R

    2015-12-01

    Research suggests that characteristics identified in obese individuals, such as impulsive decision-making and hedonic hunger, may exist in nonobese populations. This study examined the independent and interactive effects of impulsive decision-making (measured via delay discounting, DD) and hedonic hunger (assessed with the Power of Food Scale, PFS) on food intake. Female participants (N=78) ate a self-determined amount of plain oatmeal, completed self-report measures and the delay discounting task, and participated in a sham taste test of palatable sweet and salty foods. Unexpectedly, PFS and DD scores interacted to predict consumption of the total amount of food consumed, and of oatmeal alone, but not of snack food alone. High-PFS participants consumed more when also high in DD, while low-PFS participants showed the opposite pattern of consumption. The findings identify variables that may increase propensity toward overconsumption and potential weight gain; future research is necessary to evaluate the utility of these constructs to predict increases in BMI over time. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Total variation regularization for fMRI-based prediction of behavior

    Science.gov (United States)

    Michel, Vincent; Gramfort, Alexandre; Varoquaux, Gaël; Eger, Evelyn; Thirion, Bertrand

    2011-01-01

    While medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional MRI (fMRI) data, that provide an indirect measure of task-related or spontaneous neuronal activity, are classically analyzed in a mass-univariate procedure yielding statistical parametric maps. This analysis framework disregards some important principles of brain organization: population coding, distributed and overlapping representations. Multivariate pattern analysis, i.e., the prediction of behavioural variables from brain activation patterns better captures this structure. To cope with the high dimensionality of the data, the learning method has to be regularized. However, the spatial structure of the image is not taken into account in standard regularization methods, so that the extracted features are often hard to interpret. More informative and interpretable results can be obtained with the ℓ1 norm of the image gradient, a.k.a. its Total Variation (TV), as regularization. We apply for the first time this method to fMRI data, and show that TV regularization is well suited to the purpose of brain mapping while being a powerful tool for brain decoding. Moreover, this article presents the first use of TV regularization for classification. PMID:21317080

  6. An instrument based on protection motivation theory to predict Chinese adolescents' intention to engage in protective behaviors against schistosomiasis.

    Science.gov (United States)

    Xiao, Han; Peng, Minjin; Yan, Hong; Gao, Mengting; Li, Jingjing; Yu, Bin; Wu, Hanbo; Li, Shiyue

    2016-01-01

    Further advancement in schistosomiasis prevention requires new tools to assess protective motivation, and promote innovative intervention program. This study aimed to develop and evaluate an instrument developed based on the Protection Motivation Theory (PMT) to predict protective behavior intention against schistosomiasis among adolescents in China. We developed the Schistosomiasis PMT Scale based on two appraisal pathways of protective motivation- threat appraisal pathway and coping appraisal pathway. Data from a large sample of middle school students ( n  = 2238, 51 % male, mean age 13.13 ± 1.10) recruited in Hubei, China was used to evaluated the validity and reliability of the scale. The final scale contains 18 items with seven sub-constructs. Cronbach's Alpha coefficients for the entire instrument was 0.76, and for the seven sub-constructs of severity, vulnerability, intrinsic reward, extrinsic reward, response efficacy, self-efficacy and response cost was 0.56, 0.82, 0.75, 0.80, 0.90, 0.72 and 0.70, respectively. The construct validity analysis revealed that the one level 7 sub-constructs model fitted data well (GFI = 0.98, CFI = 0.98, RMSEA = 0.03, Chi-sq/df = 3.90, p  motivation in schistosomiasis prevention control. Further studies are needed to develop more effective intervention programs for schistosomiasis prevention.

  7. Prediction of Risk Behaviors in HIV-infected Patients Based on Family Functioning: The Mediating Roles of Lifestyle and Risky Decision Making

    Directory of Open Access Journals (Sweden)

    Fariba Ebrahim Babaei

    2017-09-01

    Full Text Available Background and Objective: Risk behaviors are more common in the HIV-positive patients than that in the general population. These behaviors are affected by various factors, such as biological, familial, and social determinants, peer group, media, and lifestyle. Low family functioning is one of the important factors predicting risk behaviors. Regarding this, the present study aimed to investigate the role of family functioning in predicting risk behaviors in the HIV-infected patients based on the mediating roles of risky decision making and lifestyle. Materials and Methods: This descriptive correlational study was conducted on 147 HIV-positive patients selected through convenience sampling technique. The data were collected using the health promoting lifestyle profile-2 (HPLP-2, family adaptability and cohesion scale IV (FACES-IV, balloon analogue risk task (BART, and risk behavior assessment in social situation. The data were analyzed using structural equation modeling method in LISREL 8.8 software. Results: According to the results, there was an indirect relationship between family functioning and risk behaviors. Furthermore, family functioning both directly and indirectly affected the risk behaviors through two mediators of lifestyle and risky decision making. Conclusion: As the findings indicated, family functioning directly contributed to risk behaviors. Moreover, this variable indirectly affected risk behaviors through the mediating roles of risky decision making and lifestyle. Consequently, the future studies should focus more deeply on family functioning role in the risk behaviors of the HIV-infected patients.

  8. Predictability of the future development of aggressive behavior of cranial dural arteriovenous fistulas based on decision tree analysis.

    Science.gov (United States)

    Satomi, Junichiro; Ghaibeh, A Ammar; Moriguchi, Hiroki; Nagahiro, Shinji

    2015-07-01

    The severity of clinical signs and symptoms of cranial dural arteriovenous fistulas (DAVFs) are well correlated with their pattern of venous drainage. Although the presence of cortical venous drainage can be considered a potential predictor of aggressive DAVF behaviors, such as intracranial hemorrhage or progressive neurological deficits due to venous congestion, accurate statistical analyses are currently not available. Using a decision tree data mining method, the authors aimed at clarifying the predictability of the future development of aggressive behaviors of DAVF and at identifying the main causative factors. Of 266 DAVF patients, 89 were eligible for analysis. Under observational management, 51 patients presented with intracranial hemorrhage/infarction during the follow-up period. The authors created a decision tree able to assess the risk for the development of aggressive DAVF behavior. Evaluated by 10-fold cross-validation, the decision tree's accuracy, sensitivity, and specificity were 85.28%, 88.33%, and 80.83%, respectively. The tree shows that the main factor in symptomatic patients was the presence of cortical venous drainage. In its absence, the lesion location determined the risk of a DAVF developing aggressive behavior. Decision tree analysis accurately predicts the future development of aggressive DAVF behavior.

  9. Predicting organic food consumption: A meta-analytic structural equation model based on the theory of planned behavior.

    Science.gov (United States)

    Scalco, Andrea; Noventa, Stefano; Sartori, Riccardo; Ceschi, Andrea

    2017-05-01

    During the last decade, the purchase of organic food within a sustainable consumption context has gained momentum. Consequently, the amount of research in the field has increased, leading in some cases to discrepancies regarding both methods and results. The present review examines those works that applied the theory of planned behavior (TPB; Ajzen, 1991) as a theoretical framework in order to understand and predict consumers' motivation to buy organic food. A meta-analysis has been conducted to assess the strength of the relationships between attitude, subjective norms, perceived behavioral control, and intention, as well as between intention and behavior. Results confirm the major role played by individual attitude in shaping buying intention, followed by subjective norms and perceived behavioral control. Intention-behavior shows a large effect size, few studies however explicitly reported such an association. Furthermore, starting from a pooled correlation matrix, a meta-analytic structural equation model has been applied to jointly evaluate the strength of the relationships among the factors of the original model. Results suggest the robustness of the TPB model. In addition, mediation analysis indicates a potential direct effect from subjective norms to individual attitude in the present context. Finally, some issues regarding methodological aspects of the application of the TPB within the context of organic food are discussed for further research developments. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. The Quality of Maternal Secure-Base Scripts Predicts Children's Secure-Base Behavior at Home in Three Sociocultural Groups

    Science.gov (United States)

    Vaughn, Brian E.; Coppola, Gabrielle; Verissimo, Manuela; Monteiro, Ligia; Santos, Antonio Jose; Posada, German; Carbonell, Olga A.; Plata, Sandra J.; Waters, Harriet S.; Bost, Kelly K.; McBride, Brent; Shin, Nana; Korth, Bryan

    2007-01-01

    The secure-base phenomenon is central to the Bowlby/Ainsworth theory of attachment and is also central to the assessment of attachment across the lifespan. The present study tested whether mothers' knowledge about the secure-base phenomenon, as assessed using a recently designed wordlist prompt measure for eliciting attachment-relevant stories,…

  11. Using meta-analytic path analysis to test theoretical predictions in health behavior: An illustration based on meta-analyses of the theory of planned behavior.

    Science.gov (United States)

    Hagger, Martin S; Chan, Derwin K C; Protogerou, Cleo; Chatzisarantis, Nikos L D

    2016-08-01

    Synthesizing research on social cognitive theories applied to health behavior is an important step in the development of an evidence base of psychological factors as targets for effective behavioral interventions. However, few meta-analyses of research on social cognitive theories in health contexts have conducted simultaneous tests of theoretically-stipulated pattern effects using path analysis. We argue that conducting path analyses of meta-analytic effects among constructs from social cognitive theories is important to test nomological validity, account for mediation effects, and evaluate unique effects of theory constructs independent of past behavior. We illustrate our points by conducting new analyses of two meta-analyses of a popular theory applied to health behaviors, the theory of planned behavior. We conducted meta-analytic path analyses of the theory in two behavioral contexts (alcohol and dietary behaviors) using data from the primary studies included in the original meta-analyses augmented to include intercorrelations among constructs and relations with past behavior missing from the original analysis. Findings supported the nomological validity of the theory and its hypotheses for both behaviors, confirmed important model processes through mediation analysis, demonstrated the attenuating effect of past behavior on theory relations, and provided estimates of the unique effects of theory constructs independent of past behavior. Our analysis illustrates the importance of conducting a simultaneous test of theory-stipulated effects in meta-analyses of social cognitive theories applied to health behavior. We recommend researchers adopt this analytic procedure when synthesizing evidence across primary tests of social cognitive theories in health. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Using meta-analytic path analysis to test theoretical predictions in health behavior: An illustration based on meta-analyses of the theory of planned behavior

    OpenAIRE

    Hagger, Martin; Chan, Dervin K. C.; Protogerou, Cleo; Chatzisarantis, Nikos L. D.

    2016-01-01

    Objective Synthesizing research on social cognitive theories applied to health behavior is an important step in the development of an evidence base of psychological factors as targets for effective behavioral interventions. However, few meta-analyses of research on social cognitive theories in health contexts have conducted simultaneous tests of theoretically-stipulated pattern effects using path analysis. We argue that conducting path analyses of meta-analytic effects among constructs fr...

  13. Predicting Flowering Behavior and Exploring Its Genetic Determinism in an Apple Multi-family Population Based on Statistical Indices and Simplified Phenotyping

    Directory of Open Access Journals (Sweden)

    Jean-Baptiste Durand

    2017-06-01

    Full Text Available Irregular flowering over years is commonly observed in fruit trees. The early prediction of tree behavior is highly desirable in breeding programmes. This study aims at performing such predictions, combining simplified phenotyping and statistics methods. Sequences of vegetative vs. floral annual shoots (AS were observed along axes in trees belonging to five apple related full-sib families. Sequences were analyzed using Markovian and linear mixed models including year and site effects. Indices of flowering irregularity, periodicity and synchronicity were estimated, at tree and axis scales. They were used to predict tree behavior and detect QTL with a Bayesian pedigree-based analysis, using an integrated genetic map containing 6,849 SNPs. The combination of a Biennial Bearing Index (BBI with an autoregressive coefficient (γg efficiently predicted and classified the genotype behaviors, despite few misclassifications. Four QTLs common to BBIs and γg and one for synchronicity were highlighted and revealed the complex genetic architecture of the traits. Irregularity resulted from high AS synchronism, whereas regularity resulted from either asynchronous locally alternating or continual regular AS flowering. A relevant and time-saving method, based on a posteriori sampling of axes and statistical indices is proposed, which is efficient to evaluate the tree breeding values for flowering regularity and could be transferred to other species.

  14. Predicting Flowering Behavior and Exploring Its Genetic Determinism in an Apple Multi-family Population Based on Statistical Indices and Simplified Phenotyping.

    Science.gov (United States)

    Durand, Jean-Baptiste; Allard, Alix; Guitton, Baptiste; van de Weg, Eric; Bink, Marco C A M; Costes, Evelyne

    2017-01-01

    Irregular flowering over years is commonly observed in fruit trees. The early prediction of tree behavior is highly desirable in breeding programmes. This study aims at performing such predictions, combining simplified phenotyping and statistics methods. Sequences of vegetative vs. floral annual shoots (AS) were observed along axes in trees belonging to five apple related full-sib families. Sequences were analyzed using Markovian and linear mixed models including year and site effects. Indices of flowering irregularity, periodicity and synchronicity were estimated, at tree and axis scales. They were used to predict tree behavior and detect QTL with a Bayesian pedigree-based analysis, using an integrated genetic map containing 6,849 SNPs. The combination of a Biennial Bearing Index (BBI) with an autoregressive coefficient (γ g ) efficiently predicted and classified the genotype behaviors, despite few misclassifications. Four QTLs common to BBIs and γ g and one for synchronicity were highlighted and revealed the complex genetic architecture of the traits. Irregularity resulted from high AS synchronism, whereas regularity resulted from either asynchronous locally alternating or continual regular AS flowering. A relevant and time-saving method, based on a posteriori sampling of axes and statistical indices is proposed, which is efficient to evaluate the tree breeding values for flowering regularity and could be transferred to other species.

  15. Predicting healthy and unhealthy behaviors through physical education: A self-determination theory-based longitudinal approach.

    Science.gov (United States)

    Ferriz, R; González-Cutre, D; Sicilia, Á; Hagger, M S

    2016-05-01

    The aim of this study was to evaluate the relations between three dimensions of the structured teaching environment (promotion of theoretical knowledge, physical learning, and health improvement) in physical education (PE) and the adoption of health-related behaviors by students. The study adopted a two-occasion longitudinal design based on self-determination theory (SDT). PE students (N = 654, mean age = 16.13, SD = .77) completed measures of perceived structured teaching environment, satisfaction of basic psychological needs and motivation for PE, and healthy (physical activity, sport participation, and healthy eating) and unhealthy (consumption of tobacco, alcohol, and drugs) behaviors at the beginning and end of the first year of post-compulsory secondary education. Path analysis of the proposed relations among variables supported SDT tenets and showed positive relations between the three dimensions of the structured teaching environment, the satisfaction of basic psychological needs, and autonomous motivation in PE. Autonomous motivation contributed to an explanation of variance in two healthy behaviors, physical activity and sport participation. However, no relation was found among motivation in PE, healthy eating, and consumption of tobacco, alcohol, and drugs. These results show negligible trans-contextual influence of SDT motivational factors in PE on other healthy behaviors beyond physical activity. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  16. Predictors of Traditional Medical Practices in Illness Behavior in Northwestern Ethiopia: An Integrated Model of Behavioral Prediction Based Logistic Regression Analysis

    Directory of Open Access Journals (Sweden)

    Abenezer Yared

    2017-01-01

    Full Text Available This study aimed at investigating traditional medical beliefs and practices in illness behavior as well as predictors of the practices in Gondar city, northwestern Ethiopia, by using the integrated model of behavioral prediction. A cross-sectional quantitative survey was conducted to collect data through interviewer administered structured questionnaires from 496 individuals selected by probability proportional to size sampling technique. Unadjusted bivariate and adjusted multivariate logistic regression analyses were performed, and the results indicated that sociocultural predictors of normative response and attitude as well as psychosocial individual difference variables of traditional understanding of illness causation and perceived efficacy had statistically significant associations with traditional medical practices. Due to the influence of these factors, majority of the study population (85% thus relied on both herbal and spiritual varieties of traditional medicine to respond to their perceived illnesses, supporting the conclusion that characterized the illness behavior of the people as mainly involving traditional medical practices. The results implied two-way medicine needs to be developed with ongoing research, and health educations must take the traditional customs into consideration, for integrating interventions in the health care system in ways that the general public accepts yielding a better health outcome.

  17. Does Religiosity Predict Suicidal Behavior?

    Directory of Open Access Journals (Sweden)

    David Lester

    2017-10-01

    Full Text Available Research was reviewed on whether self-report measures of religiosity were a protective factor against suicidal behaviors. It was found that scores on Francis’s measure of religiosity was negatively associated with non-lethal suicidal behavior (ideation and attempts, a protective effect. Similarly, it was found that intrinsic religiosity (but not extrinsic religiosity was negatively associated with non-lethal suicidal behaviors. However, these associations were weak. Research is needed on the issue whether counselors can use their patients’ religiosity to reduce the risk of dying by suicide.

  18. Efficient and accurate two-scale FE-FFT-based prediction of the effective material behavior of elasto-viscoplastic polycrystals

    Science.gov (United States)

    Kochmann, Julian; Wulfinghoff, Stephan; Ehle, Lisa; Mayer, Joachim; Svendsen, Bob; Reese, Stefanie

    2017-09-01

    Recently, two-scale FE-FFT-based methods (e.g., Spahn et al. in Comput Methods Appl Mech Eng 268:871-883, 2014; Kochmann et al. in Comput Methods Appl Mech Eng 305:89-110, 2016) have been proposed to predict the microscopic and overall mechanical behavior of heterogeneous materials. The purpose of this work is the extension to elasto-viscoplastic polycrystals, efficient and robust Fourier solvers and the prediction of micromechanical fields during macroscopic deformation processes. Assuming scale separation, the macroscopic problem is solved using the finite element method. The solution of the microscopic problem, which is embedded as a periodic unit cell (UC) in each macroscopic integration point, is found by employing fast Fourier transforms, fixed-point and Newton-Krylov methods. The overall material behavior is defined by the mean UC response. In order to ensure spatially converged micromechanical fields as well as feasible overall CPU times, an efficient but simple solution strategy for two-scale simulations is proposed. As an example, the constitutive behavior of 42CrMo4 steel is predicted during macroscopic three-point bending tests.

  19. General predictive model of friction behavior regimes for metal contacts based on the formation stability and evolution of nanocrystalline surface films.

    Energy Technology Data Exchange (ETDEWEB)

    Argibay, Nicolas [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Cheng, Shengfeng [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Sawyer, W. G. [Univ. of Florida, Gainesville, FL (United States); Michael, Joseph R. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Chandross, Michael E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

    2015-09-01

    The prediction of macro-scale friction and wear behavior based on first principles and material properties has remained an elusive but highly desirable target for tribologists and material scientists alike. Stochastic processes (e.g. wear), statistically described parameters (e.g. surface topography) and their evolution tend to defeat attempts to establish practical general correlations between fundamental nanoscale processes and macro-scale behaviors. We present a model based on microstructural stability and evolution for the prediction of metal friction regimes, founded on recently established microstructural deformation mechanisms of nanocrystalline metals, that relies exclusively on material properties and contact stress models. We show through complementary experimental and simulation results that this model overcomes longstanding practical challenges and successfully makes accurate and consistent predictions of friction transitions for a wide range of contact conditions. This framework not only challenges the assumptions of conventional causal relationships between hardness and friction, and between friction and wear, but also suggests a pathway for the design of higher performance metal alloys.

  20. Do attitudes predict consumer's behavior?

    Directory of Open Access Journals (Sweden)

    Đelošević Ivana

    2017-01-01

    Full Text Available There are many themes in marketing to analyze the psychological and marketing aspect of research. The survey of consumer attitudes is one of them. The consumer attitudes have long been discussed and written about. For this purpose, numerous theories, models and researches have emerged. The research of powerful feelings of consumers towards products is something that marketers are constantly trying to achieve. Therefore it is very important for them to understand the factors affecting the attitudes of consumers. Issues related to consumers' attitudes have always been subject matter of the marketers who are trying to keep and maintain the positive and minimize negative attitudes towards the products and services of company. Bearing in the mind that attitudes play a central role in purchase decision, marketers are trying to explore the relation between attitudes and behavior of consumers.

  1. A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions

    Directory of Open Access Journals (Sweden)

    Lan-Rong Dung

    2016-01-01

    Full Text Available A new battery simulator based on a hybrid model is proposed in this paper for dynamic discharging behavior and runtime predictions in existing electronic simulation environments, e.g., PSIM, so it can help power circuit designers to develop and optimize their battery-powered electronic systems. The hybrid battery model combines a diffusion model and a switching overpotential model, which automatically switches overpotential resistance mode or overpotential voltage mode to accurately describe the voltage difference between battery electro-motive force (EMF and terminal voltage. Therefore, this simulator can simply run in an electronic simulation software with less computational efforts and estimate battery performances by further considering nonlinear capacity effects. A linear extrapolation technique is adopted for extracting model parameters from constant current discharging tests, so the EMF hysteresis problem is avoided. For model validation, experiments and simulations in MATLAB and PSIM environments are conducted with six different profiles, including constant loads, an interrupted load, increasing and decreasing loads and a varying load. The results confirm the usefulness and accuracy of the proposed simulator. The behavior and runtime prediction errors can be as low as 3.1% and 1.2%, respectively.

  2. Predicting Academic Achievement from Classroom Behaviors

    OpenAIRE

    Flynt, Cynthia J.

    2008-01-01

    PREDICTING ACADEMIC ACHIEVEMENT FROM CLASSROOM BEHAVIORS by Cynthia J. Flynt Nancy Bodenhorn & Kusum Singh, Co-Chairs Counselor Education (ABSTRACT) This study examined the influence of behaviors exhibited in the classroom on reading and math achievement in the first, third and eighth grades; and the influence of teacher perceptions on reading and math achievement of African-Americans versus White students and male versus female students. Lastly, the study examined te...

  3. Stable prediction of mood and anxiety disorders based on behavioral and emotional problems in childhood: a 14-year follow-up during childhood, adolescence, and young adulthood

    NARCIS (Netherlands)

    S.J. Roza (Sabine); M.B. Hofstra (Marijke); J. van der Ende (Jan); F.C. Verhulst (Frank)

    2003-01-01

    textabstractOBJECTIVE: The goal of this study was to predict the onset of mood and anxiety disorders from parent-reported emotional and behavioral problems in childhood across a 14-year period from childhood into young adulthood. METHOD: In 1983, parent reports of behavioral and

  4. Maternal Characteristics Predicting Young Girls' Disruptive Behavior

    Science.gov (United States)

    van der Molen, Elsa; Hipwell, Alison E.; Vermeiren, Robert; Loeber, Rolf

    2011-01-01

    Little is known about the relative predictive utility of maternal characteristics and parenting skills on the development of girls' disruptive behavior. The current study used five waves of parent- and child-report data from the ongoing Pittsburgh Girls Study to examine these relationships in a sample of 1,942 girls from age 7 to 12 years.…

  5. Factors predicting perioperative delirium and acute exacerbation of behavioral and psychological symptoms of dementia based on admission data in elderly patients with proximal femoral fracture: A retrospective study.

    Science.gov (United States)

    Tanaka, Tomohiro

    2016-07-01

    To examine factors predicting the onset of perioperative delirium and acute exacerbation of behavioral and psychological symptoms of dementia (BPSD), based on patient background, operative background and laboratory data obtained on admission, in elderly patients with proximal femoral fracture. The participants were 152 patients (aged >70 years) who underwent surgery between 1 November 2012 and 31 March 2014. The participants were classified into group B (with onset of perioperative delirium or acute exacerbation of BPSD, n = 52), or group N, (without onset, n = 100), and risk factors were retrospectively examined. Onset was judged based on the presence or absence of common items; that is, "hallucination and delusion," "disturbing speech," "excitatory behavior" and "altered sleep-wake cycle." The participants were observed for 1 week after admission. The incidence of perioperative delirium or acute exacerbation of BPSD was 34.2% in total. In univariate analysis, the incidence was significantly higher (P delirium and acute exacerbation of BPSD. Geriatr Gerontol Int 2016; 16: 821-828. © 2015 Japan Geriatrics Society.

  6. Childhood trajectories of inattention, hyperactivity and oppositional behaviors and prediction of substance abuse/dependence: a 15-year longitudinal population-based study.

    Science.gov (United States)

    Pingault, J-B; Côté, S M; Galéra, C; Genolini, C; Falissard, B; Vitaro, F; Tremblay, R E

    2013-07-01

    Numerous prospective studies have shown that children diagnosed with attention deficit/hyperactivity disorder (ADHD) are at higher risk of long-term substance abuse/dependence. However, there are three important limits to these studies: (a) most did not differentiate the role of hyperactivity and inattention; (b) most did not control for associated behavioral problems; and (c) most did not consider females. Our aim was to clarify the unique and interactive contributions of childhood inattention and hyperactivity symptoms to early adulthood substance abuse/dependence. Behavioral problems of 1803 participants (814 males) in a population-based longitudinal study were assessed yearly between 6 and 12 years by mothers and teachers. The prevalence of substance abuse/dependence at age 21 years was 30.7% for nicotine, 13.4% for alcohol, 9.1% for cannabis and 2.0% for cocaine. The significant predictors of nicotine dependence were inattention (odds ratio (OR): 2.25; 95% confidence interval (CI): 1.63-3.11) and opposition (OR: 1.65; 95%: 1.20-2.28). Only opposition contributed to the prediction of cannabis dependence (OR: 2.33; 95% CI: 1.40-3.87) and cocaine dependence (OR: 2.97; 95% CI: 1.06-8.57). The best behavioral predictor of alcohol abuse/dependence (opposition) was only marginally significant (OR: 1.38; 95% CI: 0.98-1.95). Frequent oppositional behaviors during elementary school were clearly the most pervasive predictors of substance abuse/dependence in early adulthood. The association of childhood ADHD with substance abuse/dependence is largely attributable to its association with opposition problems during childhood. However, inattention remained an important predictor of nicotine dependence, in line with genetic and molecular commonalities between the two phenotypes suggested in the literature.

  7. Thermal Stress Limit Rafting Migration of Seahorses: Prediction Based on Physiological and Behavioral Responses to Thermal Stress

    Science.gov (United States)

    Qin, G.; Li, C.; Lin, Q.

    2017-12-01

    Marine fish species escape from harmful environment by migration. Seahorses, with upright posture and low mobility, could migrate from unfavorable environment by rafting with their prehensile tail. The present study was designed to examine the tolerance of lined seahorse Hippocampus erectus to thermal stress and evaluate the effects of temperature on seahorse migration. The results figured that seahorses' tolerance to thermal stress was time dependent. Acute thermal stress (30°C) increased breathing rate and HSP genes expression significantly, but didn't affect seahorse feeding behavior. Chronic thermal treatment lead to persistent high expression of HSP genes, higher breathing rate, and decreasing feeding, and final higher mortality, suggesting that seahorse cannot adapt to thermal stress by acclimation. No significant negative effects were found in seahorse reproduction in response to chronic thermal stress. Given that seahorses make much slower migration by rafting on sea surface compared to other fishes, we suggest that thermal stress might limit seahorse migration range. and the influence might be magnified by global warming in future.

  8. Changes in automatic threat processing precede and predict clinical changes with exposure-based cognitive-behavior therapy for panic disorder.

    Science.gov (United States)

    Reinecke, Andrea; Waldenmaier, Lara; Cooper, Myra J; Harmer, Catherine J

    2013-06-01

    Cognitive behavioral therapy (CBT) is an effective treatment for emotional disorders such as anxiety or depression, but the mechanisms underlying successful intervention are far from understood. Although it has been a long-held view that psychopharmacological approaches work by directly targeting automatic emotional information processing in the brain, it is usually postulated that psychological treatments affect these processes only over time, through changes in more conscious thought cycles. This study explored the role of early changes in emotional information processing in CBT action. Twenty-eight untreated patients with panic disorder were randomized to a single session of exposure-based CBT or waiting group. Emotional information processing was measured on the day after intervention with an attentional visual probe task, and clinical symptoms were assessed on the day after intervention and at 4-week follow-up. Vigilance for threat information was decreased in the treated group, compared with the waiting group, the day after intervention, before reductions in clinical symptoms. The magnitude of this early effect on threat vigilance predicted therapeutic response after 4 weeks. Cognitive behavioral therapy rapidly affects automatic processing, and these early effects are predictive of later therapeutic change. Such results suggest very fast action on automatic processes mediating threat sensitivity, and they provide an early marker of treatment response. Furthermore, these findings challenge the notion that psychological treatments work directly on conscious thought processes before automatic information processing and imply a greater similarity between early effects of pharmacological and psychological treatments for anxiety than previously thought. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  9. Maternal Characteristics Predicting Young Girls’ Disruptive Behavior

    Science.gov (United States)

    van der Molen, Elsa; Hipwell, Alison E.; Vermeiren, Robert; Loeber, Rolf

    2011-01-01

    Little is known about the relative predictive utility of maternal characteristics and parenting skills on the development of girls’ disruptive behavior. The current study used five waves of parent and child-report data from the ongoing Pittsburgh Girls Study to examine these relationships in a sample of 1,942 girls from age 7 to 12 years. Multivariate Generalized Estimating Equation (GEE) analyses indicated that European American race, mother’s prenatal nicotine use, maternal depression, maternal conduct problems prior to age 15, and low maternal warmth explained unique variance. Maladaptive parenting partly mediated the effects of maternal depression and maternal conduct problems. Both current and early maternal risk factors have an impact on young girls’ disruptive behavior, providing support for the timing and focus of the prevention of girls’ disruptive behavior. PMID:21391016

  10. Trust-based collective view prediction

    CERN Document Server

    Luo, Tiejian; Xu, Guandong; Zhou, Jia

    2013-01-01

    Collective view prediction is to judge the opinions of an active web user based on unknown elements by referring to the collective mind of the whole community. Content-based recommendation and collaborative filtering are two mainstream collective view prediction techniques. They generate predictions by analyzing the text features of the target object or the similarity of users' past behaviors. Still, these techniques are vulnerable to the artificially-injected noise data, because they are not able to judge the reliability and credibility of the information sources. Trust-based Collective View

  11. Gambling and the Reasoned Action Model: Predicting Past Behavior, Intentions, and Future Behavior.

    Science.gov (United States)

    Dahl, Ethan; Tagler, Michael J; Hohman, Zachary P

    2018-03-01

    Gambling is a serious concern for society because it is highly addictive and is associated with a myriad of negative outcomes. The current study applied the Reasoned Action Model (RAM) to understand and predict gambling intentions and behavior. Although prior studies have taken a reasoned action approach to understand gambling, no prior study has fully applied the RAM or used the RAM to predict future gambling. Across two studies the RAM was used to predict intentions to gamble, past gambling behavior, and future gambling behavior. In study 1 the model significantly predicted intentions and past behavior in both a college student and Amazon Mechanical Turk sample. In study 2 the model predicted future gambling behavior, measured 2 weeks after initial measurement of the RAM constructs. This study stands as the first to show the utility of the RAM in predicting future gambling behavior. Across both studies, attitudes and perceived normative pressure were the strongest predictors of intentions to gamble. These findings provide increased understanding of gambling and inform the development of gambling interventions based on the RAM.

  12. Predicting consumer behavior with Web search.

    Science.gov (United States)

    Goel, Sharad; Hofman, Jake M; Lahaie, Sébastien; Pennock, David M; Watts, Duncan J

    2010-10-12

    Recent work has demonstrated that Web search volume can "predict the present," meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.

  13. Does Early Childhood Callous-Unemotional Behavior Uniquely Predict Behavior Problems or Callous-Unemotional Behavior in Late Childhood?

    Science.gov (United States)

    Waller, Rebecca; Dishion, Thomas J.; Shaw, Daniel S.; Gardner, Frances; Wilson, Melvin N.; Hyde, Luke W.

    2016-01-01

    Callous-unemotional (CU) behavior has been linked to behavior problems in children and adolescents. However, few studies have examined whether CU behavior in "early childhood" predicts behavior problems or CU behavior in "late childhood". This study examined whether indicators of CU behavior at ages 2-4 predicted aggression,…

  14. Leveraging Call Center Logs for Customer Behavior Prediction

    Science.gov (United States)

    Parvathy, Anju G.; Vasudevan, Bintu G.; Kumar, Abhishek; Balakrishnan, Rajesh

    Most major businesses use business process outsourcing for performing a process or a part of a process including financial services like mortgage processing, loan origination, finance and accounting and transaction processing. Call centers are used for the purpose of receiving and transmitting a large volume of requests through outbound and inbound calls to customers on behalf of a business. In this paper we deal specifically with the call centers notes from banks. Banks as financial institutions provide loans to non-financial businesses and individuals. Their call centers act as the nuclei of their client service operations and log the transactions between the customer and the bank. This crucial conversation or information can be exploited for predicting a customer’s behavior which will in turn help these businesses to decide on the next action to be taken. Thus the banks save considerable time and effort in tracking delinquent customers to ensure minimum subsequent defaulters. Majority of the time the call center notes are very concise and brief and often the notes are misspelled and use many domain specific acronyms. In this paper we introduce a novel domain specific spelling correction algorithm which corrects the misspelled words in the call center logs to meaningful ones. We also discuss a procedure that builds the behavioral history sequences for the customers by categorizing the logs into one of the predefined behavioral states. We then describe a pattern based predictive algorithm that uses temporal behavioral patterns mined from these sequences to predict the customer’s next behavioral state.

  15. Predicting Persuasion-Induced Behavior Change from the Brain

    Science.gov (United States)

    Falk, Emily B.; Berkman, Elliot T.; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D.

    2011-01-01

    Although persuasive messages often alter people’s self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance. PMID:20573889

  16. Factors Influencing College Women's Contraceptive Behavior: An Application of the Integrative Model of Behavioral Prediction

    Science.gov (United States)

    Sutton, Jazmyne A.; Walsh-Buhi, Eric R.

    2017-01-01

    Objective: This study investigated variables within the Integrative Model of Behavioral Prediction (IMBP) as well as differences across socioeconomic status (SES) levels within the context of inconsistent contraceptive use among college women. Participants: A nonprobability sample of 515 female college students completed an Internet-based survey…

  17. Predicting intentions versus predicting behaviors: domestic violence prevention from a theory of reasoned action perspective.

    Science.gov (United States)

    Nabi, Robin L; Southwell, Brian; Hornik, Robert

    2002-01-01

    A central assumption of many models of human behavior is that intention to perform a behavior is highly predictive of actual behavior. This article presents evidence that belies this notion. Based on a survey of 1,250 Philadelphia adults, a clear and consistent pattern emerged suggesting that beliefs related to domestic violence correlate with intentions to act with respect to domestic violence but rarely correlate with reported actions (e.g., talking to the abused woman). Numerous methodological and substantive explanations for this finding are offered with emphasis placed on the complexity of the context in which an action to prevent a domestic violence incident occurs. We conclude by arguing that despite the small, insignificant relationships between beliefs and behaviors found, worthwhile aggregate effects on behavior might still exist, thus reaffirming the role of communication campaign efforts.

  18. Development of a molecular dynamic based cohesive zone model for prediction of an equivalent material behavior for Al/Al2O3 composite

    Energy Technology Data Exchange (ETDEWEB)

    Sazgar, A. [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Movahhedy, M.R., E-mail: movahhed@sharif.edu [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Mahnama, M. [School of Mechanical Engineering, University of Tehran, Tehran (Iran, Islamic Republic of); Sohrabpour, S. [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of)

    2017-01-02

    The interfacial behavior of composites is often simulated using a cohesive zone model (CZM). In this approach, a traction-separation (T-S) relation between the matrix and reinforcement particles, which is often obtained from experimental results, is employed. However, since the determination of this relation from experimental results is difficult, the molecular dynamics (MD) simulation may be used as a virtual environment to obtain this relation. In this study, MD simulations under the normal and shear loadings are used to obtain the interface behavior of Al/Al2O3 composite material and to derive the T-S relation. For better agreement with Al/Al2O3 interfacial behavior, the exponential form of the T-S relation suggested by Needleman [1] is modified to account for thermal effects. The MD results are employed to develop a parameterized cohesive zone model which is implemented in a finite element model of the matrix-particle interactions. Stress-strain curves obtained from simulations under different loading conditions and volume fractions show a close correlation with experimental results. Finally, by studying the effects of strain rate and volume fraction of particles in Al(6061-T6)/Al2O3 composite, an equivalent homogeneous model is introduced which can predict the overall behavior of the composite.

  19. Behavioral based safety approaches

    International Nuclear Information System (INIS)

    Maria Michael Raj, I.

    2009-01-01

    Approach towards the establishment of positive safety culture at Heavy Water Plant, Tuticorin includes the adoption of several important methodologies focused on human behavior and culminates with achievement of Total Safety Culture where Quality and Productivity are integrated with Safety

  20. Using the Integrative Model of Behavioral Prediction to Understand College Students' STI Testing Beliefs, Intentions, and Behaviors.

    Science.gov (United States)

    Wombacher, Kevin; Dai, Minhao; Matig, Jacob J; Harrington, Nancy Grant

    2018-03-22

    To identify salient behavioral determinants related to STI testing among college students by testing a model based on the integrative model of behavioral (IMBP) prediction. 265 undergraduate students from a large university in the Southeastern US. Formative and survey research to test an IMBP-based model that explores the relationships between determinants and STI testing intention and behavior. Results of path analyses supported a model in which attitudinal beliefs predicted intention and intention predicted behavior. Normative beliefs and behavioral control beliefs were not significant in the model; however, select individual normative and control beliefs were significantly correlated with intention and behavior. Attitudinal beliefs are the strongest predictor of STI testing intention and behavior. Future efforts to increase STI testing rates should identify and target salient attitudinal beliefs.

  1. Meal and snack-time eating disorder cognitions predict eating disorder behaviors and vice versa in a treatment seeking sample: A mobile technology based ecological momentary assessment study.

    Science.gov (United States)

    Levinson, Cheri A; Sala, Margarita; Fewell, Laura; Brosof, Leigh C; Fournier, Lauren; Lenze, Eric J

    2018-06-01

    Individuals with eating disorders experience high anxiety when eating, which may contribute to the high relapse rates seen in the eating disorders. However, it is unknown if specific cognitions associated with such anxiety (e.g., fears of gaining weight) may lead to engagement in eating disorder behaviors (e.g., weighing oneself). Participants (N = 66) recently treated at a residential eating disorder facility and diagnosed with an eating disorder (primarily anorexia nervosa; n = 40; 60.6%) utilized a mobile application to answer questions about mealtime cognitions, anxiety, and eating disorder behaviors four times a day for one week. Hierarchical linear models using cross-lag analyses identified that there were quasi-causal (and sometimes reciprocal) within-person relationships between specific eating disorder cognitions and subsequent eating disorder behaviors. These cognitions predicted higher anxiety during the next meal and eating disorder pathology at one-month follow-up. Interventions personalized to target these specific cognitions in real time might reduce eating disorder relapse. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. Predicting persuasion-induced behavior change from the brain.

    Science.gov (United States)

    Falk, Emily B; Berkman, Elliot T; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D

    2010-06-23

    Although persuasive messages often alter people's self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance.

  3. The utility of theory of planned behavior in predicting consistent ...

    African Journals Online (AJOL)

    admin

    disease. Objective: To examine the utility of theory of planned behavior in predicting consistent condom use intention of HIV .... (24-25), making subjective norms as better predictors of intention ..... Organizational Behavior and Human Decision.

  4. Comparison of Baseline Characteristics between Community-based and Hospital-based Suicidal Ideators and Its Implications for Tailoring Strategies for Suicide Prevention: Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior.

    Science.gov (United States)

    Park, C Hyung Keun; Lee, Jae Won; Lee, Sang Yeol; Moon, Jungjoon; Shim, Se Hoon; Paik, Jong Woo; Kim, Shin Gyeom; Cho, Seong Jin; Kim, Min Hyuk; Kim, Seokho; Park, Jae Hyun; You, Sungeun; Jeon, Hong Jin; Ahn, Yong Min

    2017-09-01

    In this cross-sectional study, we aimed to identify distinguishing factors between populations with suicidal ideation recruited from hospitals and communities to make an efficient allocation of limited anti-suicidal resources according to group differences. We analyzed the baseline data from 120 individuals in a community-based cohort (CC) and 137 individuals in a hospital-based cohort (HC) with suicidal ideation obtained from the Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS) study. First, their sociodemographic factors, histories of medical and psychiatric illnesses, and suicidal behaviors were compared. Second, diagnosis by the Korean version of the Mini International Neuropsychiatric Interview, scores of psychometric scales were used to assess differences in clinical severity between the groups. The results revealed that the HC had more severe clinical features: more psychiatric diagnosis including current and recurrent major depressive episodes (odds ratio [OR], 4.054; P suicide risk (OR, 4.817; P suicidality. © 2017 The Korean Academy of Medical Sciences.

  5. Statistical Models for Predicting Threat Detection From Human Behavior

    Science.gov (United States)

    Kelley, Timothy; Amon, Mary J.; Bertenthal, Bennett I.

    2018-01-01

    Users must regularly distinguish between secure and insecure cyber platforms in order to preserve their privacy and safety. Mouse tracking is an accessible, high-resolution measure that can be leveraged to understand the dynamics of perception, categorization, and decision-making in threat detection. Researchers have begun to utilize measures like mouse tracking in cyber security research, including in the study of risky online behavior. However, it remains an empirical question to what extent real-time information about user behavior is predictive of user outcomes and demonstrates added value compared to traditional self-report questionnaires. Participants navigated through six simulated websites, which resembled either secure “non-spoof” or insecure “spoof” versions of popular websites. Websites also varied in terms of authentication level (i.e., extended validation, standard validation, or partial encryption). Spoof websites had modified Uniform Resource Locator (URL) and authentication level. Participants chose to “login” to or “back” out of each website based on perceived website security. Mouse tracking information was recorded throughout the task, along with task performance. After completing the website identification task, participants completed a questionnaire assessing their security knowledge and degree of familiarity with the websites simulated during the experiment. Despite being primed to the possibility of website phishing attacks, participants generally showed a bias for logging in to websites versus backing out of potentially dangerous sites. Along these lines, participant ability to identify spoof websites was around the level of chance. Hierarchical Bayesian logistic models were used to compare the accuracy of two-factor (i.e., website security and encryption level), survey-based (i.e., security knowledge and website familiarity), and real-time measures (i.e., mouse tracking) in predicting risky online behavior during phishing

  6. Statistical Models for Predicting Threat Detection From Human Behavior

    Directory of Open Access Journals (Sweden)

    Timothy Kelley

    2018-04-01

    Full Text Available Users must regularly distinguish between secure and insecure cyber platforms in order to preserve their privacy and safety. Mouse tracking is an accessible, high-resolution measure that can be leveraged to understand the dynamics of perception, categorization, and decision-making in threat detection. Researchers have begun to utilize measures like mouse tracking in cyber security research, including in the study of risky online behavior. However, it remains an empirical question to what extent real-time information about user behavior is predictive of user outcomes and demonstrates added value compared to traditional self-report questionnaires. Participants navigated through six simulated websites, which resembled either secure “non-spoof” or insecure “spoof” versions of popular websites. Websites also varied in terms of authentication level (i.e., extended validation, standard validation, or partial encryption. Spoof websites had modified Uniform Resource Locator (URL and authentication level. Participants chose to “login” to or “back” out of each website based on perceived website security. Mouse tracking information was recorded throughout the task, along with task performance. After completing the website identification task, participants completed a questionnaire assessing their security knowledge and degree of familiarity with the websites simulated during the experiment. Despite being primed to the possibility of website phishing attacks, participants generally showed a bias for logging in to websites versus backing out of potentially dangerous sites. Along these lines, participant ability to identify spoof websites was around the level of chance. Hierarchical Bayesian logistic models were used to compare the accuracy of two-factor (i.e., website security and encryption level, survey-based (i.e., security knowledge and website familiarity, and real-time measures (i.e., mouse tracking in predicting risky online behavior

  7. Statistical Models for Predicting Threat Detection From Human Behavior.

    Science.gov (United States)

    Kelley, Timothy; Amon, Mary J; Bertenthal, Bennett I

    2018-01-01

    Users must regularly distinguish between secure and insecure cyber platforms in order to preserve their privacy and safety. Mouse tracking is an accessible, high-resolution measure that can be leveraged to understand the dynamics of perception, categorization, and decision-making in threat detection. Researchers have begun to utilize measures like mouse tracking in cyber security research, including in the study of risky online behavior. However, it remains an empirical question to what extent real-time information about user behavior is predictive of user outcomes and demonstrates added value compared to traditional self-report questionnaires. Participants navigated through six simulated websites, which resembled either secure "non-spoof" or insecure "spoof" versions of popular websites. Websites also varied in terms of authentication level (i.e., extended validation, standard validation, or partial encryption). Spoof websites had modified Uniform Resource Locator (URL) and authentication level. Participants chose to "login" to or "back" out of each website based on perceived website security. Mouse tracking information was recorded throughout the task, along with task performance. After completing the website identification task, participants completed a questionnaire assessing their security knowledge and degree of familiarity with the websites simulated during the experiment. Despite being primed to the possibility of website phishing attacks, participants generally showed a bias for logging in to websites versus backing out of potentially dangerous sites. Along these lines, participant ability to identify spoof websites was around the level of chance. Hierarchical Bayesian logistic models were used to compare the accuracy of two-factor (i.e., website security and encryption level), survey-based (i.e., security knowledge and website familiarity), and real-time measures (i.e., mouse tracking) in predicting risky online behavior during phishing attacks

  8. Prediction based on mean subset

    DEFF Research Database (Denmark)

    Øjelund, Henrik; Brown, P. J.; Madsen, Henrik

    2002-01-01

    , it is found that the proposed mean subset method has superior prediction performance than prediction based on the best subset method, and in some settings also better than the ridge regression and lasso methods. The conclusions drawn from the Monte Carlo study is corroborated in an example in which prediction......Shrinkage methods have traditionally been applied in prediction problems. In this article we develop a shrinkage method (mean subset) that forms an average of regression coefficients from individual subsets of the explanatory variables. A Bayesian approach is taken to derive an expression of how...... the coefficient vectors from each subset should be weighted. It is not computationally feasible to calculate the mean subset coefficient vector for larger problems, and thus we suggest an algorithm to find an approximation to the mean subset coefficient vector. In a comprehensive Monte Carlo simulation study...

  9. HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling

    Directory of Open Access Journals (Sweden)

    S. J. Schymanski

    2011-02-01

    Full Text Available Most hydrological models are valid at most only in a few places and cannot be reasonably transferred to other places or to far distant time periods. Transfer in space is difficult because the models are conditioned on past observations at particular places to define parameter values and unobservable processes that are needed to fully characterize the structure and functioning of the landscape. Transfer in time has to deal with the likely temporal changes to both parameters and processes under future changed conditions. This remains an important obstacle to addressing some of the most urgent prediction questions in hydrology, such as prediction in ungauged basins and prediction under global change. In this paper, we propose a new approach to catchment hydrological modeling, based on universal principles that do not change in time and that remain valid across many places. The key to this framework, which we call behavioral modeling, is to assume that there are universal and time-invariant organizing principles that can be used to identify the most appropriate model structure (including parameter values and responses for a given ecosystem at a given moment in time. These organizing principles may be derived from fundamental physical or biological laws, or from empirical laws that have been demonstrated to be time-invariant and to hold at many places and scales. Much fundamental research remains to be undertaken to help discover these organizing principles on the basis of exploration of observed patterns of landscape structure and hydrological behavior and their interpretation as legacy effects of past co-evolution of climate, soils, topography, vegetation and humans. Our hope is that the new behavioral modeling framework will be a step forward towards a new vision for hydrology where models are capable of more confidently predicting the behavior of catchments beyond what has been observed or experienced before.

  10. Body odor quality predicts behavioral attractiveness in humans.

    Science.gov (United States)

    Roberts, S Craig; Kralevich, Alexandra; Ferdenzi, Camille; Saxton, Tamsin K; Jones, Benedict C; DeBruine, Lisa M; Little, Anthony C; Havlicek, Jan

    2011-12-01

    Growing effort is being made to understand how different attractive physical traits co-vary within individuals, partly because this might indicate an underlying index of genetic quality. In humans, attention has focused on potential markers of quality such as facial attractiveness, axillary odor quality, the second-to-fourth digit (2D:4D) ratio and body mass index (BMI). Here we extend this approach to include visually-assessed kinesic cues (nonverbal behavior linked to movement) which are statistically independent of structural physical traits. The utility of such kinesic cues in mate assessment is controversial, particularly during everyday conversational contexts, as they could be unreliable and susceptible to deception. However, we show here that the attractiveness of nonverbal behavior, in 20 male participants, is predicted by perceived quality of their axillary body odor. This finding indicates covariation between two desirable traits in different sensory modalities. Depending on two different rating contexts (either a simple attractiveness rating or a rating for long-term partners by 10 female raters not using hormonal contraception), we also found significant relationships between perceived attractiveness of nonverbal behavior and BMI, and between axillary odor ratings and 2D:4D ratio. Axillary odor pleasantness was the single attribute that consistently predicted attractiveness of nonverbal behavior. Our results demonstrate that nonverbal kinesic cues could reliably reveal mate quality, at least in males, and could corroborate and contribute to mate assessment based on other physical traits.

  11. Motivation and Treatment Credibility Predicts Dropout, Treatment Adherence, and Clinical Outcomes in an Internet-Based Cognitive Behavioral Relaxation Program: A Randomized Controlled Trial.

    Science.gov (United States)

    Alfonsson, Sven; Olsson, Erik; Hursti, Timo

    2016-03-08

    In previous research, variables such as age, education, treatment credibility, and therapeutic alliance have shown to affect patients' treatment adherence and outcome in Internet-based psychotherapy. A more detailed understanding of how such variables are associated with different measures of adherence and clinical outcomes may help in designing more effective online therapy. The aims of this study were to investigate demographical, psychological, and treatment-specific variables that could predict dropout, treatment adherence, and treatment outcomes in a study of online relaxation for mild to moderate stress symptoms. Participant dropout and attrition as well as data from self-report instruments completed before, during, and after the online relaxation program were analyzed. Multiple linear and logistical regression analyses were conducted to predict early dropout, overall attrition, online treatment progress, number of registered relaxation exercises, posttreatment symptom levels, and reliable improvement. Dropout was significantly predicted by treatment credibility, whereas overall attrition was associated with reporting a focus on immediate consequences and experiencing a low level of intrinsic motivation for the treatment. Treatment progress was predicted by education level and treatment credibility, whereas number of registered relaxation exercises was associated with experiencing intrinsic motivation for the treatment. Posttreatment stress symptoms were positively predicted by feeling external pressure to participate in the treatment and negatively predicted by treatment credibility. Reporting reliable symptom improvement after treatment was predicted by treatment credibility and therapeutic bond. This study confirmed that treatment credibility and a good working alliance are factors associated with successful Internet-based psychotherapy. Further, the study showed that measuring adherence in different ways provides somewhat different results, which

  12. Hierarchy and predictability in spontaneous behavior

    Science.gov (United States)

    Berman, Gordon; Bialek, William; Shaevitz, Joshua

    2015-03-01

    Animals perform a complex array of behaviors, from changes in body posture to vocalizations to other dynamic outputs. Far from being a disordered collection of actions, however, there is thought to be an intrinsic structure to the set of behaviors and their temporal organization. This structure has often been hypothesized to be hierarchical, with certain behaviors grouped together into modules that interact with other modules at time scales that are long with respect to that of an individual behavior. There have been few measurements, however, showing that a particular animal's behavioral repertoire is organized hierarchically. This has largely resulted from an inability to measure the entirety of an animal's behavioral repertoire or even to definite precisely what a ``behavior'' is. In this talk, I will apply our novel method for mapping the behavioral space of animals to videos of freely-behaving fruit flies (D. melanogaster), showing that the organisms' behavioral repertoire consists of a hierarchically-organized set of stereotyped behaviors. This hierarchical patterning results in the emergence of long time scales of memory in the system, providing insight into the mechanisms of behavioral control and patterning.

  13. Trait Impressions as Heuristics for Predicting Future Behavior.

    Science.gov (United States)

    Newman, Leonard S.

    1996-01-01

    The dispositionist bias manifests itself when behavior is overattributed to dispositions, and when contextual factors are underused when predicting behavior. Psychological processes underlying the former bias have been most thoroughly examined. Three studies support the hypothesis that trait implications of past behavior function as heuristics…

  14. Prediction of fretting fatigue behavior under elastic-plastic conditions

    International Nuclear Information System (INIS)

    Shin, Ki Su

    2009-01-01

    Fretting fatigue generally leads to the degradation of the fatigue strength of a material due to cyclic micro-slip between two contacting materials. Fretting fatigue is regarded as an important issue in designing aerospace structures. While many studies have evaluated fretting fatigue behavior under elastic deformation conditions, few have focused on fretting fatigue behavior under elastic-plastic deformation conditions, especially the crack orientation and fatigue life prediction for Ti-6Al-4V. The primary goal of this study was to characterize the fretting fatigue crack initiation behavior in the presence of plasticity. Experimental tests were performed using pad configurations involving elastic-plastic deformations. To calculate stress distributions under elastic-plastic fretting fatigue conditions, FEA was also performed. Several parametric approaches were used to predict fretting fatigue life along with stress distribution resulting from FEA. However, those parameters using surface stresses were unable to establish an equivalence between elastic fretting fatigue data and elastic-plastic fretting fatigue data. Based on this observation, the critical distance methods, which are commonly used in notch analysis, were applied to the fretting fatigue problem. In conclusion, the effective strain range method when used in conjunction with the SMSSR parameter showed a good correlation of data points between the pad configurations involving elastic and elastic plastic deformations

  15. Behavior based safety

    International Nuclear Information System (INIS)

    Sudhikumaran, T.V.; Mehta, S.C.; Goyal, D.K.

    2009-01-01

    Behaviour Based Safety (popularly known as BBS) is a new methodology for achieving injury free work place and total Safety Culture. BBS is successfully being implemented and is being practiced as a work methodology for achieving a loss and injury free work environment and work practice. Through BBS, it was brought out that the root causes of all Industrial accidents some how originate from the 'at risk' behaviour of some individual or group of individuals at some level. The policy of NPCIL is to excel in the field of Industrial and Fire Safety in comparison to international standards. This article indents to bring out the various parameters helping in installing BBS programme at any plant. (author)

  16. A novel modeling to predict the critical current behavior of Nb$_{3}$Sn PIT strand under transverse load based on a scaling law and Finite Element Analysis

    CERN Document Server

    Wang, Tiening; Takayasu, Makoto; Bordini, Bernardo

    2014-01-01

    Superconducting Nb$_{3}$Sn Powder-In-Tube (PIT) strands could be used for the superconducting magnets of the next generation Large Hadron Collider. The strands are cabled into the typical flat Rutherford cable configuration. During the assembly of a magnet and its operation the strands experience not only longitudinal but also transverse load due to the pre-compression applied during the assembly and the Lorentz load felt when the magnets are energized. To properly design the magnets and guarantee their safe operation, mechanical load effects on the strand superconducting properties are studied extensively; particularly, many scaling laws based on tensile load experiments have been established to predict the critical current dependence on strain. However, the dependence of the superconducting properties on transverse load has not been extensively studied so far. One of the reasons is that transverse loading experiments are difficult to conduct due to the small diameter of the strand (about 1 mm) and the data ...

  17. When Attitudes Don't Predict Behavior

    DEFF Research Database (Denmark)

    Bhattacherjee, Anol; Sanford, Clive Carlton

    2006-01-01

    This study introduces the concept of "attitude strength" to explain why some IT users' attitudes are not strongly related to their usage behaviors. We review the attitude strength literature, employ the elaboration likelihood model to theorize personal relevance and related expertise as two salient...... dimensions of attitude strength in the IT usage context, and postulate a research model to capture the moderating effects of these constructs on the attitude-behavior relationship. The hypothesized effects are empirically tested using a longitudinal survey of document management system usage among staff...

  18. Individual laboratory-measured discount rates predict field behavior.

    Science.gov (United States)

    Chabris, Christopher F; Laibson, David; Morris, Carrie L; Schuldt, Jonathon P; Taubinsky, Dmitry

    2008-12-01

    We estimate discount rates of 555 subjects using a laboratory task and find that these individual discount rates predict inter-individual variation in field behaviors (e.g., exercise, BMI, smoking). The correlation between the discount rate and each field behavior is small: none exceeds 0.28 and many are near 0. However, the discount rate has at least as much predictive power as any variable in our dataset (e.g., sex, age, education). The correlation between the discount rate and field behavior rises when field behaviors are aggregated: these correlations range from 0.09-0.38. We present a model that explains why specific intertemporal choice behaviors are only weakly correlated with discount rates, even though discount rates robustly predict aggregates of intertemporal decisions.

  19. Comparing three attitude-behavior theories for predicting science teachers' intentions

    Science.gov (United States)

    Zint, Michaela

    2002-11-01

    Social psychologists' attitude-behavior theories can contribute to understanding science teachers' behaviors. Such understanding can, in turn, be used to improve professional development. This article describes leading attitude-behavior theories and summarizes results from past tests of these theories. A study predicting science teachers' intention to incorporate environmental risk education based on these theories is also reported. Data for that study were collected through a mail questionnaire (n = 1336, radjusted = 80%) and analyzed using confirmatory factor and multiple regression analysis. All determinants of intention to act in the Theory of Reasoned Action and Theory of Planned Behavior and some determinants in the Theory of Trying predicted science teachers' environmental risk education intentions. Given the consistency of results across studies, the Theory of Planned Behavior augmented with past behavior is concluded to provide the best attitude-behavior model for predicting science teachers' intention to act. Thus, science teachers' attitude toward the behavior, perceived behavioral control, and subjective norm need to be enhanced to modify their behavior. Based on the Theory of Trying, improving their attitude toward the process and toward success, and expectations of success may also result in changes. Future research should focus on identifying determinants that can further enhance the ability of these theories to predict and explain science teachers' behaviors.

  20. Understanding Eating Behaviors through Parental Communication and the Integrative Model of Behavioral Prediction.

    Science.gov (United States)

    Scheinfeld, Emily; Shim, Minsun

    2017-05-01

    Emerging adulthood (EA) is an important yet overlooked period for developing long-term health behaviors. During these years, emerging adults adopt health behaviors that persist throughout life. This study applies the Integrative Model of Behavioral Prediction (IMBP) to examine the role of childhood parental communication in predicting engagement in healthful eating during EA. Participants included 239 college students, ages 18 to 25, from a large university in the southern United States. Participants were recruited and data collection occurred spring 2012. Participants responded to measures to assess perceived parental communication, eating behaviors, attitudes, subjective norms, and behavioral control over healthful eating. SEM and mediation analyses were used to address the hypotheses posited. Data demonstrated that perceived parent-child communication - specifically, its quality and target-specific content - significantly predicted emerging adults' eating behaviors, mediated through subjective norm and perceived behavioral control. This study sets the stage for further exploration and understanding of different ways parental communication influences emerging adults' healthy behavior enactment.

  1. A computational model that predicts behavioral sensitivity to intracortical microstimulation

    Science.gov (United States)

    Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J.

    2017-02-01

    Objective. Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. Approach. We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Main results. Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R 2 = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber’s law. Significance. The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.

  2. Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior

    Directory of Open Access Journals (Sweden)

    Khalil Muhammad Zuhaib

    2017-09-01

    Full Text Available Navigating a robot in a dynamic environment is a challenging task, especially when the behavior of other agents such as pedestrians, is only partially predictable. Also, the kinodynamic constraints on robot motion add an extra challenge. This paper proposes a novel navigational strategy for collision avoidance of a kinodynamically constrained robot from multiple moving passive agents with partially predictable behavior. Specifically, this paper presents a new approach to identify the set of control inputs to the robot, named control obstacle, which leads it towards a collision with a passive agent moving along an arbitrary path. The proposed method is developed by generalizing the concept of nonlinear velocity obstacle (NLVO, which is used to avoid collision with a passive agent, and takes into account the kinodynamic constraints on robot motion. Further, it formulates the navigational problem as an optimization problem, which allows the robot to make a safe decision in the presence of various sources of unmodelled uncertainties. Finally, the performance of the algorithm is evaluated for different parameters and is compared to existing velocity obstacle-based approaches. The simulated experiments show the excellent performance of the proposed approach in term of computation time and success rate.

  3. Predicting Overt and Covert Antisocial Behaviors: Parents, Peers, and Homelessness

    Science.gov (United States)

    Tompsett, Carolyn J.; Toro, Paul A.

    2010-01-01

    Parental deviance, parental monitoring, and deviant peers were examined as predictors of overt and covert antisocial behaviors. Homeless (N=231) and housed (N=143) adolescents were assessed in adolescence and again in early adulthood. Homelessness predicted both types of antisocial behaviors, and effects persisted in young adulthood. Parental…

  4. Predicting effective contraceptive behavior in college females.

    Science.gov (United States)

    Hughes, C B; Torre, C

    1987-09-01

    This article reports the results of a preliminary research project that explored the relationship between assertiveness, cognitive development and contraceptive behavior among single young women in their freshman and senior years at college. A total of 60 college women at a university health center volunteered to participate in this pilot study. They filled out three instruments: the Galassi College Self-Expression Scale (SES), the Measure of Intellectual Development (MID) tool and an author-developed sexuality questionnaire. Although there was a significant relationship between cognitive development and assertiveness, no significant relationships were found between cognitive development, assertiveness and use of effective contraception. Interesting descriptive characteristics were identified. Clinical implications are discussed.

  5. Winning a competition predicts dishonest behavior.

    Science.gov (United States)

    Schurr, Amos; Ritov, Ilana

    2016-02-16

    Winning a competition engenders subsequent unrelated unethical behavior. Five studies reveal that after a competition has taken place winners behave more dishonestly than competition losers. Studies 1 and 2 demonstrate that winning a competition increases the likelihood of winners to steal money from their counterparts in a subsequent unrelated task. Studies 3a and 3b demonstrate that the effect holds only when winning means performing better than others (i.e., determined in reference to others) but not when success is determined by chance or in reference to a personal goal. Finally, study 4 demonstrates that a possible mechanism underlying the effect is an enhanced sense of entitlement among competition winners.

  6. Predictive Validity of a Student Self-Report Screener of Behavioral and Emotional Risk in an Urban High School

    Science.gov (United States)

    Dowdy, Erin; Harrell-Williams, Leigh; Dever, Bridget V.; Furlong, Michael J.; Moore, Stephanie; Raines, Tara; Kamphaus, Randy W.

    2016-01-01

    Increasingly, schools are implementing school-based screening for risk of behavioral and emotional problems; hence, foundational evidence supporting the predictive validity of screening instruments is important to assess. This study examined the predictive validity of the Behavior Assessment System for Children-2 Behavioral and Emotional Screening…

  7. Feature Selection, Flaring Size and Time-to-Flare Prediction Using Support Vector Regression, and Automated Prediction of Flaring Behavior Based on Spatio-Temporal Measures Using Hidden Markov Models

    Science.gov (United States)

    Al-Ghraibah, Amani

    Solar flares release stored magnetic energy in the form of radiation and can have significant detrimental effects on earth including damage to technological infrastructure. Recent work has considered methods to predict future flare activity on the basis of quantitative measures of the solar magnetic field. Accurate advanced warning of solar flare occurrence is an area of increasing concern and much research is ongoing in this area. Our previous work 111] utilized standard pattern recognition and classification techniques to determine (classify) whether a region is expected to flare within a predictive time window, using a Relevance Vector Machine (RVM) classification method. We extracted 38 features which describing the complexity of the photospheric magnetic field, the result classification metrics will provide the baseline against which we compare our new work. We find a true positive rate (TPR) of 0.8, true negative rate (TNR) of 0.7, and true skill score (TSS) of 0.49. This dissertation proposes three basic topics; the first topic is an extension to our previous work [111, where we consider a feature selection method to determine an appropriate feature subset with cross validation classification based on a histogram analysis of selected features. Classification using the top five features resulting from this analysis yield better classification accuracies across a large unbalanced dataset. In particular, the feature subsets provide better discrimination of the many regions that flare where we find a TPR of 0.85, a TNR of 0.65 sightly lower than our previous work, and a TSS of 0.5 which has an improvement comparing with our previous work. In the second topic, we study the prediction of solar flare size and time-to-flare using support vector regression (SVR). When we consider flaring regions only, we find an average error in estimating flare size of approximately half a GOES class. When we additionally consider non-flaring regions, we find an increased average

  8. Relationships between self-downing beliefs and math performance in Greek adolescent students: A predictive study based on Rational-Emotive Behavior Education (REBE) as theoretical perspective

    OpenAIRE

    Katsiki, Alexandra; Minnaert, Alexander; Katsikis, Demitris

    2017-01-01

    There is ample evidence from both educational practice and research that math performance is often associated with increased levels of test anxiety, stress and discomfort and that students’ cognitions account for this performance. Rational-Emotive Behavior Education (REBE), derived from Ellis’s Rational-EmotiveBehavior Theory (REBT), supports that it is mainly students’ cognitions in the form of core (ir) rational beliefs that determine their performance and overallschool achievement. However...

  9. Predicting sun protection behaviors using protection motivation variables.

    Science.gov (United States)

    Ch'ng, Joanne W M; Glendon, A Ian

    2014-04-01

    Protection motivation theory components were used to predict sun protection behaviors (SPBs) using four outcome measures: typical reported behaviors, previous reported behaviors, current sunscreen use as determined by interview, and current observed behaviors (clothing worn) to control for common method bias. Sampled from two SE Queensland public beaches during summer, 199 participants aged 18-29 years completed a questionnaire measuring perceived severity, perceived vulnerability, response efficacy, response costs, and protection motivation (PM). Personal perceived risk (similar to threat appraisal) and response likelihood (similar to coping appraisal) were derived from their respective PM components. Protection motivation predicted all four SPB criterion variables. Personal perceived risk and response likelihood predicted protection motivation. Protection motivation completely mediated the effect of response likelihood on all four criterion variables. Alternative models are considered. Strengths and limitations of the study are outlined and suggestions made for future research.

  10. Robustness of the Theory of Planned Behavior in predicting entrepreneurial intentions and actions

    NARCIS (Netherlands)

    Kautonen, T.; van Gelderen, M.W.; Fink, M.

    2015-01-01

    This analysis demonstrates the relevance and robustness of the theory of planned behavior in the prediction of business start-up intentions and subsequent behavior based on longitudinal survey data (2011 and 2012; n=969) from the adult population in Austria and Finland. By doing so, the study

  11. Predicting behavior during interracial interactions: a stress and coping approach.

    Science.gov (United States)

    Trawalter, Sophie; Richeson, Jennifer A; Shelton, J Nicole

    2009-11-01

    The social psychological literature maintains unequivocally that interracial contact is stressful. Yet research and theory have rarely considered how stress may shape behavior during interracial interactions. To address this empirical and theoretical gap, the authors propose a framework for understanding and predicting behavior during interracial interactions rooted in the stress and coping literature. Specifically, they propose that individuals often appraise interracial interactions as a threat, experience stress, and therefore cope-they antagonize, avoid, freeze, or engage. In other words, the behavioral dynamics of interracial interactions can be understood as initial stress reactions and subsequent coping responses. After articulating the framework and its predictions for behavior during interracial interactions, the authors examine its ability to organize the extant literature on behavioral dynamics during interracial compared with same-race contact. They conclude with a discussion of the implications of the stress and coping framework for improving research and fostering more positive interracial contact.

  12. Agent-based modeling of sustainable behaviors

    CERN Document Server

    Sánchez-Maroño, Noelia; Fontenla-Romero, Oscar; Polhill, J; Craig, Tony; Bajo, Javier; Corchado, Juan

    2017-01-01

    Using the O.D.D. (Overview, Design concepts, Detail) protocol, this title explores the role of agent-based modeling in predicting the feasibility of various approaches to sustainability. The chapters incorporated in this volume consist of real case studies to illustrate the utility of agent-based modeling and complexity theory in discovering a path to more efficient and sustainable lifestyles. The topics covered within include: households' attitudes toward recycling, designing decision trees for representing sustainable behaviors, negotiation-based parking allocation, auction-based traffic signal control, and others. This selection of papers will be of interest to social scientists who wish to learn more about agent-based modeling as well as experts in the field of agent-based modeling.

  13. Data-Based Predictive Control with Multirate Prediction Step

    Science.gov (United States)

    Barlow, Jonathan S.

    2010-01-01

    Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

  14. Anaplasia in pilocytic astrocytoma predicts aggressive behavior.

    Science.gov (United States)

    Rodriguez, Fausto J; Scheithauer, Bernd W; Burger, Peter C; Jenkins, Sarah; Giannini, Caterina

    2010-02-01

    The clinical significance of anaplastic features, a rare event in pilocytic astrocytoma (PA), is not fully established. We reviewed 34 PA with anaplastic features (Male = 21, Female = 13; median age 35 y, 5 to 75) among approximately 2200 PA cases (1.7%). Tumors were included which demonstrated brisk mitotic activity [at least 4 mitoses/10 high power fields (400 x )], in addition to hypercellularity and moderate-to-severe cytologic atypia, with or without necrosis. The tumors either had a PA precursor, coexistent (n = 14) (41%) or documented by previous biopsy (n = 10) (29%), or exhibited typical pilocytic features in an otherwise anaplastic astrocytoma (n = 10) (29%). Clinical features of neurofibromatosis type-1 were present in 24% and a history of radiation for PA precursor in 12%. Histologically, the anaplastic component was classified as pilocytic like (41%), small cell (32%), epithelioid (15%), or fibrillary (12%). Median MIB1 labeling index was 24.7% in the anaplastic component and 2.6% in the precursor, although overlapping values were present. Strong p53 staining (3+) was limited to areas with anaplasia (19%), with overlapping values for 1 and 2+ in areas without anaplasia. Median overall and progression-free survivals after diagnosis for the entire study group were 24 and 14 months, respectively. Overall and progression-free survivals were shorter in the setting of prior radiation for a PA precursor (P = 0.007, 0.028), increasing mitotic activity (P = 0.03, 0.02), and presence of necrosis (P = 0.02, 0.02), after adjusting for age and site. The biologic behavior of PAs with high-mitotic rates and those with necrosis paralleled that of St Anne-Mayo grades 2 and 3 diffuse astrocytomas, respectively. In summary, PA with anaplastic features exhibits a spectrum of morphologies and is associated with decreased survival when compared with typical PA.

  15. Predicting Risk-Mitigating Behaviors From Indecisiveness and Trait Anxiety

    DEFF Research Database (Denmark)

    Mcneill, Ilona M.; Dunlop, Patrick D.; Skinner, Timothy C.

    2016-01-01

    Past research suggests that indecisiveness and trait anxiety may both decrease the likelihood of performing risk-mitigating preparatory behaviors (e.g., preparing for natural hazards) and suggests two cognitive processes (perceived control and worrying) as potential mediators. However, no single...... control over wildfire-related outcomes. Trait anxiety did not uniquely predict preparedness or perceived control, but it did uniquely predict worry, with higher trait anxiety predicting more worrying. Also, worry trended toward uniquely predicting preparedness, albeit in an unpredicted positive direction...

  16. Relationships between self-downing beliefs and math performance in Greek adolescent students : A predictive study based on Rational-Emotive Behavior Education (REBE) as theoretical perspective

    NARCIS (Netherlands)

    Katsiki, Alexandra; Minnaert, Alexander; Katsikis, Demitris

    2017-01-01

    There is ample evidence from both educational practice and research that math performance is often associated with increased levels of test anxiety, stress and discomfort and that students’ cognitions account for this performance. Rational-Emotive Behavior Education (REBE), derived from Ellis’s

  17. Behavior-based evacuation planning

    KAUST Repository

    Rodriguez, Samuel

    2010-05-01

    In this work, we present a formulation of an evacuation planning problem that is inspired by motion planning and describe an integrated behavioral agent-based and roadmap-based motion planning approach to solve it. Our formulation allows users to test the effect on evacuation of a number of different environmental factors. One of our main focuses is to provide a mechanism to investigate how the interaction between agents influences the resulting evacuation plans. Specifically, we explore how various types of control provided by a set of directing agents effects the overall evacuation planning strategies of the evacuating agents. ©2010 IEEE.

  18. Behavior-based evacuation planning

    KAUST Repository

    Rodriguez, Samuel; Amato, Nancy M

    2010-01-01

    In this work, we present a formulation of an evacuation planning problem that is inspired by motion planning and describe an integrated behavioral agent-based and roadmap-based motion planning approach to solve it. Our formulation allows users to test the effect on evacuation of a number of different environmental factors. One of our main focuses is to provide a mechanism to investigate how the interaction between agents influences the resulting evacuation plans. Specifically, we explore how various types of control provided by a set of directing agents effects the overall evacuation planning strategies of the evacuating agents. ©2010 IEEE.

  19. Learning Behavior Models for Interpreting and Predicting Traffic Situations

    OpenAIRE

    Gindele, Tobias

    2014-01-01

    In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees.

  20. Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior

    OpenAIRE

    Kooti, Farshad; Lerman, Kristina; Aiello, Luca Maria; Grbovic, Mihajlo; Djuric, Nemanja; Radosavljevic, Vladan

    2015-01-01

    Consumer spending accounts for a large fraction of the US economic activity. Increasingly, consumer activity is moving to the web, where digital traces of shopping and purchases provide valuable data about consumer behavior. We analyze these data extracted from emails and combine them with demographic information to characterize, model, and predict consumer behavior. Breaking down purchasing by age and gender, we find that the amount of money spent on online purchases grows sharply with age, ...

  1. Predicting malicious behavior tools and techniques for ensuring global security

    CERN Document Server

    Jackson, Gary M

    2012-01-01

    A groundbreaking exploration of how to identify and fight security threats at every level This revolutionary book combines real-world security scenarios with actual tools to predict and prevent incidents of terrorism, network hacking, individual criminal behavior, and more. Written by an expert with intelligence officer experience who invented the technology, it explores the keys to understanding the dark side of human nature, various types of security threats (current and potential), and how to construct a methodology to predict and combat malicious behavior. The companion CD demonstrates ava

  2. Predicting personality traits related to consumer behavior using SNS analysis

    Science.gov (United States)

    Baik, Jongbum; Lee, Kangbok; Lee, Soowon; Kim, Yongbum; Choi, Jayoung

    2016-07-01

    Modeling a user profile is one of the important factors for devising a personalized recommendation. The traditional approach for modeling a user profile in computer science is to collect and generalize the user's buying behavior or preference history, generated from the user's interactions with recommender systems. According to consumer behavior research, however, internal factors such as personality traits influence a consumer's buying behavior. Existing studies have tried to adapt the Big 5 personality traits to personalized recommendations. However, although studies have shown that these traits can be useful to some extent for personalized recommendation, the causal relationship between the Big 5 personality traits and the buying behaviors of actual consumers has not been validated. In this paper, we propose a novel method for predicting the four personality traits-Extroversion, Public Self-consciousness, Desire for Uniqueness, and Self-esteem-that correlate with buying behaviors. The proposed method automatically constructs a user-personality-traits prediction model for each user by analyzing the user behavior on a social networking service. The experimental results from an analysis of the collected Facebook data show that the proposed method can predict user-personality traits with greater precision than methods that use the variables proposed in previous studies.

  3. Neurophysiology in preschool improves behavioral prediction of reading ability throughout primary school.

    Science.gov (United States)

    Maurer, Urs; Bucher, Kerstin; Brem, Silvia; Benz, Rosmarie; Kranz, Felicitas; Schulz, Enrico; van der Mark, Sanne; Steinhausen, Hans-Christoph; Brandeis, Daniel

    2009-08-15

    More struggling readers could profit from additional help at the beginning of reading acquisition if dyslexia prediction were more successful. Currently, prediction is based only on behavioral assessment of early phonological processing deficits associated with dyslexia, but it might be improved by adding brain-based measures. In a 5-year longitudinal study of children with (n = 21) and without (n = 23) familial risk for dyslexia, we tested whether neurophysiological measures of automatic phoneme and tone deviance processing obtained in kindergarten would improve prediction of reading over behavioral measures alone. Together, neurophysiological and behavioral measures obtained in kindergarten significantly predicted reading in school. Particularly the late mismatch negativity measure that indicated hemispheric lateralization of automatic phoneme processing improved prediction of reading ability over behavioral measures. It was also the only significant predictor for long-term reading success in fifth grade. Importantly, this result also held for the subgroup of children at familial risk. The results demonstrate that brain-based measures of processing deficits associated with dyslexia improve prediction of reading and thus may be further evaluated to complement clinical practice of dyslexia prediction, especially in targeted populations, such as children with a familial risk.

  4. Applying a health action model to predict and improve healthy behaviors in coal miners.

    Science.gov (United States)

    Vahedian-Shahroodi, Mohammad; Tehrani, Hadi; Mohammadi, Faeze; Gholian-Aval, Mahdi; Peyman, Nooshin

    2018-05-01

    One of the most important ways to prevent work-related diseases in occupations such as mining is to promote healthy behaviors among miners. This study aimed to predict and promote healthy behaviors among coal miners by using a health action model (HAM). The study was conducted on 200 coal miners in Iran in two steps. In the first step, a descriptive study was implemented to determine predictive constructs and effectiveness of HAM on behavioral intention. The second step involved a quasi-experimental study to determine the effect of an HAM-based education intervention. This intervention was implemented by the researcher and the head of the safety unit based on the predictive construct specified in the first step over 12 sessions of 60 min. The data was collected using an HAM questionnaire and a checklist of healthy behavior. The results of the first step of the study showed that attitude, belief, and normative constructs were meaningful predictors of behavioral intention. Also, the results of the second step revealed that the mean score of attitude and behavioral intention increased significantly after conducting the intervention in the experimental group, while the mean score of these constructs decreased significantly in the control group. The findings of this study showed that HAM-based educational intervention could improve the healthy behaviors of mine workers. Therefore, it is recommended to extend the application of this model to other working groups to improve healthy behaviors.

  5. Fast prediction of the fatigue behavior of short-fiber-reinforced thermoplastics based on heat build-up measurements: application to heterogeneous cases

    Science.gov (United States)

    Serrano, Leonell; Marco, Yann; Le Saux, Vincent; Robert, Gilles; Charrier, Pierre

    2017-09-01

    Short-fiber-reinforced thermoplastics components for structural applications are usually very complex parts as stiffeners, ribs and thickness variations are used to compensate the quite low material intrinsic stiffness. These complex geometries induce complex local mechanical fields but also complex microstructures due to the injection process. Accounting for these two aspects is crucial for the design in regard to fatigue of these parts, especially for automotive industry. The aim of this paper is to challenge an energetic approach, defined to evaluate quickly the fatigue lifetime, on three different heterogeneous cases: a classic dog-bone sample with a skin-core microstructure and two structural samples representative of the thickness variations observed for industrial components. First, a method to evaluate dissipated energy fields from thermal measurements is described and is applied to the three samples in order to relate the cyclic loading amplitude to the fields of cyclic dissipated energy. Then, a local analysis is detailed in order to link the energy dissipated at the failure location to the fatigue lifetime and to predict the fatigue curve from the thermomechanical response of one single sample. The predictions obtained for the three cases are compared successfully to the Wöhler curves obtained with classic fatigue tests. Finally, a discussion is proposed to compare results for the three samples in terms of dissipation fields and fatigue lifetime. This comparison illustrates that, if the approach is leading to a very relevant diagnosis on each case, the dissipated energy field is not giving a straightforward access to the lifetime cartography as the relation between fatigue failure and dissipated energy seems to be dependent on the local mechanical and microstructural state.

  6. Predictive Models of Procedural Human Supervisory Control Behavior

    Science.gov (United States)

    2011-01-01

    821708, Brest , France. Page 139 of 150 Boussemart, Y. and M. L. Cummings (2010). "Predicting Supervisory Control Behavior with Hidden Markov Models...Strategies for Strike Planning. COGIS 2006 - Cognitive Systems with Interactive Sensors, Paris . Burges, C. (1998). "A Tutorial on Support Vector Machines

  7. Mining social media: tracking content and predicting behavior

    NARCIS (Netherlands)

    Tsagkias, M.

    2012-01-01

    The advent of social media has established a symbiotic relationship between social media and online news. This relationship can be leveraged for tracking news content, and predicting behavior with tangible real-world applications, e.g., online reputation management, ad pricing, news ranking, and

  8. Personality assessment and behavioral prediction at first impression

    NARCIS (Netherlands)

    Vartanian, O.; Stewart, K.; Mandel, D.R.; Pavlovic, N.; McLellan, L.; Taylor, Paul J

    2012-01-01

    Research has demonstrated high levels of consensus and self-other agreement for extraversion and conscientiousness. However, the mechanisms whereby these assessments contribute to accuracy in behavioral predictions remain unclear. In this study, two judges rated targets on Big Five personality

  9. Prediction of Swelling Behavior of Addis Ababa Expansive Soil ...

    African Journals Online (AJOL)

    In this study a simple hyperbolic mathematical model is used to predict the swelling behavior of an expansive soil from Addis Ababa. The main parameters that are needed to run the model are the applied pressure and initial dry density. The other parameters of the model including the initial slope of the swell-time curve, the ...

  10. Predicting stay/leave behavior among volleyball referees

    NARCIS (Netherlands)

    Van Yperen, N.W.

    1998-01-01

    This study aimed to predict stay/leave behavior among volleyball referees. The predictor variables reflect commitment aspects from the literature: attraction, perceived lack of alternatives, personal investments, and feelings of obligation to remain. Intent to quit was assumed to mediate the link

  11. Predicting Liaison: an Example-Based Approach

    NARCIS (Netherlands)

    Greefhorst, A.P.M.; Bosch, A.P.J. van den

    2016-01-01

    Predicting liaison in French is a non-trivial problem to model. We compare a memory-based machine-learning algorithm with a rule-based baseline. The memory-based learner is trained to predict whether liaison occurs between two words on the basis of lexical, orthographic, morphosyntactic, and

  12. The Autism Parent Screen for Infants: Predicting risk of autism spectrum disorder based on parent-reported behavior observed at 6-24 months of age.

    Science.gov (United States)

    Sacrey, Lori-Ann R; Bryson, Susan; Zwaigenbaum, Lonnie; Brian, Jessica; Smith, Isabel M; Roberts, Wendy; Szatmari, Peter; Vaillancourt, Tracy; Roncadin, Caroline; Garon, Nancy

    2018-04-01

    This study examined whether a novel parent-report questionnaire, the Autism Parent Screen for Infants, could differentiate infants subsequently diagnosed with autism spectrum disorder from a high-risk cohort (siblings of children diagnosed with autism spectrum disorder (n = 66)) from high-risk and low-risk comparison infants (no family history of autism spectrum disorder) who did not develop autism spectrum disorder (n = 138 and 79, respectively). Participants were assessed prospectively at 6, 9, 12, 15, 18, and 24 months of age. At 36 months, a blind independent diagnostic assessment for autism spectrum disorder was completed. Parent report on the Autism Parent Screen for Infants was examined in relation to diagnostic outcome and risk status (i.e. high-risk sibling with autism spectrum disorder, high-risk sibling without autism spectrum disorder, and low-risk control). The results indicated that from 6 months of age, total score on the Autism Parent Screen for Infants differentiated between the siblings with autism spectrum disorder and the other two groups. The sensitivity, specificity, and positive and negative predictive validity of the Autism Parent Screen for Infants highlight its potential for the early screening of autism spectrum disorder in high-risk cohorts.

  13. Predicting risk behaviors: development and validation of a diagnostic scale.

    Science.gov (United States)

    Witte, K; Cameron, K A; McKeon, J K; Berkowitz, J M

    1996-01-01

    The goal of this study was to develop and validate the Risk Behavior Diagnosis (RBD) Scale for use by health care providers and practitioners interested in promoting healthy behaviors. Theoretically guided by the Extended Parallel Process Model (EPPM; a fear appeal theory), the RBD scale was designed to work in conjunction with an easy-to-use formula to determine which types of health risk messages would be most appropriate for a given individual or audience. Because some health risk messages promote behavior change and others backfire, this type of scale offers guidance to practitioners on how to develop the best persuasive message possible to motivate healthy behaviors. The results of the study demonstrate the RBD scale to have a high degree of content, construct, and predictive validity. Specific examples and practical suggestions are offered to facilitate use of the scale for health practitioners.

  14. 基于网络拓扑结构视角的社交媒体用户转发预测算法%Individual retweet behavior prediction algorithm in social media based on network topology analyses

    Institute of Scientific and Technical Information of China (English)

    方冰; 缪文渊

    2016-01-01

    To investigate who will repost tweets,based on the literatures about whether a tweet would be reposted or not,this paper proposed a logical regression algorithm through analyzing social network topological structure,user behavior and social in-fluences between users,and it tested by real data set.The experiment results demonstrate that compared with alternative algo-rithm which does not consider social network topological structure,the novel proposed prediction algorithm performes much bet-ter.This work lays an important foundation for information propagation path prediction.%为预测某条微博的具体转发者,在微博是否会被转发的研究基础上,提出了基于社交网络拓扑结构、用户行为及用户间关联三个层面的逻辑回归分类算法,并针对该算法进行真实数据集检测。实验结果表明,该预测算法与未考虑网络拓扑结构的算法相比性能显著提升,为实现社交媒体信息传播轨迹精准预测打下了重要基础。

  15. Predicting adolescent's cyberbullying behavior: A longitudinal risk analysis.

    Science.gov (United States)

    Barlett, Christopher P

    2015-06-01

    The current study used the risk factor approach to test the unique and combined influence of several possible risk factors for cyberbullying attitudes and behavior using a four-wave longitudinal design with an adolescent US sample. Participants (N = 96; average age = 15.50 years) completed measures of cyberbullying attitudes, perceptions of anonymity, cyberbullying behavior, and demographics four times throughout the academic school year. Several logistic regression equations were used to test the contribution of these possible risk factors. Results showed that (a) cyberbullying attitudes and previous cyberbullying behavior were important unique risk factors for later cyberbullying behavior, (b) anonymity and previous cyberbullying behavior were valid risk factors for later cyberbullying attitudes, and (c) the likelihood of engaging in later cyberbullying behavior increased with the addition of risk factors. Overall, results show the unique and combined influence of such risk factors for predicting later cyberbullying behavior. Results are discussed in terms of theory. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  16. Behavioral Profile Predicts Dominance Status in Mountain Chickadees.

    Science.gov (United States)

    Fox, Rebecca A; Ladage, Lara D; Roth, Timothy C; Pravosudov, Vladimir V

    2009-06-01

    Individual variation in stable behavioral traits may explain variation in ecologically-relevant behaviors such as foraging, dispersal, anti-predator behavior, and dominance. We investigated behavioral variation in mountain chickadees (Poecile gambeli), a North American parid that lives in dominance-structured winter flocks, using two common measures of behavioral profile: exploration of a novel room and novel object exploration. We related those behavioral traits to dominance status in male chickadees following brief, pair-wise encounters. Low-exploring birds (birds that visited less than four locations in the novel room) were significantly more likely to become dominant in brief, pairwise encounters with high-exploring birds (i.e., birds that visited all perching locations within a novel room). On the other hand, there was no relationship between novel object exploration and dominance. Interestingly, novel room exploration was also not correlated with novel object exploration. These results suggest that behavioral profile may predict the social status of group-living individuals. Moreover, our results contradict the idea that novel object exploration and novel room exploration are always interchangeable measures of individuals' sensitivity to environmental novelty.

  17. Prediction of projectile ricochet behavior after water impact.

    Science.gov (United States)

    Baillargeon, Yves; Bergeron, Guy

    2012-11-01

    Although not very common, forensic investigation related to projectile ricochet on water can be required when undesirable collateral damage occurs. Predicting the ricochet behavior of a projectile is challenging owing to numerous parameters involved: impact velocity, incident angle, projectile stability, angular velocity, etc. Ricochet characteristics of different projectiles (K50 BMG, 0.5-cal Ball M2, 0.5-cal AP-T C44, 7.62-mm Ball C21, and 5.56-mm Ball C77) were studied in a pool. The results are presented to assess projectile velocity after ricochet, ricochet angle, and projectile azimuth angle based on impact velocity or incident angle for each projectile type. The azimuth ranges show the highest variability at low postricochet velocity. The critical ricochet angles were ranging from 15 to 30°. The average ricochet angles for all projectiles were pretty close for all projectiles at 2.5 and 10° incident angles for the range of velocities studied. © 2012 Her Majesty the Queen in Right of Canada 2012. Reproduced with the permission of the Minister of the Department of National Defence.

  18. Predicting the behavior of microfluidic circuits made from discrete elements

    Science.gov (United States)

    Bhargava, Krisna C.; Thompson, Bryant; Iqbal, Danish; Malmstadt, Noah

    2015-10-01

    Microfluidic devices can be used to execute a variety of continuous flow analytical and synthetic chemistry protocols with a great degree of precision. The growing availability of additive manufacturing has enabled the design of microfluidic devices with new functionality and complexity. However, these devices are prone to larger manufacturing variation than is typical of those made with micromachining or soft lithography. In this report, we demonstrate a design-for-manufacturing workflow that addresses performance variation at the microfluidic element and circuit level, in context of mass-manufacturing and additive manufacturing. Our approach relies on discrete microfluidic elements that are characterized by their terminal hydraulic resistance and associated tolerance. Network analysis is employed to construct simple analytical design rules for model microfluidic circuits. Monte Carlo analysis is employed at both the individual element and circuit level to establish expected performance metrics for several specific circuit configurations. A protocol based on osmometry is used to experimentally probe mixing behavior in circuits in order to validate these approaches. The overall workflow is applied to two application circuits with immediate use at on the bench-top: series and parallel mixing circuits that are modularly programmable, virtually predictable, highly precise, and operable by hand.

  19. Predicting the behavior of microfluidic circuits made from discrete elements.

    Science.gov (United States)

    Bhargava, Krisna C; Thompson, Bryant; Iqbal, Danish; Malmstadt, Noah

    2015-10-30

    Microfluidic devices can be used to execute a variety of continuous flow analytical and synthetic chemistry protocols with a great degree of precision. The growing availability of additive manufacturing has enabled the design of microfluidic devices with new functionality and complexity. However, these devices are prone to larger manufacturing variation than is typical of those made with micromachining or soft lithography. In this report, we demonstrate a design-for-manufacturing workflow that addresses performance variation at the microfluidic element and circuit level, in context of mass-manufacturing and additive manufacturing. Our approach relies on discrete microfluidic elements that are characterized by their terminal hydraulic resistance and associated tolerance. Network analysis is employed to construct simple analytical design rules for model microfluidic circuits. Monte Carlo analysis is employed at both the individual element and circuit level to establish expected performance metrics for several specific circuit configurations. A protocol based on osmometry is used to experimentally probe mixing behavior in circuits in order to validate these approaches. The overall workflow is applied to two application circuits with immediate use at on the bench-top: series and parallel mixing circuits that are modularly programmable, virtually predictable, highly precise, and operable by hand.

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

    Science.gov (United States)

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

    2016-11-01

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

  1. Automated Clinical Assessment from Smart home-based Behavior Data

    Science.gov (United States)

    Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

    2016-01-01

    Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behaviour in the home and predicting standard clinical assessment scores of the residents. To accomplish this goal, we propose a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident’s daily behavior and predict the corresponding standard clinical assessment scores. CAAB uses statistical features that describe characteristics of a resident’s daily activity performance to train machine learning algorithms that predict the clinical assessment scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years using prediction and classification-based experiments. In the prediction-based experiments, we obtain a statistically significant correlation (r = 0.72) between CAAB-predicted and clinician-provided cognitive assessment scores and a statistically significant correlation (r = 0.45) between CAAB-predicted and clinician-provided mobility scores. Similarly, for the classification-based experiments, we find CAAB has a classification accuracy of 72% while classifying cognitive assessment scores and 76% while classifying mobility scores. These prediction and classification results suggest that it is feasible to predict standard clinical scores using smart home sensor data and learning-based data analysis. PMID:26292348

  2. Genetic vulnerability interacts with parenting and early care education to predict increasing externalizing behavior.

    Science.gov (United States)

    Lipscomb, Shannon T; Laurent, Heidemarie; Neiderhiser, Jenae M; Shaw, Daniel S; Natsuaki, Misaki N; Reiss, David; Leve, Leslie D

    2014-01-01

    The current study examined interactions among genetic influences and children's early environments on the development of externalizing behaviors from 18 months to 6 years of age. Participants included 233 families linked through adoption (birth parents and adoptive families). Genetic influences were assessed by birth parent temperamental regulation. Early environments included both family (overreactive parenting) and out-of-home factors (center-based Early Care and Education; ECE). Overreactive parenting predicted more child externalizing behaviors. Attending center-based ECE was associated with increasing externalizing behaviors only for children with genetic liability for dysregulation. Additionally, children who were at risk for externalizing behaviors due to both genetic variability and exposure to center-based ECE were more sensitive to the effects of overreactive parenting on externalizing behavior than other children.

  3. Speaker Prediction based on Head Orientations

    NARCIS (Netherlands)

    Rienks, R.J.; Poppe, Ronald Walter; van Otterlo, M.; Poel, Mannes; Poel, M.; Nijholt, A.; Nijholt, Antinus

    2005-01-01

    To gain insight into gaze behavior in meetings, this paper compares the results from a Naive Bayes classifier, Neural Networks and humans on speaker prediction in four-person meetings given solely the azimuth head angles. The Naive Bayes classifier scored 69.4% correctly, Neural Networks 62.3% and

  4. Global Brain Dynamics During Social Exclusion Predict Subsequent Behavioral Conformity

    OpenAIRE

    Wasylyshyn, Nick; Hemenway, Brett; Garcia, Javier O.; Cascio, Christopher N.; O'Donnell, Matthew Brook; Bingham, C. Raymond; Simons-Morton, Bruce; Vettel, Jean M.; Falk, Emily B.

    2017-01-01

    Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (N = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI...

  5. Born Knowing: Tentacled Snakes Innately Predict Future Prey Behavior

    Science.gov (United States)

    Catania, Kenneth C.

    2010-01-01

    Background Aquatic tentacled snakes (Erpeton tentaculatus) can take advantage of their prey's escape response by startling fish with their body before striking. The feint usually startles fish toward the snake's approaching jaws. But when fish are oriented at a right angle to the jaws, the C-start escape response translates fish parallel to the snake's head. To exploit this latter response, snakes must predict the future location of the fish. Adult snakes can make this prediction. Is it learned, or are tentacled snakes born able to predict future fish behavior? Methods and Findings Laboratory-born, naïve snakes were investigated as they struck at fish. Trials were recorded at 250 or 500 frames per second. To prevent learning, snakes were placed in a water container with a clear transparency sheet or glass bottom. The chamber was placed over a channel in a separate aquarium with fish below. Thus snakes could see and strike at fish, without contact. The snake's body feint elicited C-starts in the fish below the transparency sheet, allowing strike accuracy to be quantified in relationship to the C-starts. When fish were oriented at a right angle to the jaws, naïve snakes biased their strikes to the future location of the escaping fish's head, such that the snake's jaws and the fish's translating head usually converged. Several different types of predictive strikes were observed. Conclusions The results show that some predators have adapted their nervous systems to directly compensate for the future behavior of prey in a sensory realm that usually requires learning. Instead of behavior selected during their lifetime, newborn tentacled snakes exhibit behavior that has been selected on a different scale—over many generations. Counter adaptations in fish are not expected, as tentacled snakes are rare predators exploiting fish responses that are usually adaptive. PMID:20585384

  6. Evidence-Based Behavioral Interventions for Repetitive Behaviors in Autism

    Science.gov (United States)

    Boyd, Brian A.; McDonough, Stephen G.; Bodfish, James W.

    2012-01-01

    Restricted and repetitive behaviors (RRBs) are a core symptom of autism spectrum disorders (ASD). There has been an increased research emphasis on repetitive behaviors; however, this research primarily has focused on phenomenology and mechanisms. Thus, the knowledge base on interventions is lagging behind other areas of research. The literature…

  7. Changes in Pilot Behavior with Predictive System Status Information

    Science.gov (United States)

    Trujillo, Anna C.

    1998-01-01

    Research has shown a strong pilot preference for predictive information of aircraft system status in the flight deck. However, changes in pilot behavior associated with using this predictive information have not been ascertained. The study described here quantified these changes using three types of predictive information (none, whether a parameter was changing abnormally, and the time for a parameter to reach an alert range) and three initial time intervals until a parameter alert range was reached (ITIs) (1 minute, 5 minutes, and 15 minutes). With predictive information, subjects accomplished most of their tasks before an alert occurred. Subjects organized the time they did their tasks by locus-of-control with no predictive information and for the 1-minute ITI, and by aviatenavigate-communicate for the time for a parameter to reach an alert range and the 15-minute conditions. Overall, predictive information and the longer ITIs moved subjects to performing tasks before the alert actually occurred and had them more mission oriented as indicated by their tasks grouping of aviate-navigate-communicate.

  8. Forming Attitudes That Predict Future Behavior: A Meta-Analysis of the Attitude–Behavior Relation

    Science.gov (United States)

    Glasman, Laura R.; Albarracín, Dolores

    2016-01-01

    A meta-analysis (k of conditions = 128; N = 4,598) examined the influence of factors present at the time an attitude is formed on the degree to which this attitude guides future behavior. The findings indicated that attitudes correlated with a future behavior more strongly when they were easy to recall (accessible) and stable over time. Because of increased accessibility, attitudes more strongly predicted future behavior when participants had direct experience with the attitude object and reported their attitudes frequently. Because of the resulting attitude stability, the attitude–behavior association was strongest when attitudes were confident, when participants formed their attitude on the basis of behavior-relevant information, and when they received or were induced to think about one- rather than two-sided information about the attitude object. PMID:16910754

  9. Do infant behaviors following immunization predict attachment? An exploratory study.

    Science.gov (United States)

    Horton, Rachel; Pillai Riddell, Rebecca; Moran, Greg; Lisi, Diana

    2016-01-01

    The relationship between infant behaviors during routine immunization, pre- and post-needle, and infant attachment was explored. A total of 130 parent-infant dyads were recruited from a larger longitudinal study and videotaped during routine immunization at 12 months and the Strange Situation Procedure (SSP) at 14 months. Six infant behaviors were coded for 1-minute pre-needle and 3-minutes post-needle. Attachment was operationalized according to the secure/avoidant/resistant/disorganized categories. As expected, none of the pre-needle behaviors predicted attachment. Proximity-seeking post-needle significantly discriminated attachment categorizations. Secure infants were more likely to seek proximity to caregivers post-needle in comparison with avoidant and disorganized infants. Proximity-seeking following immunization was positively correlated with proximity-seeking during the SSP and negatively correlated with avoidance and disorganization during the SSP. Infant proximity-seeking during immunization is associated with attachment security and parallels behaviors observed during the SSP. More research is needed to identify behavioral markers of disorganization.

  10. Observed Emotional and Behavioral Indicators of Motivation Predict School Readiness in Head Start Graduates

    Science.gov (United States)

    Berhenke, Amanda; Miller, Alison L.; Brown, Eleanor; Seifer, Ronald; Dickstein, Susan

    2011-01-01

    Emotions and behaviors observed during challenging tasks are hypothesized to be valuable indicators of young children's motivation, the assessment of which may be particularly important for children at risk for school failure. The current study demonstrated reliability and concurrent validity of a new observational assessment of motivation in young children. Head Start graduates completed challenging puzzle and trivia tasks during their kindergarten year. Children's emotion expression and task engagement were assessed based on their observed facial and verbal expressions and behavioral cues. Hierarchical regression analyses revealed that observed persistence and shame predicted teacher ratings of children's academic achievement, whereas interest, anxiety, pride, shame, and persistence predicted children's social skills and learning-related behaviors. Children's emotional and behavioral responses to challenge thus appeared to be important indicators of school success. Observation of such responses may be a useful and valid alternative to self-report measures of motivation at this age. PMID:21949599

  11. Which behavioral, emotional and school problems in middle-childhood predict early sexual behavior?

    Science.gov (United States)

    Parkes, Alison; Waylen, Andrea; Sayal, Kapil; Heron, Jon; Henderson, Marion; Wight, Daniel; Macleod, John

    2014-04-01

    Mental health and school adjustment problems are thought to distinguish early sexual behavior from normative timing (16-18 years), but little is known about how early sexual behavior originates from these problems in middle-childhood. Existing studies do not allow for co-occurring problems, differences in onset and persistence, and there is no information on middle-childhood school adjustment in relationship to early sexual activity. This study examined associations between several middle-childhood problems and early sexual behavior, using a subsample (N = 4,739, 53 % female, 98 % white, mean age 15 years 6 months) from a birth cohort study, the Avon Longitudinal Study of Parents and Children. Adolescents provided information at age 15 on early sexual behavior (oral sex and/or intercourse) and sexual risk-taking, and at age 13 on prior risk involvement (sexual behavior, antisocial behavior and substance use). Information on hyperactivity/inattention, conduct problems, depressive symptoms, peer relationship problems, school dislike and school performance was collected in middle-childhood at Time 1 (6-8 years) and Time 2 (10-11 years). In agreement with previous research, conduct problems predicted early sexual behavior, although this was found only for persistent early problems. In addition, Time 2 school dislike predicted early sexual behavior, while peer relationship problems were protective. Persistent early school dislike further characterized higher-risk groups (early sexual behavior preceded by age 13 risk, or accompanied by higher sexual risk-taking). The study establishes middle-childhood school dislike as a novel risk factor for early sexual behavior and higher-risk groups, and the importance of persistent conduct problems. Implications for the identification of children at risk and targeted intervention are discussed, as well as suggestions for further research.

  12. Maternal Behavior Predicts Infant Neurophysiological and Behavioral Attention Processes in the First Year

    Science.gov (United States)

    Swingler, Margaret M.; Perry, Nicole B.; Calkins, Susan D.; Bell, Martha Ann

    2017-01-01

    We apply a biopsychosocial conceptualization to attention development in the 1st year and examine the role of neurophysiological and social processes on the development of early attention processes. We tested whether maternal behavior measured during 2 mother-child interaction tasks when infants (N = 388) were 5 months predicted infant medial…

  13. Gender perspective on the factors predicting recycling behavior: Implications from the theory of planned behavior.

    Science.gov (United States)

    Oztekin, Ceren; Teksöz, Gaye; Pamuk, Savas; Sahin, Elvan; Kilic, Dilek Sultan

    2017-04-01

    This study aimed to assess the role of some socio-psychological attributes in explaining recycling behavior of Turkish university community from a gender perspective within the context of the theory of planned behavior with an additional variable (past experience). The recycling behavior of whole sample, females and males, has been examined in 3 sessions -depending on the arguments that explain gendered pattern of private and public environmental behavior and sticking to the fact why females' stronger environmental values, beliefs, and attitudes do not translate consistently into greater engagement in public behavior. As a result of model runs, different variables shaping intention for behavior have been found, namely perceived behavior control for females and past behavior for males. Due to the low percent of the variance in explaining recycling behavior of females, they have been identified as the ones who do not carry out intentions (non-recyclers). Since intentions alone are capable of identifying recyclers accurately but not non-recyclers, there may be other factors to be considered to understand the reason for females not carrying out the intentions. The results of descriptive statistics supported the identification by attitudes toward recycling. Female attitudes were innate (recycling is good, necessary, useful and sensitive), whereas those of males were learnt (recycling is healthy, valuable and correct). Thus, it has been concluded that males' intention for recycling is shaped by their past behavior and the conclusion is supported by males having learnt attitude toward recycling whereas females' lack of intention for recycling is shaped by their perceived behavior control and is supported by their innate attitude for recycling. All in all, the results of the present study provide further support for the utility of the TPB as a model of behavioral prediction and concur with other studies examining the utility of the TPB in the context of recycling

  14. Testing multi-theory model (MTM in predicting initiation andsustenance of physical activity behavior among college students

    Directory of Open Access Journals (Sweden)

    Vinayak Nahar

    2016-06-01

    Conclusion: Based on this study’s findings, MTM appears to be a robust theoretical framework for predicting PA behavior change. Future research directions and development of suitable intervention strategies are discussed.

  15. Global brain dynamics during social exclusion predict subsequent behavioral conformity.

    Science.gov (United States)

    Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O'Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

    2018-02-01

    Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences.

  16. Predicting the behavior of techno-social systems.

    Science.gov (United States)

    Vespignani, Alessandro

    2009-07-24

    We live in an increasingly interconnected world of techno-social systems, in which infrastructures composed of different technological layers are interoperating within the social component that drives their use and development. Examples are provided by the Internet, the World Wide Web, WiFi communication technologies, and transportation and mobility infrastructures. The multiscale nature and complexity of these networks are crucial features in understanding and managing the networks. The accessibility of new data and the advances in the theory and modeling of complex networks are providing an integrated framework that brings us closer to achieving true predictive power of the behavior of techno-social systems.

  17. FIRE BEHAVIOR PREDICTING MODELS EFFICIENCY IN BRAZILIAN COMMERCIAL EUCALYPT PLANTATIONS

    Directory of Open Access Journals (Sweden)

    Benjamin Leonardo Alves White

    2016-12-01

    Full Text Available Knowing how a wildfire will behave is extremely important in order to assist in fire suppression and prevention operations. Since the 1940’s mathematical models to estimate how the fire will behave have been developed worldwide, however, none of them, until now, had their efficiency tested in Brazilian commercial eucalypt plantations nor in other vegetation types in the country. This study aims to verify the accuracy of the Rothermel (1972 fire spread model, the Byram (1959 flame length model, and the fire spread and length equations derived from the McArthur (1962 control burn meters. To meet these objectives, 105 experimental laboratory fires were done and their results compared with the predicted values from the models tested. The Rothermel and Byram models predicted better than McArthur’s, nevertheless, all of them underestimated the fire behavior aspects evaluated and were statistically different from the experimental data.

  18. The prediction of swimming performance in competition from behavioral information.

    Science.gov (United States)

    Rushall, B S; Leet, D

    1979-06-01

    The swimming performances of the Canadian Team at the 1976 Olympic Games were categorized as being improved or worse than previous best times in the events contested. The two groups had been previously assessed on the Psychological Inventories for Competitive Swimmers. A stepwise multiple-discriminant analysis of the inventory responses revealed that 13 test questions produced a perfect discrimination of group membership. The resultant discriminant functions for predicting performance classification were applied to the test responses of 157 swimmers at the 1977 Canadian Winter National Swimming Championships. Using the same performance classification criteria the accuracy of prediction was not better than chance in three of four sex by performance classifications. This yielded a failure to locate a set of behavioral factors which determine swimming performance improvements in elite competitive circumstances. The possibility of sets of factors which do not discriminate between performances in similar environments or between similar groups of swimmers was raised.

  19. Predicting Adolescent and Adult Antisocial Behavior among Adjudicated Delinquent Females

    Science.gov (United States)

    Cernkovich, Stephen A.; Lanctot, Nadine; Giordano, Peggy C.

    2008-01-01

    Studies identifying the mechanisms underlying the causes and consequences of antisocial behavior among female delinquents as they transit to adulthood are scarce and have important limitations: Most are based on official statistics, they typically are restricted to normative samples, and rarely do they gather prospective data from samples of…

  20. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes

    Science.gov (United States)

    Rudebeck, Peter H.; Murray, Elisabeth A.

    2014-01-01

    The orbitofrontal cortex (OFC) has long been associated with the flexible control of behavior and concepts such as behavioral inhibition, self-control and emotional regulation. These ideas emphasize the suppression of behaviors and emotions, but OFC’s affirmative functions have remained enigmatic. Here we review recent work that has advanced our understanding of this prefrontal area and how its functions are shaped through interaction with subcortical structures such as the amygdala. Recent findings have overturned theories emphasizing behavioral inhibition as OFC’s fundamental function. Instead, new findings indicate that OFC provides predictions about specific outcomes associated with stimuli, choices and actions, especially their moment-to-moment value based on current internal states. OFC function thereby encompasses a broad representation or model of an individual’s sensory milieu and potential actions, along with their relationship to likely behavioral outcomes. PMID:25521376

  1. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes.

    Science.gov (United States)

    Rudebeck, Peter H; Murray, Elisabeth A

    2014-12-17

    The orbitofrontal cortex (OFC) has long been associated with the flexible control of behavior and concepts such as behavioral inhibition, self-control, and emotional regulation. These ideas emphasize the suppression of behaviors and emotions, but OFC's affirmative functions have remained enigmatic. Here we review recent work that has advanced our understanding of this prefrontal area and how its functions are shaped through interaction with subcortical structures such as the amygdala. Recent findings have overturned theories emphasizing behavioral inhibition as OFC's fundamental function. Instead, new findings indicate that OFC provides predictions about specific outcomes associated with stimuli, choices, and actions, especially their moment-to-moment value based on current internal states. OFC function thereby encompasses a broad representation or model of an individual's sensory milieu and potential actions, along with their relationship to likely behavioral outcomes. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. The role of descriptive norm within the theory of planned behavior in predicting Korean Americans' exercise behavior.

    Science.gov (United States)

    Lee, Hyo

    2011-08-01

    There are few studies investigating psychosocial mechanisms in Korean Americans' exercise behavior. The present study tested the usefulness of the theory of planned behavior in predicting Korean American's exercise behavior and whether the descriptive norm (i.e., perceptions of what others do) improved the predictive validity of the theory of planned behavior. Using a retrospective design and self-report measures, web-survey responses from 198 Korean-American adults were analyzed using hierarchical regression analyses. The theory of planned behavior constructs accounted for 31% of exercise behavior and 43% of exercise intention. Intention and perceived behavioral control were significant predictors of exercise behavior. Although the descriptive norm did not augment the theory of planned behavior, all original constructs--attitude, injunctive norm (a narrow definition of subjective norm), and perceived behavioral control--statistically significantly predicted leisure-time physical activity intention. Future studies should consider random sampling, prospective design, and objective measures of physical activity.

  3. Basing assessment and treatment of problem behavior on behavioral momentum theory: Analyses of behavioral persistence.

    Science.gov (United States)

    Schieltz, Kelly M; Wacker, David P; Ringdahl, Joel E; Berg, Wendy K

    2017-08-01

    The connection, or bridge, between applied and basic behavior analysis has been long-established (Hake, 1982; Mace & Critchfield, 2010). In this article, we describe how clinical decisions can be based more directly on behavioral processes and how basing clinical procedures on behavioral processes can lead to improved clinical outcomes. As a case in point, we describe how applied behavior analyses of maintenance, and specifically the long-term maintenance of treatment effects related to problem behavior, can be adjusted and potentially enhanced by basing treatment on Behavioral Momentum Theory. We provide a brief review of the literature including descriptions of two translational studies that proposed changes in how differential reinforcement of alternative behavior treatments are conducted based on Behavioral Momentum Theory. We then describe current clinical examples of how these translations are continuing to impact the definitions, designs, analyses, and treatment procedures used in our clinical practice. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Adaptive Encoding of Outcome Prediction by Prefrontal Cortex Ensembles Supports Behavioral Flexibility.

    Science.gov (United States)

    Del Arco, Alberto; Park, Junchol; Wood, Jesse; Kim, Yunbok; Moghaddam, Bita

    2017-08-30

    The prefrontal cortex (PFC) is thought to play a critical role in behavioral flexibility by monitoring action-outcome contingencies. How PFC ensembles represent shifts in behavior in response to changes in these contingencies remains unclear. We recorded single-unit activity and local field potentials in the dorsomedial PFC (dmPFC) of male rats during a set-shifting task that required them to update their behavior, among competing options, in response to changes in action-outcome contingencies. As behavior was updated, a subset of PFC ensembles encoded the current trial outcome before the outcome was presented. This novel outcome-prediction encoding was absent in a control task, in which actions were rewarded pseudorandomly, indicating that PFC neurons are not merely providing an expectancy signal. In both control and set-shifting tasks, dmPFC neurons displayed postoutcome discrimination activity, indicating that these neurons also monitor whether a behavior is successful in generating rewards. Gamma-power oscillatory activity increased before the outcome in both tasks but did not differentiate between expected outcomes, suggesting that this measure is not related to set-shifting behavior but reflects expectation of an outcome after action execution. These results demonstrate that PFC neurons support flexible rule-based action selection by predicting outcomes that follow a particular action. SIGNIFICANCE STATEMENT Tracking action-outcome contingencies and modifying behavior when those contingencies change is critical to behavioral flexibility. We find that ensembles of dorsomedial prefrontal cortex neurons differentiate between expected outcomes when action-outcome contingencies change. This predictive mode of signaling may be used to promote a new response strategy at the service of behavioral flexibility. Copyright © 2017 the authors 0270-6474/17/378363-11$15.00/0.

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

    Science.gov (United States)

    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

  6. Speech Intelligibility Prediction Based on Mutual Information

    DEFF Research Database (Denmark)

    Jensen, Jesper; Taal, Cees H.

    2014-01-01

    This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related...... to the mutual information between critical-band amplitude envelopes of the clean signal and the corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude using...... a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The proposed model predicts that speech intelligibility cannot be improved by any processing of noisy critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well ( ρ > 0.95) in predicting...

  7. Calorimeter prediction based on multiple exponentials

    International Nuclear Information System (INIS)

    Smith, M.K.; Bracken, D.S.

    2002-01-01

    Calorimetry allows very precise measurements of nuclear material to be carried out, but it also requires relatively long measurement times to do so. The ability to accurately predict the equilibrium response of a calorimeter would significantly reduce the amount of time required for calorimetric assays. An algorithm has been developed that is effective at predicting the equilibrium response. This multi-exponential prediction algorithm is based on an iterative technique using commercial fitting routines that fit a constant plus a variable number of exponential terms to calorimeter data. Details of the implementation and the results of trials on a large number of calorimeter data sets will be presented

  8. Corticosterone predicts foraging behavior and parental care in macaroni penguins.

    Science.gov (United States)

    Crossin, Glenn T; Trathan, Phil N; Phillips, Richard A; Gorman, Kristen B; Dawson, Alistair; Sakamoto, Kentaro Q; Williams, Tony D

    2012-07-01

    Corticosterone has received considerable attention as the principal hormonal mediator of allostasis or physiological stress in wild animals. More recently, it has also been implicated in the regulation of parental care in breeding birds, particularly with respect to individual variation in foraging behavior and provisioning effort. There is also evidence that prolactin can work either inversely or additively with corticosterone to achieve this. Here we test the hypothesis that endogenous corticosterone plays a key physiological role in the control of foraging behavior and parental care, using a combination of exogenous corticosterone treatment, time-depth telemetry, and physiological sampling of female macaroni penguins (Eudyptes chrysolophus) during the brood-guard period of chick rearing, while simultaneously monitoring patterns of prolactin secretion. Plasma corticosterone levels were significantly higher in females given exogenous implants relative to those receiving sham implants. Increased corticosterone levels were associated with significantly higher levels of foraging and diving activity and greater mass gain in implanted females. Elevated plasma corticosterone was also associated with an apparent fitness benefit in the form of increased chick mass. Plasma prolactin levels did not correlate with corticosterone levels at any time, nor was prolactin correlated with any measure of foraging behavior or parental care. Our results provide support for the corticosterone-adaptation hypothesis, which predicts that higher corticosterone levels support increased foraging activity and parental effort.

  9. “Teens are from Mars, Adults are from Venus”: Analyzing and Predicting Age Groups with Behavioral Characteristics in Instagram

    Energy Technology Data Exchange (ETDEWEB)

    Han, Kyungsik; Lee, Sanghack; Jang, Jin; Jung, Yong; Lee, Dongwon

    2016-06-22

    We present behavioral characteristics of teens and adults in Instagram and prediction of them from their behaviors. Based on two independently created datasets from user profiles and tags, we identify teens and adults, and carry out comparative analyses on their online behaviors. Our study reveals: (1) significant behavioral differences between two age groups; (2) the empirical evidence of classifying teens and adults with up to 82% accuracy, using traditional predictive models, while two baseline methods achieve 68% at best; and (3) the robustness of our models by achieving 76%—81% when tested against an independent dataset obtained without using user profiles or tags.

  10. Fracture behavior of W based materials

    International Nuclear Information System (INIS)

    Hack, J.E.

    1991-01-01

    This report describes the results of a program to investigate the fracture properties of tungsten based materials. In particular, the role of crack velocity on crack instability was determined in a W-Fe-Ni-Co ''heavy alloy'' and pure polycrystalline tungsten. A considerable effort was expended on the development of an appropriate crack velocity gage for use on these materials. Having succeeded in that, the gage technology was employed to determine the crack velocity response to the applied level of stress intensity factor at the onset of crack instability in pre-cracked specimens. The results were also correlated to the failure mode observed in two material systems of interest. Major results include: (1) unstable crack velocity measurements on metallic specimens which require high spatial resolution require the use of brittle, insulating substrates, as opposed to the ductile, polymer based substrates employed in low spatial resolution measurements; and (2) brittle failure modes, such as cleavage, are characterized by relatively slow unstable crack velocities while evidence of high degrees of deformation are associated with failures which proceed at high unstable crack velocities. This latter behavior is consistent with the predictions of the modeling of Hack et al and may have a significant impact on the interpretation of fractographs in general

  11. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  12. Unique prediction of cannabis use severity and behaviors by delay discounting and behavioral economic demand.

    Science.gov (United States)

    Strickland, Justin C; Lile, Joshua A; Stoops, William W

    2017-07-01

    Few studies have simultaneously evaluated delay discounting and behavioral economic demand to determine their unique contribution to drug use. A recent study in cannabis users found that monetary delay discounting uniquely predicted cannabis dependence symptoms, whereas cannabis demand uniquely predicted use frequency. This study sought to replicate and extend this research by evaluating delay discounting and behavioral economic demand measures for multiple commodities and including a use quantity measure. Amazon.com's Mechanical Turk was used to sample individuals reporting recent cannabis use (n=64) and controls (n=72). Participants completed measures of monetary delay discounting as well as alcohol and cannabis delay discounting and demand. Cannabis users and controls did not differ on monetary delay discounting or alcohol delay discounting and demand. Among cannabis users, regression analyses indicated that cannabis delay discounting uniquely predicted use severity, whereas cannabis demand uniquely predicted use frequency and quantity. These effects remained significant after controlling for other delay discounting and demand measures. This research replicates previous outcomes relating delay discounting and demand with cannabis use and extends them by accounting for the contribution of multiple commodities. This research also demonstrates the ability of online crowdsourcing methods to complement traditional human laboratory techniques. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Utility of the theories of reasoned action and planned behavior for predicting physician behavior: a prospective analysis.

    Science.gov (United States)

    Millstein, S G

    1996-09-01

    The utility of the theory of reasoned action (TRA) and the theory of planned behavior (TPB) for prospectively predicting physicians' delivery of preventive services was compared. Primary care physicians (N = 765) completed 2 mail surveys at periods 6 months apart. The addition of perceived behavioral control to the TRA model significantly increased the variance accounted for in behavioral intention and subsequent behavior (p behavioral control had direct effects on behavior and interacted with social norms and behavioral intentions. Applications of models such as the TRA or TPB have focused primarily on predicting the behavioral intentions and behaviors of patients. Results suggest that these models have relevance for studying the behavior of health care providers as well.

  14. Do Implicit Attitudes Predict Actual Voting Behavior Particularly for Undecided Voters?

    Science.gov (United States)

    Friese, Malte; Smith, Colin Tucker; Plischke, Thomas; Bluemke, Matthias; Nosek, Brian A.

    2012-01-01

    The prediction of voting behavior of undecided voters poses a challenge to psychologists and pollsters. Recently, researchers argued that implicit attitudes would predict voting behavior particularly for undecided voters whereas explicit attitudes would predict voting behavior particularly for decided voters. We tested this assumption in two studies in two countries with distinct political systems in the context of real political elections. Results revealed that (a) explicit attitudes predicted voting behavior better than implicit attitudes for both decided and undecided voters, and (b) implicit attitudes predicted voting behavior better for decided than undecided voters. We propose that greater elaboration of attitudes produces stronger convergence between implicit and explicit attitudes resulting in better predictive validity of both, and less incremental validity of implicit over explicit attitudes for the prediction of voting behavior. However, greater incremental predictive validity of implicit over explicit attitudes may be associated with less elaboration. PMID:22952898

  15. Knowledge-based Fragment Binding Prediction

    Science.gov (United States)

    Tang, Grace W.; Altman, Russ B.

    2014-01-01

    Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. PMID:24762971

  16. Applying theory of planned behavior to predict exercise maintenance in sarcopenic elderly

    Science.gov (United States)

    Ahmad, Mohamad Hasnan; Shahar, Suzana; Teng, Nur Islami Mohd Fahmi; Manaf, Zahara Abdul; Sakian, Noor Ibrahim Mohd; Omar, Baharudin

    2014-01-01

    This study aimed to determine the factors associated with exercise behavior based on the theory of planned behavior (TPB) among the sarcopenic elderly people in Cheras, Kuala Lumpur. A total of 65 subjects with mean ages of 67.5±5.2 (men) and 66.1±5.1 (women) years participated in this study. Subjects were divided into two groups: 1) exercise group (n=34; 25 men, nine women); and 2) the control group (n=31; 22 men, nine women). Structural equation modeling, based on TPB components, was applied to determine specific factors that most contribute to and predict actual behavior toward exercise. Based on the TPB’s model, attitude (β=0.60) and perceived behavioral control (β=0.24) were the major predictors of intention to exercise among men at the baseline. Among women, the subjective norm (β=0.82) was the major predictor of intention to perform the exercise at the baseline. After 12 weeks, attitude (men’s, β=0.68; women’s, β=0.24) and subjective norm (men’s, β=0.12; women’s, β=0.87) were the predictors of the intention to perform the exercise. “Feels healthier with exercise” was the specific factor to improve the intention to perform and to maintain exercise behavior in men (β=0.36) and women (β=0.49). “Not motivated to perform exercise” was the main barrier among men’s intention to exercise. The intention to perform the exercise was able to predict actual behavior regarding exercise at the baseline and at 12 weeks of an intervention program. As a conclusion, TPB is a useful model to determine and to predict maintenance of exercise in the sarcopenic elderly. PMID:25258524

  17. Economic demand predicts addiction-like behavior and therapeutic efficacy of oxytocin in the rat

    Science.gov (United States)

    Bentzley, Brandon S.; Jhou, Thomas C.; Aston-Jones, Gary

    2014-01-01

    Development of new treatments for drug addiction will depend on high-throughput screening in animal models. However, an addiction biomarker fit for rapid testing, and useful in both humans and animals, is not currently available. Economic models are promising candidates. They offer a structured quantitative approach to modeling behavior that is mathematically identical across species, and accruing evidence indicates economic-based descriptors of human behavior may be particularly useful biomarkers of addiction severity. However, economic demand has not yet been established as a biomarker of addiction-like behavior in animals, an essential final step in linking animal and human studies of addiction through economic models. We recently developed a mathematical approach for rapidly modeling economic demand in rats trained to self-administer cocaine. We show here that economic demand, as both a spontaneous trait and induced state, predicts addiction-like behavior, including relapse propensity, drug seeking in abstinence, and compulsive (punished) drug taking. These findings confirm economic demand as a biomarker of addiction-like behavior in rats. They also support the view that excessive motivation plays an important role in addiction while extending the idea that drug dependence represents a shift from initially recreational to compulsive drug use. Finally, we found that economic demand for cocaine predicted the efficacy of a promising pharmacotherapy (oxytocin) in attenuating cocaine-seeking behaviors across individuals, demonstrating that economic measures may be used to rapidly identify the clinical utility of prospective addiction treatments. PMID:25071176

  18. Predicting Learned Helplessness Based on Personality

    Science.gov (United States)

    Maadikhah, Elham; Erfani, Nasrollah

    2014-01-01

    Learned helplessness as a negative motivational state can latently underlie repeated failures and create negative feelings toward the education as well as depression in students and other members of a society. The purpose of this paper is to predict learned helplessness based on students' personality traits. The research is a predictive…

  19. Predicting Academics via Behavior within an Elementary Sample: An Evaluation of the Social, Academic, and Emotional Behavior Risk Screener (SAEBRS)

    Science.gov (United States)

    Kilgus, Stephen P.; Bowman, Nicollette A.; Christ, Theodore J.; Taylor, Crystal N.

    2017-01-01

    This study examined the extent to which teacher ratings of student behavior via the "Social, Academic, and Emotional Behavior Risk Screener" (SAEBRS) predicted academic achievement in math and reading. A secondary purpose was to compare the predictive capacity of three SAEBRS subscales corresponding to social, academic, or emotional…

  20. The Pupillary Orienting Response Predicts Adaptive Behavioral Adjustment after Errors.

    Directory of Open Access Journals (Sweden)

    Peter R Murphy

    Full Text Available Reaction time (RT is commonly observed to slow down after an error. This post-error slowing (PES has been thought to arise from the strategic adoption of a more cautious response mode following deployment of cognitive control. Recently, an alternative account has suggested that PES results from interference due to an error-evoked orienting response. We investigated whether error-related orienting may in fact be a pre-cursor to adaptive post-error behavioral adjustment when the orienting response resolves before subsequent trial onset. We measured pupil dilation, a prototypical measure of autonomic orienting, during performance of a choice RT task with long inter-stimulus intervals, and found that the trial-by-trial magnitude of the error-evoked pupil response positively predicted both PES magnitude and the likelihood that the following response would be correct. These combined findings suggest that the magnitude of the error-related orienting response predicts an adaptive change of response strategy following errors, and thereby promote a reconciliation of the orienting and adaptive control accounts of PES.

  1. Eye Movements During Everyday Behavior Predict Personality Traits.

    Science.gov (United States)

    Hoppe, Sabrina; Loetscher, Tobias; Morey, Stephanie A; Bulling, Andreas

    2018-01-01

    Besides allowing us to perceive our surroundings, eye movements are also a window into our mind and a rich source of information on who we are, how we feel, and what we do. Here we show that eye movements during an everyday task predict aspects of our personality. We tracked eye movements of 42 participants while they ran an errand on a university campus and subsequently assessed their personality traits using well-established questionnaires. Using a state-of-the-art machine learning method and a rich set of features encoding different eye movement characteristics, we were able to reliably predict four of the Big Five personality traits (neuroticism, extraversion, agreeableness, conscientiousness) as well as perceptual curiosity only from eye movements. Further analysis revealed new relations between previously neglected eye movement characteristics and personality. Our findings demonstrate a considerable influence of personality on everyday eye movement control, thereby complementing earlier studies in laboratory settings. Improving automatic recognition and interpretation of human social signals is an important endeavor, enabling innovative design of human-computer systems capable of sensing spontaneous natural user behavior to facilitate efficient interaction and personalization.

  2. Eye Movements During Everyday Behavior Predict Personality Traits

    Directory of Open Access Journals (Sweden)

    Sabrina Hoppe

    2018-04-01

    Full Text Available Besides allowing us to perceive our surroundings, eye movements are also a window into our mind and a rich source of information on who we are, how we feel, and what we do. Here we show that eye movements during an everyday task predict aspects of our personality. We tracked eye movements of 42 participants while they ran an errand on a university campus and subsequently assessed their personality traits using well-established questionnaires. Using a state-of-the-art machine learning method and a rich set of features encoding different eye movement characteristics, we were able to reliably predict four of the Big Five personality traits (neuroticism, extraversion, agreeableness, conscientiousness as well as perceptual curiosity only from eye movements. Further analysis revealed new relations between previously neglected eye movement characteristics and personality. Our findings demonstrate a considerable influence of personality on everyday eye movement control, thereby complementing earlier studies in laboratory settings. Improving automatic recognition and interpretation of human social signals is an important endeavor, enabling innovative design of human–computer systems capable of sensing spontaneous natural user behavior to facilitate efficient interaction and personalization.

  3. Improved prediction of reservoir behavior through integration of quantitative geological and petrophysical data

    Energy Technology Data Exchange (ETDEWEB)

    Auman, J. B.; Davies, D. K.; Vessell, R. K.

    1997-08-01

    Methodology that promises improved reservoir characterization and prediction of permeability, production and injection behavior during primary and enhanced recovery operations was demonstrated. The method is based on identifying intervals of unique pore geometry by a combination of image analysis techniques and traditional petrophysical measurements to calculate rock type and estimate permeability and saturation. Results from a complex carbonate and sandstone reservoir were presented as illustrative examples of the versatility and high level of accuracy of this method in predicting reservoir quality. 16 refs., 5 tabs., 14 figs.

  4. Prediction of hot deformation behavior of high phosphorus steel using artificial neural network

    Science.gov (United States)

    Singh, Kanchan; Rajput, S. K.; Soota, T.; Verma, Vijay; Singh, Dharmendra

    2018-03-01

    To predict the hot deformation behavior of high phosphorus steel, the hot compression experiments were performed with the help of thermo-mechanical simulator Gleeble® 3800 in the temperatures ranging from 750 °C to 1050 °C and strain rates of 0.001 s-1, 0.01 s-1, 0.1 s-1, 0.5 s-1, 1.0 s-1 and 10 s-1. The experimental stress-strain data are employed to develop artificial neural network (ANN) model and their predictability. Using different combination of temperature, strain and strain rate as a input parameter and obtained experimental stress as a target, a multi-layer ANN model based on feed-forward back-propagation algorithm is trained, to predict the flow stress for a given processing condition. The relative error between predicted and experimental stress are in the range of ±3.5%, whereas the correlation coefficient (R2) of training and testing data are 0.99986 and 0.99999 respectively. This shows that a well-trained ANN model has excellent capability to predict the hot deformation behavior of materials. Comparative study shows quite good agreement of predicted and experimental values.

  5. Adolescent expectations of early death predict adult risk behaviors.

    Directory of Open Access Journals (Sweden)

    Quynh C Nguyen

    Full Text Available Only a handful of public health studies have investigated expectations of early death among adolescents. Associations have been found between these expectations and risk behaviors in adolescence. However, these beliefs may not only predict worse adolescent outcomes, but worse trajectories in health with ties to negative outcomes that endure into young adulthood. The objectives of this study were to investigate perceived chances of living to age 35 (Perceived Survival Expectations, PSE as a predictor of suicidal ideation, suicide attempt and substance use in young adulthood. We examined the predictive capacity of PSE on future suicidal ideation/attempt after accounting for sociodemographics, depressive symptoms, and history of suicide among family and friends to more fully assess its unique contribution to suicide risk. We investigated the influence of PSE on legal and illegal substance use and varying levels of substance use. We utilized the National Longitudinal Study of Adolescent Health (Add Health initiated in 1994-95 among 20,745 adolescents in grades 7-12 with follow-up interviews in 1996 (Wave II, 2001-02 (Wave III and 2008 (Wave IV; ages 24-32. Compared to those who were almost certain of living to age 35, perceiving a 50-50 or less chance of living to age 35 at Waves I or III predicted suicide attempt and ideation as well as regular substance use (i.e., exceeding daily limits for moderate drinking; smoking ≥ a pack/day; and using illicit substances other than marijuana at least weekly at Wave IV. Associations between PSE and detrimental adult outcomes were particularly strong for those reporting persistently low PSE at both Waves I and III. Low PSE at Wave I or Wave III was also related to a doubling and tripling, respectively, of death rates in young adulthood. Long-term and wide-ranging ties between PSE and detrimental outcomes suggest these expectations may contribute to identifying at-risk youth.

  6. Behavioral interventions for office-based care: behavior change.

    Science.gov (United States)

    Delfino, Matthew; Larzelere, Michele McCarthy

    2014-03-01

    Family physicians play an important role in identifying and treating the behavioral etiologies of morbidity and mortality. Changing behavior is a challenging process that begins with identifying a patient's readiness to change. Interventions, such as motivational interviewing, are used to increase a patient's desire to change, and cognitive behavioral therapy can be initiated to increase a patient's likelihood of change, particularly if barriers are identified. After patients embark on change, family physicians are uniquely positioned to connect them to self-help programs, more intensive psychotherapy, and newer technology-based support programs, and to provide repeated, brief, positive reinforcement. Specific behavioral interventions that can be effective include computerized smoking cessation programs; electronic reminders and support delivered by family physicians or other clinicians for weight loss; linkage to community-based programs for seniors; increased length and demands of in-school programs to support exercise participation by children; and access reduction education to prevent firearm injury. Written permission from the American Academy of Family Physicians is required for reproduction of this material in whole or in part in any form or medium.

  7. Supporting smartphone-based behavioral activation

    DEFF Research Database (Denmark)

    Bardram, Jakob Eyvind; Rohani, Darius A.; Tuxen, Nanna

    2017-01-01

    Behavioral activation has shown to be a simple yet efective therapy for depressive patients. The method relies on extensive collection of patient reported activity data on an hourly basis. We are currently in the process of designing a smartphone-based behavioral activation system for depressive...... disorders. However, it is an open question to what degree patients would use this approach given the high demand for user input. In order to investigate this question, we collected paper-based behavioral activation forms from 5 patients, covering in total 18 weeks, 115 days, and 1,614 hours of self......-reported activity data. In this paper we present an analysis of this data and discuss the implications for the design of a smartphone-based system for behavioral activation....

  8. A behavioral economic reward index predicts drinking resolutions: moderation revisited and compared with other outcomes.

    Science.gov (United States)

    Tucker, Jalie A; Roth, David L; Vignolo, Mary J; Westfall, Andrew O

    2009-04-01

    Data were pooled from 3 studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1- to 2-year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes than abstinence or relapse and to be predicted by more balanced preresolution monetary allocations between short-term and longer term objectives (i.e., drinking and saving for the future). Standardized odds ratios (ORs) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this "Alcohol-Savings Discretionary Expenditure" index predicted higher rates of abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of preresolution spending patterns aids in predicting moderation.

  9. Early-onset Conduct Problems: Predictions from daring temperament and risk taking behavior.

    Science.gov (United States)

    Bai, Sunhye; Lee, Steve S

    2017-12-01

    Given its considerable public health significance, identifying predictors of early expressions of conduct problems is a priority. We examined the predictive validity of daring, a key dimension of temperament, and the Balloon Analog Risk Task (BART), a laboratory-based measure of risk taking behavior, with respect to two-year change in parent, teacher-, and youth self-reported oppositional defiant disorder (ODD), conduct disorder (CD), and antisocial behavior. At baseline, 150 ethnically diverse 6- to 10-year old (M=7.8, SD=1.1; 69.3% male) youth with ( n =82) and without ( n =68) DSM-IV ADHD completed the BART whereas parents rated youth temperament (i.e., daring); parents and teachers also independently rated youth ODD and CD symptoms. Approximately 2 years later, multi-informant ratings of youth ODD, CD, and antisocial behavior were gathered from rating scales and interviews. Whereas risk taking on the BART was unrelated to conduct problems, individual differences in daring prospectively predicted multi-informant rated conduct problems, independent of baseline risk taking, conduct problems, and ADHD diagnostic status. Early differences in the propensity to show positive socio-emotional responses to risky or novel experiences uniquely predicted escalating conduct problems in childhood, even with control of other potent clinical correlates. We consider the role of temperament in the origins and development of significant conduct problems from childhood to adolescence, including possible explanatory mechanisms underlying these predictions.

  10. 融合因子分解机和用户行为预测的音乐推荐%Music recommendation based on factorization machine and user behavior prediction

    Institute of Scientific and Technical Information of China (English)

    潘洋; 陈盛双; 李石君

    2017-01-01

    Traditional music rating recommendation model has lower accuracy, and recommended accuracy has received a great impact, because of user insufficient score and large subjective difference. Mining user interest from huge history behavior data is an excellent method to address those problems of rating model. Features are extracted from user behavior data to establish feature model, which help get corresponding user preferences. FM(Factorization Machine)predicts different types of user behavior, which is the basis of recommendation. FM has made full use of useful music features and user features information. The most important is that FM can simulate hidden factors of user behavior data to fill sparse matrix, and sparsity has little impact on prediction. Compared with traditional recommendation models, ming user interest from behavior data is feasible, experimental results also show that this method has good effect on music recommendation.%针对传统音乐评分推荐模式用户评分缺失和主观差异性较大等问题,通过提取用户行为数据构建行为特征模型,用以分析用户行为与兴趣的关联性,并采用因子分解机(Factorization Machine,FM)预测用户行为类型,作为音乐推荐的依据.将FM应用到该方法中,充分利用音乐和用户属性特征,并且通过模拟用户行为特征数据中的隐因子来填充推荐的稀疏矩阵,降低数据稀疏对预测的影响.与传统音乐推荐方法相比,从用户历史行为中挖掘用户兴趣倾向以解决评分模型带来的问题更具可行性,实验结果表明该方法用于音乐推荐也具有良好的效果.

  11. BEHAVE: fire behavior prediction and fuel modeling system-BURN Subsystem, part 1

    Science.gov (United States)

    Patricia L. Andrews

    1986-01-01

    Describes BURN Subsystem, Part 1, the operational fire behavior prediction subsystem of the BEHAVE fire behavior prediction and fuel modeling system. The manual covers operation of the computer program, assumptions of the mathematical models used in the calculations, and application of the predictions.

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

    Science.gov (United States)

    Kiviniemi, Marc T; Brown-Kramer, Carolyn R

    2015-05-01

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

  13. Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Behavioral Domain.

    Science.gov (United States)

    Lytle, Leslie A; Nicastro, Holly L; Roberts, Susan B; Evans, Mary; Jakicic, John M; Laposky, Aaron D; Loria, Catherine M

    2018-04-01

    The ability to identify and measure behaviors that are related to weight loss and the prevention of weight regain is crucial to understanding the variability in response to obesity treatment and the development of tailored treatments. The overarching goal of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project is to provide obesity researchers with guidance on a set of constructs and measures that are related to weight control and that span and integrate obesity-related behavioral, biological, environmental, and psychosocial domains. This article describes how the behavioral domain subgroup identified the initial list of high-priority constructs and measures to be included, and it describes practical considerations for assessing the following four behavioral areas: eating, activity, sleep, and self-monitoring of weight. Challenges and considerations for advancing the science related to weight loss and maintenance behaviors are also discussed. Assessing a set of core behavioral measures in combination with those from other ADOPT domains is critical to improve our understanding of individual variability in response to adult obesity treatment. The selection of behavioral measures is based on the current science, although there continues to be much work needed in this field. © 2018 The Obesity Society.

  14. Board-invited review: Using behavior to predict and identify ill health in animals.

    Science.gov (United States)

    Weary, D M; Huzzey, J M; von Keyserlingk, M A G

    2009-02-01

    We review recent research in one of the oldest and most important applications of ethology: evaluating animal health. Traditionally, such evaluations have been based on subjective assessments of debilitative signs; animals are judged ill when they appear depressed or off feed. Such assessments are prone to error but can be dramatically improved with training using well-defined clinical criteria. The availability of new technology to automatically record behaviors allows for increased use of objective measures; automated measures of feeding behavior and intake are increasingly available in commercial agriculture, and recent work has shown these to be valuable indicators of illness. Research has also identified behaviors indicative of risk of disease or injury. For example, the time spent standing on wet, concrete surfaces can be used to predict susceptibility to hoof injuries in dairy cattle, and time spent nuzzling the udder of the sow can predict the risk of crushing in piglets. One conceptual advance has been to view decreased exploration, feeding, social, sexual, and other behaviors as a coordinated response that helps afflicted individuals recover from illness. We argue that the sickness behaviors most likely to decline are those that provide longer-term fitness benefits (such as play), as animals divert resources to those functions of critical short-term value such as maintaining body temperature. We urge future research assessing the strength of motivation to express sickness behaviors, allowing for quantitative estimates of how sick an animal feels. Finally, we call for new theoretical and empirical work on behaviors that may act to signal health status, including behaviors that have evolved as honest (i.e., reliable) signals of condition for offspring-parent, inter- and intra-sexual, and predator-prey communication.

  15. Highway traffic noise prediction based on GIS

    Science.gov (United States)

    Zhao, Jianghua; Qin, Qiming

    2014-05-01

    Before building a new road, we need to predict the traffic noise generated by vehicles. Traditional traffic noise prediction methods are based on certain locations and they are not only time-consuming, high cost, but also cannot be visualized. Geographical Information System (GIS) can not only solve the problem of manual data processing, but also can get noise values at any point. The paper selected a road segment from Wenxi to Heyang. According to the geographical overview of the study area and the comparison between several models, we combine the JTG B03-2006 model and the HJ2.4-2009 model to predict the traffic noise depending on the circumstances. Finally, we interpolate the noise values at each prediction point and then generate contours of noise. By overlaying the village data on the noise contour layer, we can get the thematic maps. The use of GIS for road traffic noise prediction greatly facilitates the decision-makers because of GIS spatial analysis function and visualization capabilities. We can clearly see the districts where noise are excessive, and thus it becomes convenient to optimize the road line and take noise reduction measures such as installing sound barriers and relocating villages and so on.

  16. THE ROLE OF RISK AVERSION IN PREDICTING INDIVIDUAL BEHAVIOR

    OpenAIRE

    Luigi Guiso; Monica Paiella

    2005-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers� decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways co...

  17. Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses

    International Nuclear Information System (INIS)

    Li, Liang; You, Sixiong; Yang, Chao; Yan, Bingjie; Song, Jian; Chen, Zheng

    2016-01-01

    Highlights: • The novel approximated global optimal energy management strategy has been proposed for hybrid powertrains. • Eight typical driving behaviors have been classified with K-means to deal with the multiplicative traffic conditions. • The stochastic driver models of different driving behaviors were established based on the Markov chains. • ECMS was used to modify the SMPC-based energy management strategy to improve its fuel economy. • The approximated global optimal energy management strategy for plug-in hybrid electric buses has been verified and analyzed. - Abstract: Driving cycles of a city bus is statistically characterized by some repetitive features, which makes the predictive energy management strategy very desirable to obtain approximate optimal fuel economy of a plug-in hybrid electric bus. But dealing with the complicated traffic conditions and finding an approximated global optimal strategy which is applicable to the plug-in hybrid electric bus still remains a challenging technique. To solve this problem, a novel driving-behavior-aware modified stochastic model predictive control method is proposed for the plug-in hybrid electric bus. Firstly, the K-means is employed to classify driving behaviors, and the driver models based on Markov chains is obtained under different kinds of driving behaviors. While the obtained driver behaviors are regarded as stochastic disturbance inputs, the local minimum fuel consumption might be obtained with a traditional stochastic model predictive control at each step, taking tracking the reference battery state of charge trajectory into consideration in the finite predictive horizons. However, this technique is still accompanied by some working points with reduced/worsened fuel economy. Thus, the stochastic model predictive control is modified with the equivalent consumption minimization strategy to eliminate these undesirable working points. The results in real-world city bus routines show that the

  18. Direct behavior rating as a school-based behavior screener for elementary and middle grades.

    Science.gov (United States)

    Chafouleas, Sandra M; Kilgus, Stephen P; Jaffery, Rose; Riley-Tillman, T Chris; Welsh, Megan; Christ, Theodore J

    2013-06-01

    The purpose of this study was to investigate how Direct Behavior Rating Single Item Scales (DBR-SIS) involving targets of academically engaged, disruptive, and respectful behaviors function in school-based screening assessment. Participants included 831 students in kindergarten through eighth grades who attended schools in the northeastern United States. Teachers provided behavior ratings for a sample of students in their classrooms on the DBR-SIS, the Behavioral and Emotional Screening System (Kamphaus & Reynolds, 2007), and the Student Risk Screening Scale (Drummond, 1994). Given variations in rating procedures to accommodate scheduling differences across grades, analysis was conducted separately for elementary school and middle school grade levels. Results suggested that the recommended cut scores, the combination of behavior targets, and the resulting conditional probability indices varied depending on grade level grouping (lower elementary, upper elementary, middle). For example, for the lower elementary grade level grouping, a combination of disruptive behavior (cut score=2) and academically engaged behavior (cut score=8) was considered to offer the best balance among indices of diagnostic accuracy, whereas a cut score of 1 for disruptive behavior and 8 for academically engaged behavior were recommended for the upper elementary school grade level grouping and cut scores of 1 and 9, respectively, were suggested for middle school grade level grouping. Generally, DBR-SIS cut scores considered optimal for screening using single or combined targets including academically engaged behavior and disruptive behavior by offering a reasonable balance of indices for sensitivity (.51-.90), specificity (.47-.83), negative predictive power (.94-.98), and positive predictive power (.14-.41). The single target of respectful behavior performed poorly across all grade level groups, and performance of DBR-SIS targets was relatively better in the elementary school than middle

  19. Personality and behavior prediction and consistency across cultures: A multimethod study of Blacks and Whites in South Africa.

    Science.gov (United States)

    Fetvadjiev, Velichko H; Meiring, Deon; van de Vijver, Fons J R; Nel, J Alewyn; Sekaja, Lusanda; Laher, Sumaya

    2018-03-01

    The cross-cultural universality of behavior's consistency and predictability from personality, assumed in trait models though challenged in cultural psychological models, has usually been operationalized in terms of beliefs and perceptions, and assessed using single-instance self-reports. In a multimethod study of actual behavior across a range of situations, we examined predictability and consistency in participants from the more collectivistic Black ethnic group and the more individualistic White group in South Africa. Participants completed personality questionnaires before the behavior measurements. In Study 1, 107 Black and 241 White students kept diaries for 21 days, recording their behaviors and the situations in which they had occurred. In Study 2, 57 Black and 52 White students were video-recorded in 12 situations in laboratory settings, and external observers scored their behaviors. Across both studies, behavior was predicted by personality on average equally well in the 2 groups, and equally well when using trait-adjective- and behavior-based personality measures. The few cultural differences in situational variability were not in line with individualism-collectivism; however, subjective perceptions of variability, operationalized as dialectical beliefs, were more in line with individualism-collectivism: Blacks viewed their behavior as more variable than Whites. We propose drawing a distinction between subjective beliefs and objective behavior in the study of personality and culture. Larger cultural differences can be expected in beliefs and perceptions than in the links between personality and actual behavior. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  20. Dst Prediction Based on Solar Wind Parameters

    Directory of Open Access Journals (Sweden)

    Yoon-Kyung Park

    2009-12-01

    Full Text Available We reevaluate the Burton equation (Burton et al. 1975 of predicting Dst index using high quality hourly solar wind data supplied by the ACE satellite for the period from 1998 to 2006. Sixty magnetic storms with monotonously decreasing main phase are selected. In order to determine the injection term (Q and the decay time (tau of the equation, we examine the relationships between Dst* and VB_s, Delta Dst* and VB_s, and Delta Dst* and Dst* during the magnetic storms. For this analysis, we take into account one hour of the propagation time from the ACE satellite to the magnetopause, and a half hour of the response time of the magnetosphere/ring current to the solar wind forcing. The injection term is found to be Q({nT}/h=-3.56VB_s for VB_s>0.5mV/m and Q({nT}/h=0 for VB_s leq0.5mV/m. The tau (hour is estimated as 0.060 Dst* + 16.65 for Dst*>-175nT and 6.15 hours for Dst* leq -175nT. Based on these empirical relationships, we predict the 60 magnetic storms and find that the correlation coefficient between the observed and predicted Dst* is 0.88. To evaluate the performance of our prediction scheme, the 60 magnetic storms are predicted again using the models by Burton et al. (1975 and O'Brien & McPherron (2000a. The correlation coefficients thus obtained are 0.85, the same value for both of the two models. In this respect, our model is slightly improved over the other two models as far as the correlation coefficients is concerned. Particularly our model does a better job than the other two models in predicting intense magnetic storms (Dst* lesssim -200nT.

  1. The growth benefits of aggressive behavior vary with individual metabolism and resource predictability

    NARCIS (Netherlands)

    Hoogenboom, Mia O.; Armstrong, John D.; Groothuis, Ton G. G.; Metcalfe, Neil B.

    2013-01-01

    Differences in behavioral responses to environmental conditions and biological interactions are a key determinant of individual performance. This study investigated how the availability and predictability of food resources modulates the growth of animals that adopt different behavioral strategies.

  2. Predictive Scheduling for Electric Vehicles Considering Uncertainty of Load and User Behaviors

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Bin; Huang, Rui; Wang, Yubo; Nazaripouya, Hamidreza; Qiu, Charlie; Chu, Chi-Cheng; Gadh, Rajit

    2016-05-02

    Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimization module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.

  3. Organization of intrinsic functional brain connectivity predicts decisions to reciprocate social behavior.

    Science.gov (United States)

    Cáceda, Ricardo; James, G Andrew; Gutman, David A; Kilts, Clinton D

    2015-10-01

    Reciprocation of trust exchanges is central to the development of interpersonal relationships and societal well-being. Understanding how humans make pro-social and self-centered decisions in dyadic interactions and how to predict these choices has been an area of great interest in social neuroscience. A functional magnetic resonance imaging (fMRI) based technology with potential clinical application is the study of resting state brain connectivity. We tested if resting state connectivity may predict choice behavior in a social context. Twenty-nine healthy adults underwent resting state fMRI before performing the Trust Game, a two person monetary exchange game. We assessed the ability of patterns of resting-state functional brain organization, demographic characteristics and a measure of moral development, the Defining Issues Test (DIT-2), to predict individuals' decisions to reciprocate money during the Trust Game. Subjects reciprocated in 74.9% of the trials. Independent component analysis identified canonical resting-state networks. Increased functional connectivity between the salience (bilateral insula/anterior cingulate) and central executive (dorsolateral prefrontal cortex/ posterior parietal cortex) networks significantly predicted the choice to reciprocate pro-social behavior (R(2) = 0.20, p = 0.015). Stepwise linear regression analysis showed that functional connectivity between these two networks (p = 0.002), age (p = 0.007) and DIT-2 personal interest schema score (p = 0.032) significantly predicted reciprocity behavior (R(2) = 0.498, p = 0.001). Intrinsic functional connectivity between neural networks in conjunction with other individual characteristics may be a valuable tool for predicting performance during social interactions. Future replication and temporal extension of these findings may bolster the understanding of decision making in clinical, financial and marketing settings. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Behaviors of impurity in ITER and DEMOs using BALDUR integrated predictive modeling code

    International Nuclear Information System (INIS)

    Onjun, Thawatchai; Buangam, Wannapa; Wisitsorasak, Apiwat

    2015-01-01

    The behaviors of impurity are investigated using self-consistent modeling of 1.5D BALDUR integrated predictive modeling code, in which theory-based models are used for both core and edge region. In these simulations, a combination of NCLASS neoclassical transport and Multi-mode (MMM95) anomalous transport model is used to compute a core transport. The boundary is taken to be at the top of the pedestal, where the pedestal values are described using a theory-based pedestal model. This pedestal temperature model is based on a combination of magnetic and flow shear stabilization pedestal width scaling and an infinite-n ballooning pressure gradient model. The time evolution of plasma current, temperature and density profiles is carried out for ITER and DEMOs plasmas. As a result, the impurity behaviors such as impurity accumulation and impurity transport can be investigated. (author)

  5. The neural components of empathy: Predicting daily prosocial behavior

    Science.gov (United States)

    Rameson, Lian T.; Lieberman, Matthew D.

    2014-01-01

    Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing painful, anxious, and happy events that varied in valence and amount of social context provided. They also completed a 14-day experience sampling survey that assessed real-world helping behaviors. The results demonstrate that empathy for positive and negative emotions selectively activates regions associated with positive and negative affect, respectively. In addition, the mirror system was more active during empathy for context-independent events (pain), whereas the mentalizing system was more active during empathy for context-dependent events (anxiety, happiness). Finally, the septal area, previously linked to prosocial motivation, was the only region that was commonly activated across empathy for pain, anxiety, and happiness. Septal activity during each of these empathic experiences was predictive of daily helping. These findings suggest that empathy has multiple input pathways, produces affect-congruent activations, and results in septally mediated prosocial motivation. PMID:22887480

  6. A neural network to predict reactor core behaviors

    International Nuclear Information System (INIS)

    Juan Jose Ortiz-Servin; Jose Alejandro Castillo; Pelta, David A.

    2014-01-01

    The global fuel management problem in BWRs (Boiling Water Reactors) can be understood as a very complex optimization problem, where the variables represent design decisions and the quality assessment of each solution is done through a complex and computational expensive simulation. This last aspect is the major impediment to perform an extensive exploration of the design space, mainly due to the time lost evaluating non promising solutions. In this work, we show how we can train a Multi-Layer Perceptron (MLP) to predict the reactor behavior for a given configuration. The trained MLP is able to evaluate the configurations immediately, thus allowing performing an exhaustive evaluation of the possible configurations derived from a stock of fuel lattices, fuel reload patterns and control rods patterns. For our particular problem, the number of configurations is approximately 7.7 x 10 10 ; the evaluation with the core simulator would need above 200 years, while only 100 hours were required with our approach to discern between bad and good configurations. The later were then evaluated by the simulator and we confirm the MLP usefulness. The good core configurations reached the energy requirements, satisfied the safety parameter constrains and they could reduce uranium enrichment costs. (authors)

  7. The neural components of empathy: predicting daily prosocial behavior.

    Science.gov (United States)

    Morelli, Sylvia A; Rameson, Lian T; Lieberman, Matthew D

    2014-01-01

    Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing painful, anxious, and happy events that varied in valence and amount of social context provided. They also completed a 14-day experience sampling survey that assessed real-world helping behaviors. The results demonstrate that empathy for positive and negative emotions selectively activates regions associated with positive and negative affect, respectively. In addition, the mirror system was more active during empathy for context-independent events (pain), whereas the mentalizing system was more active during empathy for context-dependent events (anxiety, happiness). Finally, the septal area, previously linked to prosocial motivation, was the only region that was commonly activated across empathy for pain, anxiety, and happiness. Septal activity during each of these empathic experiences was predictive of daily helping. These findings suggest that empathy has multiple input pathways, produces affect-congruent activations, and results in septally mediated prosocial motivation.

  8. Childhood and adolescent sexual behaviors predict adult sexual orientations

    Directory of Open Access Journals (Sweden)

    Keith W. Beard

    2015-12-01

    Full Text Available Anonymous retrospective data were provided by 3,443 adult participants via computer-assisted self-interview. This was the first study focused on determinants of adult sexual orientation to adjust for the effects of same-sex sibling incest. Five measures of adult sexual orientations (ASOs provided evidence consistent with the theory that ASOs result from early sex-specific romantic attachment, conditioning caused by early sexual experiences with partners, and other experiences, such as early masturbation using human images, acting synergistically with critical period learning, and sexual imprinting. Early same-sex crushes were the most powerful predictor of ASOs, and they also increased the likelihood of engaging in early same-sex partnered and masturbation behaviors. Incestuous experiences with same-sex siblings affected the ASOs of the incest participants. And, lesbian, gay, and bisexual participants tended to have an earlier onset of puberty than heterosexual controls within sexes. However, statistical analyses showed that the incest and puberty effects were mathematically explained by the participant’s early sexual experiences with partners and other experiences such as masturbation using human images. Early same-sex crushes were predicted by nuclear family variables implying that same-sex crushes were more likely when the opposite-sex parent modeled an unsatisfactory heterosexual romantic partner.

  9. Mathematical model predicts the elastic behavior of composite materials

    Directory of Open Access Journals (Sweden)

    Zoroastro de Miranda Boari

    2005-03-01

    Full Text Available Several studies have found that the non-uniform distribution of reinforcing elements in a composite material can markedly influence its characteristics of elastic and plastic deformation and that a composite's overall response is influenced by the physical and geometrical properties of its reinforcing phases. The finite element method, Eshelby's method and dislocation mechanisms are usually employed in formulating a composite's constitutive response. This paper discusses a composite material containing SiC particles in an aluminum matrix. The purpose of this study was to find the correlation between a composite material's particle distribution and its resistance, and to come up with a mathematical model to predict the material's elastic behavior. The proposed formulation was applied to establish the thermal stress field in the aluminum-SiC composite resulting from its fabrication process, whereby the mixture is prepared at 600 °C and the composite material is used at room temperature. The analytical results, which are presented as stress probabilities, were obtained from the mathematical model proposed herein. These results were compared with the numerical ones obtained by the FEM method. A comparison of the results of the two methods, analytical and numerical, reveals very similar average thermal stress values. It is also shown that Maxwell-Boltzmann's distribution law can be applied to identify the correlation between the material's particle distribution and its resistance, using Eshelby's thermal stresses.

  10. Plutonium in the environment. Can we predict its subsurface behavior?

    Energy Technology Data Exchange (ETDEWEB)

    Kersting, Annie [Glenn T. Seaborg Institute, Lawrence Livermore National Laborartory, CA (United States)

    2015-07-01

    There is an acute need to expedite progress toward a permanent storage facility that can safely isolated long-lived radionuclides from the biosphere. Significant uncertainty remains on how to safely store long-lived radionuclides that will make up the majority of the dose after a few hundred years.Plutonium (Pu) is of particular interest because of its high toxicity and long half life (t1/2 239Pu 2.4 x104 yrs). The chemical interactions of Pu are dependent on its oxidation state, which in turn control its stability and solubility. Understanding the interplay (the bio-geo-chemistry) between Pu and the repository environment is necessary to predict the conditions for which Pu will either migrate or remain immobile. A mechanistic understanding of the surface structure and reactivity of coupled Pu*mineral, Pu*organic ligand, and Pu*microbe interfacial processes is needed to advance our understanding Pu. To elucidate the mechanisms controlling Pu transport, we have investigated Pu desorption rates from montmorillonite and other mineral colloids. These data suggest that Pu desorption rates are slow enough that colloid-facilitated transport of adsorbed Pu is possible at the field scale (km distances and decade timescales). Additional experiments show that the presence of organic matter plays an important role in stabilizing Pu both in solution and on mineral surfaces. Our experiments are helping to develop a conceptual model of Pu subsurface behavior.

  11. Wavelet-based prediction of oil prices

    International Nuclear Information System (INIS)

    Yousefi, Shahriar; Weinreich, Ilona; Reinarz, Dominik

    2005-01-01

    This paper illustrates an application of wavelets as a possible vehicle for investigating the issue of market efficiency in futures markets for oil. The paper provides a short introduction to the wavelets and a few interesting wavelet-based contributions in economics and finance are briefly reviewed. A wavelet-based prediction procedure is introduced and market data on crude oil is used to provide forecasts over different forecasting horizons. The results are compared with data from futures markets for oil and the relative performance of this procedure is used to investigate whether futures markets are efficiently priced

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

    Science.gov (United States)

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

    2012-01-01

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

  13. Predicting children's sunscreen use: application of the theories of reasoned action and planned behavior.

    Science.gov (United States)

    Martin, S C; Jacobsen, P B; Lucas, D J; Branch, K A; Ferron, J M

    1999-07-01

    Skin cancer remains the most common form of cancer in the United States despite the fact that most cases can be prevented by limiting sun exposure. Childhood and adolescence are periods of life during which prolonged sun exposure is particularly common. Accordingly, promoting sun-protective behaviors during these formative years can be of critical importance in preventing skin cancer. The present study applied the theories of reasoned action and planned behavior to the understanding of children's sunscreen use. Based on these theories, it was hypothesized that attitudes, subjective norms, and perceived behavioral control would be related to intentions to use sunscreen, which, in turn, would be related to actual sunscreen use. Questionnaires measuring sun-related attitudes, beliefs, perceived control, and intentions were administered to 199 fourth graders (ages 9 to 13, mean = 10.3) attending public schools in Florida. Self-report measures of sun-related behavior were administered to the same subjects 1 month later. Results of correlational analyses were consistent with study hypotheses. Higher rates of sunscreen use at follow-up were predicted by stronger intentions to use sunscreen assessed 1 month previously. In addition, stronger intentions to use sunscreen were found to be related to more favorable attitudes toward sunscreen use, stronger beliefs that peers and parents favored sunscreen use, and greater perceptions of personal control in using sunscreen. Path and multiple regression analyses identified direct and indirect relationships among study variables that partially confirmed those predicted by the theories and provided support for the use of an expanded model that included perceived behavioral control. The present study confirmed hypotheses derived from the theories of reasoned action and planned behavior regarding the relation of attitudes, subjective norms, and perceived behavioral control to sunscreen use among fourth graders. In addition to their

  14. Behavioral rules of bank’s point-of-sale for segments description and scoring prediction

    Directory of Open Access Journals (Sweden)

    Mehdi Bizhani

    2011-04-01

    Full Text Available One of the important factors for the success of a bank industry is to monitor their customers' behavior and their point-of-sale (POS. The bank needs to know its merchants' behavior to find interesting ones to attract more transactions which results in the growth of its income and assets. The recency, frequency and monetary (RFM analysis is a famous approach for extracting behavior of customers and is a basis for marketing and customer relationship management (CRM, but it is not aligned enough for banking context. Introducing RF*M* in this article results in a better understanding of groups of merchants. Another artifact of RF*M* is RF*M* scoring which is applied in two ways, preprocessing the POSs and assigning behavioral meaningful labels to the merchants’ segments. The class labels and the RF*M* parameters are entered into a rule-based classification algorithm to achieve descriptive rules of the clusters. These descriptive rules outlined the boundaries of RF*M* parameters for each cluster. Since the rules are generated by a classification algorithm, they can also be applied for predicting the behavioral label and scoring of the upcoming POSs. These rules are called behavioral rules.

  15. Link prediction based on nonequilibrium cooperation effect

    Science.gov (United States)

    Li, Lanxi; Zhu, Xuzhen; Tian, Hui

    2018-04-01

    Link prediction in complex networks has become a common focus of many researchers. But most existing methods concentrate on neighbors, and rarely consider degree heterogeneity of two endpoints. Node degree represents the importance or status of endpoints. We describe the large-degree heterogeneity as the nonequilibrium between nodes. This nonequilibrium facilitates a stable cooperation between endpoints, so that two endpoints with large-degree heterogeneity tend to connect stably. We name such a phenomenon as the nonequilibrium cooperation effect. Therefore, this paper proposes a link prediction method based on the nonequilibrium cooperation effect to improve accuracy. Theoretical analysis will be processed in advance, and at the end, experiments will be performed in 12 real-world networks to compare the mainstream methods with our indices in the network through numerical analysis.

  16. A Simplified Micromechanical Modeling Approach to Predict the Tensile Flow Curve Behavior of Dual-Phase Steels

    Science.gov (United States)

    Nanda, Tarun; Kumar, B. Ravi; Singh, Vishal

    2017-11-01

    Micromechanical modeling is used to predict material's tensile flow curve behavior based on microstructural characteristics. This research develops a simplified micromechanical modeling approach for predicting flow curve behavior of dual-phase steels. The existing literature reports on two broad approaches for determining tensile flow curve of these steels. The modeling approach developed in this work attempts to overcome specific limitations of the existing two approaches. This approach combines dislocation-based strain-hardening method with rule of mixtures. In the first step of modeling, `dislocation-based strain-hardening method' was employed to predict tensile behavior of individual phases of ferrite and martensite. In the second step, the individual flow curves were combined using `rule of mixtures,' to obtain the composite dual-phase flow behavior. To check accuracy of proposed model, four distinct dual-phase microstructures comprising of different ferrite grain size, martensite fraction, and carbon content in martensite were processed by annealing experiments. The true stress-strain curves for various microstructures were predicted with the newly developed micromechanical model. The results of micromechanical model matched closely with those of actual tensile tests. Thus, this micromechanical modeling approach can be used to predict and optimize the tensile flow behavior of dual-phase steels.

  17. Applying theory of planned behavior to predict exercise maintenance in sarcopenic elderly

    Directory of Open Access Journals (Sweden)

    Ahmad MH

    2014-09-01

    Full Text Available Mohamad Hasnan Ahmad,1 Suzana Shahar,2 Nur Islami Mohd Fahmi Teng,2 Zahara Abdul Manaf,2 Noor Ibrahim Mohd Sakian,3 Baharudin Omar41Centre of Nutrition Epidemiology Research, Institute of Public Health, Ministry of Health, Kuala Lumpur, Malaysia; 2Dietetics Program, 3Occupational Therapy Program, 4Department of Biomedical Sciences, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia Abstract: This study aimed to determine the factors associated with exercise behavior based on the theory of planned behavior (TPB among the sarcopenic elderly people in Cheras, Kuala ­Lumpur. A total of 65 subjects with mean ages of 67.5±5.2 (men and 66.1±5.1 (women years participated in this study. Subjects were divided into two groups: 1 exercise group (n=34; 25 men, nine women; and 2 the control group (n=31; 22 men, nine women. Structural equation modeling, based on TPB components, was applied to determine specific factors that most contribute to and predict actual behavior toward exercise. Based on the TPB’s model, attitude (ß=0.60 and perceived behavioral control (ß=0.24 were the major predictors of intention to exercise among men at the baseline. Among women, the subjective norm (ß=0.82 was the major predictor of intention to perform the exercise at the baseline. After 12 weeks, attitude (men’s, ß=0.68; women’s, ß=0.24 and subjective norm (men’s, ß=0.12; women’s, ß=0.87 were the predictors of the intention to perform the exercise. “Feels healthier with exercise” was the specific factor to improve the intention to perform and to maintain exercise behavior in men (ß=0.36 and women (ß=0.49. “Not motivated to perform exercise” was the main barrier among men’s intention to exercise. The intention to perform the exercise was able to predict actual behavior regarding exercise at the baseline and at 12 weeks of an intervention program. As a conclusion, TPB is a useful model to determine and

  18. Brittle Creep Failure, Critical Behavior, and Time-to-Failure Prediction of Concrete under Uniaxial Compression

    Directory of Open Access Journals (Sweden)

    Yingchong Wang

    2015-01-01

    Full Text Available Understanding the time-dependent brittle deformation behavior of concrete as a main building material is fundamental for the lifetime prediction and engineering design. Herein, we present the experimental measures of brittle creep failure, critical behavior, and the dependence of time-to-failure, on the secondary creep rate of concrete under sustained uniaxial compression. A complete evolution process of creep failure is achieved. Three typical creep stages are observed, including the primary (decelerating, secondary (steady state creep regime, and tertiary creep (accelerating creep stages. The time-to-failure shows sample-specificity although all samples exhibit a similar creep process. All specimens exhibit a critical power-law behavior with an exponent of −0.51 ± 0.06, approximately equal to the theoretical value of −1/2. All samples have a long-term secondary stage characterized by a constant strain rate that dominates the lifetime of a sample. The average creep rate expressed by the total creep strain over the lifetime (tf-t0 for each specimen shows a power-law dependence on the secondary creep rate with an exponent of −1. This could provide a clue to the prediction of the time-to-failure of concrete, based on the monitoring of the creep behavior at the steady stage.

  19. Fire behavior potential in central Saskatchewan under predicted climate change : summary document

    International Nuclear Information System (INIS)

    Parisien, M.; Hirsch, K.; Todd, B.; Flannigan, M.; Kafka, V.; Flynn, N.

    2005-01-01

    This study assesses fire danger and fire behaviour potential in central Saskatchewan using simulated climate scenarios produced by the Canadian Regional Climate Model (CRCM), including scenario analysis of base, double and triple level carbon dioxide in the atmosphere and uses available forest fuels to develop an absolute measure of fire behaviour. For each of these climate scenarios, the CRCM-generated weather was used as input variables into the Canadian Forest Fire Behavior Prediction (FBP) System. Fire behavior potential was quantified using head fire intensity, a measure of the fire's energy output because it can be related to fire behavior characteristics, suppression effectiveness, and fire effects. The report discusses the implications of fire behavior potential changes for fire and forest management. Preliminary results suggest a large increase in area burned in the study area by the end of the twenty-first century. Some of the possible fire management activities for long-term prediction include: pre-positioning of resources, preparedness planning, prioritization of fire and forest management activities and fire threat evaluation. 16 refs., 1 tab, 7 figs

  20. Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.

    Science.gov (United States)

    Mokhtari, Fatemeh; Rejeski, W Jack; Zhu, Yingying; Wu, Guorong; Simpson, Sean L; Burdette, Jonathan H; Laurienti, Paul J

    2018-06-01

    More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnetic resonance imaging (fMRI) data to prospectively identify individuals most likely to succeed in a behavioral weight loss intervention. Brain imaging was performed in overweight or obese older adults (age: 65-79 years) who participated in an 18-month lifestyle weight loss intervention. Machine learning and functional brain networks were combined to produce multivariate prediction models. The prediction accuracy exceeded 95%, suggesting that there exists a consistent pattern of connectivity which correctly predicts success with weight loss at the individual level. Connectivity patterns that contributed to the prediction consisted of complex multivariate network components that substantially overlapped with known brain networks that are associated with behavior emergence, self-regulation, body awareness, and the sensory features of food. Future work on independent datasets and diverse populations is needed to corroborate our findings. Additionally, we believe that efforts can begin to

  1. TWT transmitter fault prediction based on ANFIS

    Science.gov (United States)

    Li, Mengyan; Li, Junshan; Li, Shuangshuang; Wang, Wenqing; Li, Fen

    2017-11-01

    Fault prediction is an important component of health management, and plays an important role in the reliability guarantee of complex electronic equipments. Transmitter is a unit with high failure rate. The cathode performance of TWT is a common fault of transmitter. In this dissertation, a model based on a set of key parameters of TWT is proposed. By choosing proper parameters and applying adaptive neural network training model, this method, combined with analytic hierarchy process (AHP), has a certain reference value for the overall health judgment of TWT transmitters.

  2. Applying the theory of planned behavior to predict dairy product consumption by older adults.

    Science.gov (United States)

    Kim, Kyungwon; Reicks, Marla; Sjoberg, Sara

    2003-01-01

    The purpose of this study was to explain intention to consume dairy products and consumption of dairy products by older adults using the Theory of Planned Behavior (TPB). The factors examined were attitudes, subjective norms, and perceived behavioral control. A cross-sectional questionnaire was administered. Community centers with congregate dining programs, group classes, and recreational events for older adults. One hundred and sixty-two older adults (mean age 75 years) completed the questionnaire. Subjects were mostly women (76%) and white (65%), with about half having less than a high school education or completing high school. Variables based on the TPB were assessed through questionnaire items that were constructed to form scales measuring attitudes, subjective norms, perceived behavioral control, and intention to consume dairy products. Dairy product consumption was measured using a food frequency questionnaire. Regression analyses were used to determine the association between the scales for the 3 variables proposed in the TPB and intention to consume and consumption of dairy products; the alpha level was set at.05 to determine the statistical significance of results. Attitudes toward eating dairy products and perceived behavioral control contributed to the model for predicting intention, whereas subjective norms did not. Attitudes toward eating dairy products were slightly more important than perceived behavioral control in predicting intention. In turn, intention was strongly related to dairy product consumption, and perceived behavioral control was independently associated with dairy product consumption. These results suggest the utility of the TPB in explaining dairy product consumption for older adults. Nutrition education should focus on improving attitudes and removing barriers to consumption of dairy products for older adults.

  3. A Theory of Planned Behavior Research Model for Predicting the Sleep Intentions and Behaviors of Undergraduate College Students

    Science.gov (United States)

    Knowlden, Adam P.; Sharma, Manoj; Bernard, Amy L.

    2012-01-01

    The purpose of this study was to operationalize the constructs of the Theory of Planned Behavior (TPB) to predict the sleep intentions and behaviors of undergraduate college students attending a Midwestern University. Data collection spanned three phases. The first phase included a semi-structured qualitative interview (n = 11), readability by…

  4. Does an ethic matter to predict misreporting behavior?

    OpenAIRE

    Ascaryan Rafinda; Triani Arofah; Rasyid Mei Mustafa; Halomoan Ompusunggu

    2015-01-01

    This study was conducted to verify the assertions of various previous studies examining the relationship between individual moral reasoning and ethical behavior. Those studies conclude that individuals with good moral reasoning tend to behave better. However, they do not consider situational factors that can change this individual behavior. This study attempts to consider situational factors linked to the individual as antecedents of unethical behavior. Situational factors are taken into acco...

  5. Predictive Validity of the Columbia-Suicide Severity Rating Scale for Short-Term Suicidal Behavior

    DEFF Research Database (Denmark)

    Conway, Paul Maurice; Erlangsen, Annette; Teasdale, Thomas William

    2017-01-01

    adolescents (90.6% females) who participated at follow-up (85.9%) out of the 99 (49.7%) baseline respondents. All adolescents were recruited from a specialized suicide-prevention clinic in Denmark. Through multivariate logistic regression analyses, we examined whether baseline suicidal behavior predicted......Using the Columbia-Suicide Severity Rating Scale (C-SSRS), we examined the predictive and incremental predictive validity of past-month suicidal behavior and ideation for short-term suicidal behavior among adolescents at high risk of suicide. The study was conducted in 2014 on a sample of 85...... subsequent suicidal behavior (actual attempts and suicidal behavior of any type, including preparatory acts, aborted, interrupted and actual attempts; mean follow-up of 80.8 days, SD = 52.4). Furthermore, we examined whether suicidal ideation severity and intensity incrementally predicted suicidal behavior...

  6. Predicting Sympathy and Prosocial Behavior from Young Children’s Dispositional Sadness

    Science.gov (United States)

    Edwards, Alison; Eisenberg, Nancy; Spinrad, Tracy L.; Reiser, Mark; Eggum-Wilkens, Natalie D.; Liew, Jeffrey

    2014-01-01

    The purpose of this study was to examine whether dispositional sadness predicted children's prosocial behavior and if sympathy mediated this relation. Constructs were measured when children (N = 256 at Time 1) were 18-, 30-, and 42-months old. Mothers and non-parental caregivers rated children’s sadness; mothers, caregivers, and fathers rated children’s prosocial behavior; sympathy (concern and hypothesis testing) and prosocial behavior (indirect and direct, as well as verbal at older ages) were assessed with a task in which the experimenter feigned injury. In a panel path analysis, 30-month dispositional sadness predicted marginally higher 42-month sympathy; in addition, 30-month sympathy predicted 42-month sadness. Moreover, when controlling for prior levels of prosocial behavior, 30-month sympathy significantly predicted reported and observed prosocial behavior at 42 months. Sympathy did not mediate the relation between sadness and prosocial behavior (either reported or observed). PMID:25663753

  7. Prediction of Vibrational Behavior of Grid-Stiffened Cylindrical Shells

    Directory of Open Access Journals (Sweden)

    G. H. Rahimi

    2014-01-01

    Full Text Available A unified analytical approach is applied to investigate the vibrational behavior of grid-stiffened cylindrical shells with different boundary conditions. A smeared method is employed to superimpose the stiffness contribution of the stiffeners with those of shell in order to obtain the equivalent stiffness parameters of the whole panel. Theoretical formulation is established based on Sanders’ thin shell theory. The modal forms are assumed to have the axial dependency in the form of Fourier series whose derivatives are legitimized using Stoke's transformation. A 3D finite element model is also built using ABAQUS software which takes into consideration the exact geometric configuration of the stiffeners and the shell. The achievements from the two types of analyses are compared with each other and good agreement has been obtained. The Influences of variations in shell geometrical parameters, boundary condition, and changes in the cross stiffeners angle on the natural frequencies are studied. The results obtained are novel and can be used as a benchmark for further studies. The simplicity and the capability of the present method are also discussed.

  8. Neural oscillatory deficits in schizophrenia predict behavioral and neurocognitive impairments

    Directory of Open Access Journals (Sweden)

    Antigona eMartinez

    2015-07-01

    Full Text Available Paying attention to visual stimuli is typically accompanied by event-related desynchronizations (ERD of ongoing alpha (7-14 Hz activity in visual cortex. The present study used time-frequency based analyses to investigate the role of impaired alpha ERD in visual processing deficits in schizophrenia (Sz. Subjects viewed sinusoidal gratings of high (HSF and low (LSF spatial frequency designed to test functioning of the parvo- versus magnocellular pathways, respectively. Patients with Sz and healthy controls paid attention selectively to either the LSF or HSF gratings which were presented in random order. Event-related brain potentials (ERPs were recorded to all stimuli. As in our previous study, it was found that Sz patients were selectively impaired at detecting LSF target stimuli and that ERP amplitudes to LSF stimuli were diminished, both for the early sensory-evoked components and for the attend minus unattend difference component (the Selection Negativity, which is generally regarded as a specific index of feature-selective attention. In the time-frequency domain, the differential ERP deficits to LSF stimuli were echoed in a virtually absent theta-band phase locked response to both unattended and attended LSF stimuli (along with relatively intact theta-band activity for HSF stimuli. In contrast to the theta-band evoked responses which were tightly stimulus locked, stimulus-induced desynchronizations of ongoing alpha activity were not tightly stimulus locked and were apparent only in induced power analyses. Sz patients were significantly impaired in the attention-related modulation of ongoing alpha activity for both HSF and LSF stimuli. These deficits correlated with patients’ behavioral deficits in visual information processing as well as with visually based neurocognitive deficits. These findings suggest an additional, pathway-independent, mechanism by which deficits in early visual processing contribute to overall cognitive impairment in

  9. Specific responsible environmental behavior among boaters on the Chesapeake Bay: a predictive model part II

    Science.gov (United States)

    Stuart P. Cottrell; Alan R. Graefe

    1995-01-01

    This paper examines predictors of boater behavior in a specific behavior situation, namely the percentage of raw sewage discharged from recreational vessels in a sanitation pumpout facility on the Chesapeake Bay. Results of a multiple regression analysis show knowledge predicts behavior in specific issue situations. In addition, the more specific the...

  10. Using Theory of Planned Behavior to Predict Healthy Eating among Danish Adolescents

    Science.gov (United States)

    Gronhoj, Alice; Bech-Larsen, Tino; Chan, Kara; Tsang, Lennon

    2013-01-01

    Purpose: The purpose of the study was to apply the theory of planned behavior to predict Danish adolescents' behavioral intention for healthy eating. Design/methodology/approach: A cluster sample survey of 410 students aged 11 to 16 years studying in Grade 6 to Grade 10 was conducted in Denmark. Findings: Perceived behavioral control followed by…

  11. Changes in fire weather distributions: effects on predicted fire behavior

    Science.gov (United States)

    Lucy A. Salazar; Larry S. Bradshaw

    1984-01-01

    Data that represent average worst fire weather for a particular area are used to index daily fire danger; however, they do not account for different locations or diurnal weather changes that significantly affect fire behavior potential. To study the effects that selected changes in weather databases have on computed fire behavior parameters, weather data for the...

  12. Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis.

    Science.gov (United States)

    Agarwal, Vibhu; Zhang, Liangliang; Zhu, Josh; Fang, Shiyuan; Cheng, Tim; Hong, Chloe; Shah, Nigam H

    2016-09-21

    By recent estimates, the steady rise in health care costs has deprived more than 45 million Americans of health care services and has encouraged health care providers to better understand the key drivers of health care utilization from a population health management perspective. Prior studies suggest the feasibility of mining population-level patterns of health care resource utilization from observational analysis of Internet search logs; however, the utility of the endeavor to the various stakeholders in a health ecosystem remains unclear. The aim was to carry out a closed-loop evaluation of the utility of health care use predictions using the conversion rates of advertisements that were displayed to the predicted future utilizers as a surrogate. The statistical models to predict the probability of user's future visit to a medical facility were built using effective predictors of health care resource utilization, extracted from a deidentified dataset of geotagged mobile Internet search logs representing searches made by users of the Baidu search engine between March 2015 and May 2015. We inferred presence within the geofence of a medical facility from location and duration information from users' search logs and putatively assigned medical facility visit labels to qualifying search logs. We constructed a matrix of general, semantic, and location-based features from search logs of users that had 42 or more search days preceding a medical facility visit as well as from search logs of users that had no medical visits and trained statistical learners for predicting future medical visits. We then carried out a closed-loop evaluation of the utility of health care use predictions using the show conversion rates of advertisements displayed to the predicted future utilizers. In the context of behaviorally targeted advertising, wherein health care providers are interested in minimizing their cost per conversion, the association between show conversion rate and predicted

  13. Prediction of Ductile Fracture Behaviors for 42CrMo Steel at Elevated Temperatures

    Science.gov (United States)

    Lin, Y. C.; Liu, Yan-Xing; Liu, Ge; Chen, Ming-Song; Huang, Yuan-Chun

    2015-01-01

    The ductile fracture behaviors of 42CrMo steel are studied by hot tensile tests with the deformation temperature range of 1123-1373 K and strain rate range of 0.0001-0.1 s-1. Effects of deformation temperature and strain rate on the flow stress and fracture strain of the studied steel are discussed in detail. Based on the experimental results, a ductile damage model is established to describe the combined effects of deformation temperature and strain rate on the ductile fracture behaviors of 42CrMo steel. It is found that the flow stress first increases to a peak value and then decreases, showing an obvious dynamic softening. This is mainly attributed to the dynamic recrystallization and material intrinsic damage during the hot tensile deformation. The established damage model is verified by hot forging experiments and finite element simulations. Comparisons between the predicted and experimental results indicate that the established ductile damage model is capable of predicting the fracture behaviors of 42CrMo steel during hot forging.

  14. Predicting short-term weight loss using four leading health behavior change theories

    Directory of Open Access Journals (Sweden)

    Barata José T

    2007-04-01

    Full Text Available Abstract Background This study was conceived to analyze how exercise and weight management psychosocial variables, derived from several health behavior change theories, predict weight change in a short-term intervention. The theories under analysis were the Social Cognitive Theory, the Transtheoretical Model, the Theory of Planned Behavior, and Self-Determination Theory. Methods Subjects were 142 overweight and obese women (BMI = 30.2 ± 3.7 kg/m2; age = 38.3 ± 5.8y, participating in a 16-week University-based weight control program. Body weight and a comprehensive psychometric battery were assessed at baseline and at program's end. Results Weight decreased significantly (-3.6 ± 3.4%, p Conclusion The present models were able to predict 20–30% of variance in short-term weight loss and changes in weight management self-efficacy accounted for a large share of the predictive power. As expected from previous studies, exercise variables were only moderately associated with short-term outcomes; they are expected to play a larger explanatory role in longer-term results.

  15. Behavioral activation and inhibition system's role in predicting addictive behaviors of patients with bipolar disorder of Roozbeh Psychiatric Hospital

    Science.gov (United States)

    Abbasi, Moslem; Sadeghi, Hasan; Pirani, Zabih; Vatandoust, Leyla

    2016-01-01

    Background: Nowadays, prevalence of addictive behaviors among bipolar patients is considered to be a serious health threat by the World Health Organization. The aim of this study is to investigate the role of behavioral activation and inhibition systems in predicting addictive behaviors of male patients with bipolar disorder at the Roozbeh Psychiatric Hospital. Materials and Methods: The research method used in this study is correlation. The study population consisted of 80 male patients with bipolar disorder referring to the psychiatrics clinics of Tehran city in 2014 who were referred to the Roozbeh Psychiatric Hospital. To collect data, the international and comprehensive inventory diagnostic interview, behavioral activation and inhibition systems scale, and addictive behaviors scale were used. Results: The results showed that there is a positive and significant relationship between behavioral activation systems and addictive behaviors (addictive eating, alcohol addiction, television addiction, cigarette addiction, mobile addiction, etc.). In addition, correlation between behavioral inhibition systems and addictive behaviors (addictive eating, alcohol addiction, TV addiction, cigarette addiction, mobile addiction) is significantly negative. Finally, regression analysis showed that behavioral activation and inhibition systems could significantly predict 47% of addictive behaviors in patients with bipolar disorder. Conclusions: It can be said that the patients with bipolar disorder use substance and addictive behaviors for enjoyment and as pleasure stimulants; they also use substances to suppress unpleasant stimulants and negative emotions. These results indicate that behavioral activation and inhibition systems have an important role in the incidence and exacerbation of addictive behaviors. Therefore, preventive interventions in this direction seem to be necessary. PMID:28194203

  16. Recommendation advertising method based on behavior retargeting

    Science.gov (United States)

    Zhao, Yao; YIN, Xin-Chun; CHEN, Zhi-Min

    2011-10-01

    Online advertising has become an important business in e-commerce. Ad recommended algorithms are the most critical part in recommendation systems. We propose a recommendation advertising method based on behavior retargeting which can avoid leakage click of advertising due to objective reasons and can observe the changes of the user's interest in time. Experiments show that our new method can have a significant effect and can be further to apply to online system.

  17. Infant attachment security and early childhood behavioral inhibition interact to predict adolescent social anxiety symptoms.

    Science.gov (United States)

    Lewis-Morrarty, Erin; Degnan, Kathryn A; Chronis-Tuscano, Andrea; Pine, Daniel S; Henderson, Heather A; Fox, Nathan A

    2015-01-01

    Insecure attachment and behavioral inhibition (BI) increase risk for internalizing problems, but few longitudinal studies have examined their interaction in predicting adolescent anxiety. This study included 165 adolescents (ages 14-17 years) selected based on their reactivity to novelty at 4 months. Infant attachment was assessed with the Strange Situation. Multimethod BI assessments were conducted across childhood. Adolescents and their parents independently reported on anxiety. The interaction of attachment and BI significantly predicted adolescent anxiety symptoms, such that BI and anxiety were only associated among adolescents with histories of insecure attachment. Exploratory analyses revealed that this effect was driven by insecure-resistant attachment and that the association between BI and social anxiety was significant only for insecure males. Clinical implications are discussed. © 2014 The Authors. Child Development © 2014 Society for Research in Child Development, Inc.

  18. Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: A person-centered event-related potential study.

    Science.gov (United States)

    Stange, Jonathan P; MacNamara, Annmarie; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

    2017-06-23

    Single-trial-level analyses afford the ability to link neural indices of elaborative attention (such as the late positive potential [LPP], an event-related potential) with downstream markers of attentional processing (such as reaction time [RT]). This approach can provide useful information about individual differences in information processing, such as the ability to adapt behavior based on attentional demands ("brain-behavioral adaptability"). Anxiety and depression are associated with maladaptive information processing implicating aberrant cognition-emotion interactions, but whether brain-behavioral adaptability predicts response to psychotherapy is not known. We used a novel person-centered, trial-level analysis approach to link neural indices of stimulus processing to behavioral responses and to predict treatment outcome. Thirty-nine patients with anxiety and/or depression received 12 weeks of cognitive behavioral therapy (CBT). Prior to treatment, patients performed a speeded reaction-time task involving briefly-presented pairs of aversive and neutral pictures while electroencephalography was recorded. Multilevel modeling demonstrated that larger LPPs predicted slower responses on subsequent trials, suggesting that increased attention to the task-irrelevant nature of pictures interfered with reaction time on subsequent trials. Whereas using LPP and RT averages did not distinguish CBT responders from nonresponders, in trial-level analyses individuals who demonstrated greater ability to benefit behaviorally (i.e., faster RT) from smaller LPPs on the previous trial (greater brain-behavioral adaptability) were more likely to respond to treatment and showed greater improvements in depressive symptoms. These results highlight the utility of trial-level analyses to elucidate variability in within-subjects, brain-behavioral attentional coupling in the context of emotion processing, in predicting response to CBT for emotional disorders. Copyright © 2017 Elsevier Ltd

  19. Predicting homophobic behavior among heterosexual youth: domain general and sexual orientation-specific factors at the individual and contextual level.

    Science.gov (United States)

    Poteat, V Paul; DiGiovanni, Craig D; Scheer, Jillian R

    2013-03-01

    As a form of bias-based harassment, homophobic behavior remains prominent in schools. Yet, little attention has been given to factors that underlie it, aside from bullying and sexual prejudice. Thus, we examined multiple domain general (empathy, perspective-taking, classroom respect norms) and sexual orientation-specific factors (sexual orientation identity importance, number of sexual minority friends, parents' sexual minority attitudes, media messages). We documented support for a model in which these sets of factors converged to predict homophobic behavior, mediated through bullying and prejudice, among 581 students in grades 9-12 (55 % female). The structural equation model indicated that, with the exception of media messages, these additional factors predicted levels of prejudice and bullying, which in turn predicted the likelihood of students to engage in homophobic behavior. These findings highlight the importance of addressing multiple interrelated factors in efforts to reduce bullying, prejudice, and discrimination among youth.

  20. Prediction and Analysis of students Behavior using BARC Algorithm

    OpenAIRE

    M.Sindhuja; Dr.S.Rajalakshmi; S.M.Nandagopal

    2013-01-01

    Educational Data mining is a recent trends where data mining methods are experimented for the improvement of student performance in academics. The work describes the mining of higher education students’ related attributes such as behavior, attitude and relationship. The data were collected from a higher education institution in terms of the mentioned attributes. The proposed work explored Behavior Attitude Relationship Clustering (BARC) Algorithm, which showed the improvement in students’ per...

  1. Predicting Behavioral Problems in Craniopharyngioma Survivors after Conformal Radiation Therapy

    Science.gov (United States)

    Dolson, Eugenia P.; Conklin, Heather M.; Li, Chenghong; Xiong, Xiaoping; Merchant, Thomas E.

    2009-01-01

    Background Although radiation therapy is a primary treatment for craniopharyngioma, it can exacerbate existing problems related to the tumor and pre-irradiation management. Survival is often marked by neurologic deficits, panhypopituitarism, diabetes insipidus, cognitive deficiencies and behavioral and social problems. Procedure The Achenbach Child Behavior Checklist (CBCL) was used to evaluate behavioral and social problems during the first five years of follow-up in 27 patients with craniopharyngioma treated with conformal radiation therapy. Results All group averages for the CBCL scales were within the age-typical range at pre-irradiation baseline. Extent of surgical resection was implicated in baseline differences for the Internalizing, Externalizing, Behavior Problem and Social scores. Significant longitudinal changes were found in Internalizing, Externalizing, Behavior Problem and School scores that correlated with tumor and treatment related factors. Conclusions The most common variables implicated in post-irradiation behavioral and social problems were CSF shunting, presence of an Ommaya reservoir, diabetes insipidus, and low pre-irradiation growth hormone levels. PMID:19191345

  2. Do Motivational Interviewing Behaviors Predict Reductions in Partner Aggression for Men and Women?

    Science.gov (United States)

    Woodin, Erica M.; Sotskova, Alina; O’Leary, K. Daniel

    2011-01-01

    Motivational interviewing is a directive, non-confrontational intervention to promote behavior change. The current study examined therapist behaviors during a successful brief motivational interviewing intervention for physically aggressive college dating couples (Woodin & O’Leary, 2010). Forty-five minute motivational interviews with each partner were videotaped and coded using the Motivational Interviewing Treatment Integrity scale (MITI; Moyers, Martin, Manuel, & Miller, 2003). Hierarchical modeling analyses demonstrated that therapist behaviors consistent with motivational interviewing competency predicted significantly greater reductions in physical aggression perpetration following the intervention. Specifically, greater reflection to question ratios by the therapists predicted reductions in aggression for both men and women, greater percentages of open versus closed questions predicted aggression reductions for women, and there was a trend for greater levels of global therapist empathy to predict aggression reductions for women. These findings provide evidence that motivational interviewing seems to have an effect on behavior change through therapist behaviors consistent with the theoretical underpinnings of motivational interviewing. PMID:22119133

  3. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  4. Impulsive approach tendencies towards physical activity and sedentary behaviors, but not reflective intentions, prospectively predict non-exercise activity thermogenesis.

    Science.gov (United States)

    Cheval, Boris; Sarrazin, Philippe; Pelletier, Luc

    2014-01-01

    Understanding the determinants of non-exercise activity thermogenesis (NEAT) is crucial, given its extensive health benefits. Some scholars have assumed that a proneness to react differently to environmental cues promoting sedentary versus active behaviors could be responsible for inter-individual differences in NEAT. In line with this reflection and grounded on the Reflective-Impulsive Model, we test the assumption that impulsive processes related to sedentary and physical activity behaviors can prospectively predict NEAT, operationalized as spontaneous effort exerted to maintain low intensity muscle contractions within the release phases of an intermittent maximal isometric contraction task. Participants (n = 91) completed a questionnaire assessing their intentions to adopt physical activity behaviors and a manikin task to assess impulsive approach tendencies towards physical activity behaviors (IAPA) and sedentary behaviors (IASB). Participants were then instructed to perform a maximal handgrip strength task and an intermittent maximal isometric contraction task. As hypothesized, multilevel regression analyses revealed that spontaneous effort was (a) positively predicted by IAPA, (b) negatively predicted by IASB, and (c) was not predicted by physical activity intentions, after controlling for some confounding variables such as age, sex, usual PA level and average force provided during the maximal-contraction phases of the task. These effects remained constant throughout all the phases of the task. This study demonstrated that impulsive processes may play a unique role in predicting spontaneous physical activity behaviors. Theoretically, this finding reinforces the utility of a motivational approach based on dual-process models to explain inter-individual differences in NEAT. Implications for health behavior theories and behavior change interventions are outlined.

  5. Impulsive approach tendencies towards physical activity and sedentary behaviors, but not reflective intentions, prospectively predict non-exercise activity thermogenesis.

    Directory of Open Access Journals (Sweden)

    Boris Cheval

    Full Text Available Understanding the determinants of non-exercise activity thermogenesis (NEAT is crucial, given its extensive health benefits. Some scholars have assumed that a proneness to react differently to environmental cues promoting sedentary versus active behaviors could be responsible for inter-individual differences in NEAT. In line with this reflection and grounded on the Reflective-Impulsive Model, we test the assumption that impulsive processes related to sedentary and physical activity behaviors can prospectively predict NEAT, operationalized as spontaneous effort exerted to maintain low intensity muscle contractions within the release phases of an intermittent maximal isometric contraction task. Participants (n = 91 completed a questionnaire assessing their intentions to adopt physical activity behaviors and a manikin task to assess impulsive approach tendencies towards physical activity behaviors (IAPA and sedentary behaviors (IASB. Participants were then instructed to perform a maximal handgrip strength task and an intermittent maximal isometric contraction task. As hypothesized, multilevel regression analyses revealed that spontaneous effort was (a positively predicted by IAPA, (b negatively predicted by IASB, and (c was not predicted by physical activity intentions, after controlling for some confounding variables such as age, sex, usual PA level and average force provided during the maximal-contraction phases of the task. These effects remained constant throughout all the phases of the task. This study demonstrated that impulsive processes may play a unique role in predicting spontaneous physical activity behaviors. Theoretically, this finding reinforces the utility of a motivational approach based on dual-process models to explain inter-individual differences in NEAT. Implications for health behavior theories and behavior change interventions are outlined.

  6. The Effect of Training on Adopting Behaviors Preventing from Knee Osteoarthritis Based on Planned Behavior Theory

    Directory of Open Access Journals (Sweden)

    S

    2015-05-01

    Full Text Available Introduction: the arthritis is believed to be among common diseases which prevail in the developed and developing countries, including Iran. In demographic studies, the prevalence of knee arthritis which stands at %15/3 in the population above 15-years old was shown. Owing to the fact that societies are about to be aged than before, the issue has become a growing significance in the subject matter of public health. The present study is conducted with an aim to investigate into the effect of training based on the planned behavior model on preventing the teachers of preliminary schools from getting knee arthritis. Methods: the study as an intervention research is of quasi-experimental kind. The population in question included 114 individuals among female teachers of preliminary schools who were brought to the study randomly and divided into two groups intervention and non-intervention. Based on the primary results, the educational contents were designed and submitted in the intervention group. After two months of executing the training program, the post test was carried out. The data was analyzed by SPSS version 18. Due to the loss of normality in data distribution, non- parametric tests were used. Results: the study demonstrated that the components of the planned behavior theory (i.e. the attitudes, subjective norms and the control of perceived behavior could altogether estimate %37 of intention and %43 of behavior. Meanwhile, the role of subjective norms (β =56/0 in predicting intention was overriding, In this study,after the educational program, control of perceived behavior scores increased of 32/50 ± 4/05 to 34/82 ± 5/66. indicating that the major obstacles in adopting behaviors preventing from knee arthritis are the lack of regular physical activity (%72/4 and failure to use western-style toilet (%57. Conclusion: In this Study the effect theory of planned behavior support in predicting exercise intentions and behavior in the prevention of

  7. Modeling and Control of CSTR using Model based Neural Network Predictive Control

    OpenAIRE

    Shrivastava, Piyush

    2012-01-01

    This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...

  8. The role of trait emotional intelligence in predicting networking behavior

    Directory of Open Access Journals (Sweden)

    Teresa Torres-Coronas

    2017-02-01

    Full Text Available Objective – The purpose of this paper is to obtain evidence of the relation between entrepreneur proactive networking behavior and trait emotional intelligence to support transition towards entrepreneurial careers. Design/methodology/approach – The Trait Emotional Intelligence Questionnaire-Short form (TEIQue-SF, developed by Cooper and Petrides (2010, was used to test hypotheses on the factors that define a proactive use of a professional network and their relationship with the individual level of trait emotional intelligence and its four components (well-being, self-control, emotionality and sociability. A questionnaire was sent to local entrepreneurs to verify whether trait emotional intelligence act as a predictor of proactive networking behavior. Theoretical foundation – We will be using Petrides and Furnham’s (2001 trait EI definition and EI will be studied within a personality framework (Petrides, 2001, Petrides & Furnham, 2001, 2006, 2014. Findings – Final findings partially confirms research hypothesis, with some components of EI (well-being and self-control factors showing a significant positive correlation with proactive networking behavior. This indicates that entrepreneurs’ ability to regulate emotions influences their networking behavior helping them to succeed in their business relationships. Practical implications – The present study provides a clear direction for further research by focusing on how trait emotional intelligence affects social networking behavior amongst entrepreneurs, thus demonstrating the utility of using trait EI to evaluate high potential entrepreneurs.

  9. Prediction of inelastic behavior and creep-fatigue life of perforated plates

    International Nuclear Information System (INIS)

    Igari, Toshihide; Setoguchi, Katsuya; Nakano, Shohki; Nomura, Shinichi

    1991-01-01

    Prediction methods of macroscopic and local stress-strain behavior of perforated plates in plastic and creep regime which are proposed by the authors are applied to the inelastic analysis and creep-fatigue life prediction of perforated cylinder subjected to cyclic thermal stress. Stress-strain behavior of perforated cylinder is analyzed by modeling the perforated portion to cylinder with equivalent-solid-plate properties. Creep-fatigue lives at around a hole of perforated plates are predicted by using the local stress-strain behavior and are compared with experimentally observed lives. (author)

  10. Prediction of macroscopic and local stress-strain behaviors of perforated plates under primary and secondary creep conditions

    International Nuclear Information System (INIS)

    Igari, Toshihide; Tokiyoshi, Takumi; Mizokami, Yorikata

    2000-01-01

    Prediction methods of macroscopic and local creep behaviors of perforated plates are examined in order to apply these methods to the structural design of perforated structures such as heat exchangers used in elevated temperatures. Both primary and secondary creeps are considered for predicting macroscopic and local creep behaviors of perorated plates which are made of actual structural materials. Both uniaxial and multiaxial loading of perforated plates are taken into consideration. The concept of effective stress is applied to the prediction of macroscopic creep behaviors of perforated plates, and the predicted results are compared with the numerical results by FEM for the unit section of perorated plated under creep, in order to confirm the propriety of the proposed method. Based on the idea that stress exponents in creep equations govern the stress distribution of perforated plates, a modified Neuber's rule is used for predicting local stress and strain concentrations. The propriety of this prediction method is shown through a comparison of the prediction with the numerical results by FEM for the unit section of perforated plates under creep, and experimental results by the Moire method. (author)

  11. A numerical simulation strategy on occupant evacuation behaviors and casualty prediction in a building during earthquakes

    Science.gov (United States)

    Li, Shuang; Yu, Xiaohui; Zhang, Yanjuan; Zhai, Changhai

    2018-01-01

    Casualty prediction in a building during earthquakes benefits to implement the economic loss estimation in the performance-based earthquake engineering methodology. Although after-earthquake observations reveal that the evacuation has effects on the quantity of occupant casualties during earthquakes, few current studies consider occupant movements in the building in casualty prediction procedures. To bridge this knowledge gap, a numerical simulation method using refined cellular automata model is presented, which can describe various occupant dynamic behaviors and building dimensions. The simulation on the occupant evacuation is verified by a recorded evacuation process from a school classroom in real-life 2013 Ya'an earthquake in China. The occupant casualties in the building under earthquakes are evaluated by coupling the building collapse process simulation by finite element method, the occupant evacuation simulation, and the casualty occurrence criteria with time and space synchronization. A case study of casualty prediction in a building during an earthquake is provided to demonstrate the effect of occupant movements on casualty prediction.

  12. A prediction model for the radiation safety management behavior of medical cyclotrons

    International Nuclear Information System (INIS)

    Jung, Ji Hye; Han, Eun Ok; Kim, Ssang Tae

    2008-01-01

    This study attempted to provide reference materials for improving the behavior level in radiation safety managements by drawing a prediction model that affects the radiation safety management behavior because the radiation safety management of medical Cyclotrons, which can be used to produce radioisotopes, is an important factor that protects radiation caused diseases not only for radiological operators but average users. In addition, this study obtained follows results through the investigation applied from January 2 to January 30, 2008 for the radiation safety managers employed in 24 authorized organizations, which have already installed Cyclotrons, through applying a specific form of questionnaire in which the validity was guaranteed by reference study, site investigation, and focus discussion by related experts. The radiation safety management were configured as seven steps: step 1 is a production preparation step, step 2 is an RI production step, step 3 is a synthesis step, step 4 is a distribution step, step 5 is a quality control step, step 6 is a carriage container packing step, and step 7 is a transportation step. It was recognized that the distribution step was the most exposed as 15 subjects (62.5%), the items of 'the sanction and permission related works' and 'the guarantee of installation facilities and production equipment' were the most difficult as 9 subjects (37.5%), and in the trouble steps in such exposure, the item of 'the synthesis and distribution' steps were 4 times, respectively (30.8%). In the score of the behavior level in radiation safety managements, the minimum and maximum scores were 2.42 and 4.00, respectively, and the average score was 3.46 ± 0.47 out of 4. Prosperity and well-being programs in the behavior and job in radiation safety managements (r=0.529) represented a significant correlation statistically. In the drawing of a prediction model based on the factors that affected the behavior in radiation safety managements, general

  13. A prediction model for the radiation safety management behavior of medical cyclotrons

    Energy Technology Data Exchange (ETDEWEB)

    Jung, Ji Hye; Han, Eun Ok [Daegu Health College, Daegu (Korea, Republic of); Kim, Ssang Tae [CareCamp Inc., Seoul (Korea, Republic of)

    2008-06-15

    This study attempted to provide reference materials for improving the behavior level in radiation safety managements by drawing a prediction model that affects the radiation safety management behavior because the radiation safety management of medical Cyclotrons, which can be used to produce radioisotopes, is an important factor that protects radiation caused diseases not only for radiological operators but average users. In addition, this study obtained follows results through the investigation applied from January 2 to January 30, 2008 for the radiation safety managers employed in 24 authorized organizations, which have already installed Cyclotrons, through applying a specific form of questionnaire in which the validity was guaranteed by reference study, site investigation, and focus discussion by related experts. The radiation safety management were configured as seven steps: step 1 is a production preparation step, step 2 is an RI production step, step 3 is a synthesis step, step 4 is a distribution step, step 5 is a quality control step, step 6 is a carriage container packing step, and step 7 is a transportation step. It was recognized that the distribution step was the most exposed as 15 subjects (62.5%), the items of 'the sanction and permission related works' and 'the guarantee of installation facilities and production equipment' were the most difficult as 9 subjects (37.5%), and in the trouble steps in such exposure, the item of 'the synthesis and distribution' steps were 4 times, respectively (30.8%). In the score of the behavior level in radiation safety managements, the minimum and maximum scores were 2.42 and 4.00, respectively, and the average score was 3.46 {+-} 0.47 out of 4. Prosperity and well-being programs in the behavior and job in radiation safety managements (r=0.529) represented a significant correlation statistically. In the drawing of a prediction model based on the factors that affected the behavior in

  14. Predicting Pilot Behavior in Medium Scale Scenarios Using Game Theory and Reinforcement Learning

    Science.gov (United States)

    Yildiz, Yildiray; Agogino, Adrian; Brat, Guillaume

    2013-01-01

    Effective automation is critical in achieving the capacity and safety goals of the Next Generation Air Traffic System. Unfortunately creating integration and validation tools for such automation is difficult as the interactions between automation and their human counterparts is complex and unpredictable. This validation becomes even more difficult as we integrate wide-reaching technologies that affect the behavior of different decision makers in the system such as pilots, controllers and airlines. While overt short-term behavior changes can be explicitly modeled with traditional agent modeling systems, subtle behavior changes caused by the integration of new technologies may snowball into larger problems and be very hard to detect. To overcome these obstacles, we show how integration of new technologies can be validated by learning behavior models based on goals. In this framework, human participants are not modeled explicitly. Instead, their goals are modeled and through reinforcement learning their actions are predicted. The main advantage to this approach is that modeling is done within the context of the entire system allowing for accurate modeling of all participants as they interact as a whole. In addition such an approach allows for efficient trade studies and feasibility testing on a wide range of automation scenarios. The goal of this paper is to test that such an approach is feasible. To do this we implement this approach using a simple discrete-state learning system on a scenario where 50 aircraft need to self-navigate using Automatic Dependent Surveillance-Broadcast (ADS-B) information. In this scenario, we show how the approach can be used to predict the ability of pilots to adequately balance aircraft separation and fly efficient paths. We present results with several levels of complexity and airspace congestion.

  15. Efficacy of the theory of planned behavior in predicting breastfeeding: Meta-analysis and structural equation modeling.

    Science.gov (United States)

    Guo, J L; Wang, T F; Liao, J Y; Huang, C M

    2016-02-01

    This study assessed the applicability and efficacy of the theory of planned behavior (TPB) in predicting breastfeeding. The TPB assumes a rational approach for engaging in various behaviors, and has been used extensively for explaining health behavior. However, most studies have tested the effectiveness of TPB constructs in predicting how people perform actions for their own benefit rather than performing behaviors that are beneficial to others, such as breastfeeding infants. A meta-analysis approach could help clarify the breastfeeding practice to promote breastfeeding. This study used meta-analytic procedures. We searched for studies to include in our analysis, examining those published between January 1, 1990 and December 31, 2013 in PubMed, Medline, CINAHL, ProQuest, and Mosby's Index. We also reviewed journals with a history of publishing breastfeeding studies and searched reference lists for potential articles to include. Ten studies comprising a total of 2694 participants were selected for analysis. These studies yielded 10 effect sizes from the TPB, which ranged from 0.20 to 0.59. Structural equation model analysis using the pooled correlation matrix enabled us to determine the relative coefficients among TPB constructs. Attitude, subjective norms, and perceived behavioral control were all significant predictors of breastfeeding intention, whereas intention was a strong predictor of breastfeeding behavior. Perceived behavioral control reached a borderline level of significance to breastfeeding behavior. Theoretical and empirical implications are discussed from the perspective of evidence-based practice. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Predicting Bullying: Exploring the Contributions of Childhood Negative Life Experiences in Predicting Adolescent Bullying Behavior.

    Science.gov (United States)

    Connell, Nadine M; Morris, Robert G; Piquero, Alex R

    2016-07-01

    Although there has been much interest in research on aggression and in particular bullying, a relatively less charted area of research has centered on articulating a better understanding of the mechanisms and processes by which persons are at increased risk for bullying. Furthermore, those studies that have investigated the linkages between childhood experiences and bullying perpetration have been limited with respect to definitional and operational issues, reliance on cross-sectional data, and the lack of assessing competing explanations of bullying perpetration. Using five waves of data from a community-based longitudinal sample of children followed through age 18 (N = 763), the current study examines the extent to which childhood negative life events in a variety of domains predict adolescent bullying. Results show that early childhood experiences, particularly those within the family and school domains, may alter life trajectories and can act as predictors for later adolescent bullying, thereby underscoring the potential importance that relatively minor experiences can have over the long term. Implications for future research based on these analyses are examined. © The Author(s) 2015.

  17. Prediction of BMI by impulsivity, eating behavior and activity level

    Directory of Open Access Journals (Sweden)

    Jiang Xiaxia

    2016-01-01

    Full Text Available Objective: Discuss the relationship between the impulsivity, eating behavior and activity level and the body mass index (BMI. Method: Test 147 female college students with the impulsivity questionnaire (BIS-11 and BIS/BAS, Dutch Eating Behavior Questionnaire (DBEQ, Sitting Time Scale (STS and Exercising Time Scale (ETS. Results: (1 The correlation analysis indicates that BMI and impulsivity (r = 0.43 and 0.52 have a significant positive correlation with the sitting time (r = 0.61 and a significant negative correlation with the activity level (r= −0.49. (2 The path analysis indicates that the reward sensitivity directly affects BMI and indirectly affects BMI through the activity level as well; the eating behavior has an insignificantly direct impact on BMI, because its impact is generated by the intermediary role of induced diet. Conclusion: (1 The impulsivity, eating behavior and activity level are closely related to BMI; (2 the activity level, sitting time and induced diet play an intermediary role between the impulsivity and BMI.

  18. Neural correlates of dynamically evolving interpersonal ties predict prosocial behavior

    NARCIS (Netherlands)

    J.J. Fahrenfort (Johannes J.); F.A.A.M. Winden, van (Frans); B. Pelloux (Benjamin); M. Stallen (Mirre); K.R. Ridderinkhof (Richard)

    2012-01-01

    textabstractThere is a growing interest for the determinants of human choice behavior in social settings. Upon initial contact, investment choices in social settings can be inherently risky, as the degree to which the other person will reciprocate is unknown. Nevertheless, people have been shown to

  19. Neural correlates of dynamically evolving interpersonal ties predict prosocial behavior

    NARCIS (Netherlands)

    Fahrenfort, J.J.; van Winden, F.A.A.M.; Pelloux, B.; Stallen, M.; Ridderinkhof, K.R.

    2012-01-01

    There is a growing interest for the determinants of human choice behavior in social settings. Upon initial contact, investment choices in social settings can be inherently risky, as the degree to which the other person will reciprocate is unknown. Nevertheless, people have been shown to exhibit

  20. Impatience and uncertainty : Experimental decisions predict adolescents' field behavior

    NARCIS (Netherlands)

    Sutter, M.; Kocher, M.G.; Rützler, D.; Trautmann, S.T.

    2013-01-01

    We study risk attitudes, ambiguity attitudes, and time preferences of 661 children and adolescents, aged ten to eighteen years, in an incentivized experiment and relate experimental choices to field behavior. Experimental measures of impatience are found to be significant predictors of

  1. Does an ethic matter to predict misreporting behavior?

    Directory of Open Access Journals (Sweden)

    Ascaryan Rafinda

    2015-07-01

    Full Text Available This study was conducted to verify the assertions of various previous studies examining the relationship between individual moral reasoning and ethical behavior. Those studies conclude that individuals with good moral reasoning tend to behave better. However, they do not consider situational factors that can change this individual behavior. This study attempts to consider situational factors linked to the individual as antecedents of unethical behavior. Situational factors are taken into account for verifying whether an individual with high moral reasoning in a situation that supports unethical actions will be acting unethically. The data were taken by experimental methods 2 1 between the subjects where the manipulation is by positive and negative treatment given to see the effect against their intentions to do fraud. The level of moral reasoning is measured using a test instrument which defines the issue for categorizing the participants with high morale and low morale. Difference- t-test was performed to investigate the differences between the two groups experimental. It shows that situational factors are things that can affect a person's ethical or unethical act regardless of their moral reasoning abilities. The implication is that to minimize the unethical behavior of employees, the company can focus on situational factors rather than individual moral.

  2. Disorganized Attachment and Inhibitory Capacity: Predicting Externalizing Problem Behaviors

    Science.gov (United States)

    Bohlin, Gunilla; Eninger, Lilianne; Brocki, Karin Cecilia; Thorell, Lisa B.

    2012-01-01

    The aim of the present study was to investigate whether attachment insecurity, focusing on disorganized attachment, and the executive function (EF) component of inhibition, assessed at age 5, were longitudinally related to general externalizing problem behaviors as well as to specific symptoms of ADHD and Autism spectrum disorder (ASD), and…

  3. Mobile Phone-Based Mood Ratings Prospectively Predict Psychotherapy Attendance.

    Science.gov (United States)

    Bruehlman-Senecal, Emma; Aguilera, Adrian; Schueller, Stephen M

    2017-09-01

    Psychotherapy nonattendance is a costly and pervasive problem. While prior research has identified stable patient-level predictors of attendance, far less is known about dynamic (i.e., time-varying) factors. Identifying dynamic predictors can clarify how clinical states relate to psychotherapy attendance and inform effective "just-in-time" interventions to promote attendance. The present study examines whether daily mood, as measured by responses to automated mobile phone-based text messages, prospectively predicts attendance in group cognitive-behavioral therapy (CBT) for depression. Fifty-six Spanish-speaking Latino patients with elevated depressive symptoms (46 women, mean age=50.92years, SD=10.90years), enrolled in a manualized program of group CBT, received daily automated mood-monitoring text messages. Patients' daily mood ratings, message response rate, and delay in responding were recorded. Patients' self-reported mood the day prior to a scheduled psychotherapy session significantly predicted attendance, even after controlling for patients' prior attendance history and age (OR=1.33, 95% CI [1.04, 1.70], p=.02). Positive mood corresponded to a greater likelihood of attendance. Our results demonstrate the clinical utility of automated mood-monitoring text messages in predicting attendance. These results underscore the value of text messaging, and other mobile technologies, as adjuncts to psychotherapy. Future work should explore the use of such monitoring to guide interventions to increase attendance, and ultimately the efficacy of psychotherapy. Copyright © 2017. Published by Elsevier Ltd.

  4. Modeling the Temporal Nature of Human Behavior for Demographics Prediction

    DEFF Research Database (Denmark)

    Felbo, Bjarke; Sundsøy, Pål; Pentland, Alex

    2017-01-01

    Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of the datasets. For these reasons, there has been a growing...... interest in predicting demographic information from mobile phone metadata. Previous work focused on creating increasingly advanced features to be modeled with standard machine learning algorithms. We here instead model the raw mobile phone metadata directly using deep learning, exploiting the temporal...... on both age and gender prediction using only the temporal modality in mobile metadata. We finally validate our method on low activity users and evaluate the modeling assumptions....

  5. Research on software behavior trust based on hierarchy evaluation

    Science.gov (United States)

    Long, Ke; Xu, Haishui

    2017-08-01

    In view of the correlation software behavior, we evaluate software behavior credibility from two levels of control flow and data flow. In control flow level, method of the software behavior of trace based on support vector machine (SVM) is proposed. In data flow level, behavioral evidence evaluation based on fuzzy decision analysis method is put forward.

  6. Childhood self-regulatory skills predict adolescent smoking behavior.

    Science.gov (United States)

    deBlois, Madeleine E; Kubzansky, Laura D

    2016-01-01

    Cigarette smoking is the primary preventable cause of premature death. Better self-regulatory capacity is a key psychosocial factor that has been linked with reduced likelihood of tobacco use. Studies point to the importance of multiple forms of self-regulation, in the domains of emotion, attention, behavior, and social regulation, although no work has evaluated all of these domains in a single prospective study. Considering those four self-regulation domains separately and in combination, this study prospectively investigated whether greater self-regulation in childhood is associated with reduced likelihood of either trying cigarettes or becoming a regular smoker. Hypotheses were tested using longitudinal data from a cohort of 1709 US children participating in the Panel Study of Income Dynamics--Child Development Supplement. Self-regulation was assessed at study baseline when children ranged in age from 6 to 14 years, using parent-reported measures derived from the Behavior Problems Index and Positive Behavior Scale. Children ages 12-19 self-reported their cigarette smoking, defined in two ways: (1) trying and (2) regular use. Separate multiple logistic regression models were used to evaluate odds of trying or regularly using cigarettes, taking account of various potential confounders. Over an average of five years of follow-up, 34.5% of children ever tried cigarettes and 10.6% smoked regularly. Higher behavioral self-regulation was the only domain associated with reduced odds of trying cigarettes (odds ratio (OR) = .85, 95% confidence interval (CI) = .73-.99). Effective regulation in each of the domains was associated with reduced likelihood of regular smoking, although the association with social regulation was not statistically significant (ORs range .70-.85). For each additional domain in which a child was able to regulate successfully, the odds of becoming a regular smoker dropped by 18% (95% CI = .70-.97). These findings suggest that effective childhood self

  7. Unified model to predict flexural shear behavior of externally bonded RC beams

    International Nuclear Information System (INIS)

    Colotti, V.; Spadea, G.; Swamy, R.N.

    2006-01-01

    Structural strengthening with externally bonded reinforcement is now recognized as a cost-effective, structurally sound and practically efficient method of rehabilitating deteriorating and damaged reinforced concrete beams. There is now an urgent need to develop a sound engineering basis which can predict the failure loads of all such strengthened beams in a reliable and consistent manner. Existing models to predict the behavior at ultimate of strengthened beams suffer from many limitations and weaknesses. This paper presents a unified global model, based on the Strut-and-Tie approach, to predict the failure loads of reinforced concrete beams strengthened for flexure and/or shear. This structural model is based on rational engineering principles, considers all the possible failure modes, and incorporates the load transfer mechanism bond to reflect the debonding phenomena which has a dominant influence on the failure process of plated beams. The model is validated against about 200 strengthened beam test reported in the literature and failing in flexure and/or shear, involving a large number of structural variables and steel, carbon and glass fiber reinforced polymer laminates as reinforcing medium. (author)

  8. Prediction of mortality based on facial characteristics

    Directory of Open Access Journals (Sweden)

    Arnaud Delorme

    2016-05-01

    Full Text Available Recent studies have shown that characteristics of the face contain a wealth of information about health, age and chronic clinical conditions. Such studies involve objective measurement of facial features correlated with historical health information. But some individuals also claim to be adept at gauging mortality based on a glance at a person’s photograph. To test this claim, we invited 12 such individuals to see if they could determine if a person was alive or dead based solely on a brief examination of facial photographs. All photos used in the experiment were transformed into a uniform gray scale and then counterbalanced across eight categories: gender, age, gaze direction, glasses, head position, smile, hair color, and image resolution. Participants examined 404 photographs displayed on a computer monitor, one photo at a time, each shown for a maximum of 8 seconds. Half of the individuals in the photos were deceased, and half were alive at the time the experiment was conducted. Participants were asked to press a button if they thought the person in a photo was living or deceased. Overall mean accuracy on this task was 53.8%, where 50% was expected by chance (p < 0.004, two-tail. Statistically significant accuracy was independently obtained in 5 of the 12 participants. We also collected 32-channel electrophysiological recordings and observed a robust difference between images of deceased individuals correctly vs. incorrectly classified in the early event related potential at 100 ms post-stimulus onset. Our results support claims of individuals who report that some as-yet unknown features of the face predict mortality. The results are also compatible with claims about clairvoyance and warrants further investigation.

  9. Behavior-Based Assists for Telerobotic Manipulation

    International Nuclear Information System (INIS)

    Noakes, Mark W.; Hamel, Dr. William R.

    2008-01-01

    Teleoperated manipulation has been a critical tool in hazardous operations where the presence of humans has been precluded since the early days of nuclear material handling. Performance levels and limitations were understood and accepted. However, in the current era of decontamination and decommissioning (D and D) of facilities owned by the U.S. Department of Energy, there has been criticism that traditional remote systems are too expensive, too slow, and too difficult to use by cost-driven demolition companies. Previous research in telerobotics has attempted to alleviate some of these issues; however, it has been difficult to get capabilities generated in the research lab into the field. One major difficulty is the severely unstructured environments found in real D and D type environments. Behavior-based robotics (BBR) is based on concepts specifically designed to permit autonomous robots to function in unstructured environments. BBR schemes use sensor data to interact with the world directly rather than to generate models that are manipulated. Because the robot is immersed in its environment and since sensors are mounted on the robot, sensing and motion are inherently calibrated with respect to the robot. This paper presents a behavior-based approach and architecture for executing telerobotic D and D type tooling tasks

  10. BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    V. Dheepa

    2012-07-01

    Full Text Available Along with the great increase of internet and e-commerce, the use of credit card is an unavoidable one. Due to the increase of credit card usage, the frauds associated with this have also increased. There are a lot of approaches used to detect the frauds. In this paper, behavior based classification approach using Support Vector Machines are employed and efficient feature extraction method also adopted. If any discrepancies occur in the behaviors transaction pattern then it is predicted as suspicious and taken for further consideration to find the frauds. Generally credit card fraud detection problem suffers from a large amount of data, which is rectified by the proposed method. Achieving finest accuracy, high fraud catching rate and low false alarms are the main tasks of this approach.

  11. The proposition of a general version of the theory of planned behavior: Predicting ecological behavior

    NARCIS (Netherlands)

    Kaiser, F.G.; Gutscher, H.

    2003-01-01

    The present paper explores whether the theory of planned behavior (TPB) must abandon the notion that perceived behavioral control (PBC) has a direct influence on behavior. In a cross-sectional survey of 895 Swiss residents, our hypothesis was tested by means of structural equation models. Applied

  12. What Predicts Method Effects in Child Behavior Ratings

    Science.gov (United States)

    Low, Justin A.; Keith, Timothy Z.; Jensen, Megan

    2015-01-01

    The purpose of this research was to determine whether child, parent, and teacher characteristics such as sex, socioeconomic status (SES), parental depressive symptoms, the number of years of teaching experience, number of children in the classroom, and teachers' disciplinary self-efficacy predict deviations from maternal ratings in a…

  13. Subjective fear, interference by threat, and fear associations independently predict fear-related behavior in children

    NARCIS (Netherlands)

    Klein, A.M.; Kleinherenbrink, A.V.; Simons, C.; de Gier, E.; Klein, S.; Allart, E.; Bögels, S.M.; Becker, E.S.; Rinck, M.

    2012-01-01

    Background and objectives: Several information-processing models highlight the independent roles of controlled and automatic processes in explaining fearful behavior. Therefore, we investigated whether direct measures of controlled processes and indirect measures of automatic processes predict

  14. Internet gambling is a predictive factor of Internet addictive behavior.

    Science.gov (United States)

    Critselis, Elena; Janikian, Mari; Paleomilitou, Noni; Oikonomou, Despoina; Kassinopoulos, Marios; Kormas, George; Tsitsika, Artemis

    2013-12-01

    Adolescent Internet gambling is associated with concomitant addictive behaviors. This study aimed to assess the prevalence of Internet gambling practices, its impact upon psychosocial development and to evaluate the association between gambling practices and Internet addictive behavior among Cypriot adolescents. A cross-sectional study was conducted in a convenience sample (n = 805) of adolescents attending selected public schools (9th and 10th grades) in Cyprus. Anonymous self-completed questionnaires were used including the Internet Addiction Test and the Strengths and Difficulties Questionnaire. Among the study population (n = 805), approximately one third (n = 28; 34.9%) reported Internet gambling. Internet gamblers were twice as likely to utilize Internet café portals (adjusted odds ratio for gender and age, AOR: 2.13; 95% confidence interval, 95% CI: 1.56-2.91) for interactive game-playing (AOR: 6.84; 95% CI: 4.23-11.07), chat-rooms (AOR: 2.57; 95% CI: 1.31-4.85), and retrieval of sexual information (AOR: 1.99; 95% CI: 1.42-2.81). Among Internet gamblers 26.0% (n = 73) reported borderline addictive Internet use and 4.3% (n = 12) addictive behavior. Internet gamblers more often had comprehensive psychosocial and emotional maladjustment (AOR: 4.00; 95% CI: 1.97-8.13), including Abnormal Conduct Problems (AOR: 3.26; 95% CI: 2.00-5.32), Emotional Symptoms (AOR: 1.78; 95% CI: 1.02-3.11), and Peer Problems (AOR: 2.44; 95% CI: 1.08-5.48) scores. The multivariate regression analyses indicated that the single independent predictor associated with Internet addictive behavior was Internet gambling (AOR: 5.66; 95% CI: 1.45-22.15). Internet gambling is associated with addictive Internet use, as well as emotional maladjustment and behavioral problems, among Cypriot adolescents. Longitudinal studies are needed to elucidate whether Internet gambling constitutes a risk factor for the development of Internet addictive behavior among adolescents.

  15. Can current models of accommodation and vergence predict accommodative behavior in myopic children?

    Science.gov (United States)

    Sreenivasan, Vidhyapriya; Irving, Elizabeth L; Bobier, William R

    2014-08-01

    Investigations into the progression of myopia in children have long considered the role of accommodation as a cause and solution. Myopic children show high levels of accommodative adaptation, coupled with accommodative lag and high response AC/A (accommodative convergence per diopter of accommodation). This pattern differs from that predicted by current models of interaction between accommodation and vergence, where weakened reflex responses and a high AC/A would be associated with a low not high levels of accommodative adaptation. However, studies of young myopes were limited to only part of the accommodative vergence synkinesis and the reciprocal components of vergence adaptation and convergence accommodation were not studied in tandem. Accordingly, we test the hypothesis that the accommodative behavior of myopic children is not predicted by current models and whether that departure is explained by differences in the accommodative plant of the myopic child. Responses to incongruent stimuli (-2D, +2D adds, 10 prism diopter base-out prism) were investigated in 28 myopic and 25 non-myopic children aged 7-15 years. Subjects were divided into phoria groups - exo, ortho and eso based upon their near phoria. The school aged myopes showed high levels of accommodative adaptation but with reduced accommodation and high AC/A. This pattern is not explained by current adult models and could reflect a sluggish gain of the accommodative plant (ciliary muscle and lens), changes in near triad innervation or both. Further, vergence adaptation showed a predictable reciprocal relationship with the high accommodative adaptation, suggesting that departures from adult models were limited to accommodation not vergence behavior. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Predicting students' happiness from physiology, phone, mobility, and behavioral data.

    Science.gov (United States)

    Jaques, Natasha; Taylor, Sara; Azaria, Asaph; Ghandeharioun, Asma; Sano, Akane; Picard, Rosalind

    2015-09-01

    In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.

  17. Sensation Seeking Predicting Growth in Adolescent Problem Behaviors

    Science.gov (United States)

    Byck, Gayle R.; Swann, Greg; Schalet, Benjamin; Bolland, John; Mustanski, Brian

    2014-01-01

    There is limited literature on the relationship between sensation seeking and adolescent risk behaviors, particularly among African Americans. We tested the association between psychometrically-derived subscales of the Zuckerman Sensation Seeking Scale and the intercepts and slopes of individual growth curves of conduct problems, sexual risk taking, and substance use from ages 13-18 years by sex. Boys and girls had different associations between sensation seeking and baseline levels and growth of risk behaviors. The Pleasure Seeking scale was associated with baseline levels of conduct problems in boys and girls, baseline substance use in boys, and growth in sexual risk taking and substance use by girls. Girls had the same pattern of associations with the Danger/Novelty scale as the Pleasure Seeking scale. Knowledge about the relationships between adolescent risk taking and sensation seeking can help in the targeted design of prevention and intervention programs for the understudied population of very low-income, African American adolescents. PMID:25112599

  18. The role of trait emotional intelligence in predicting networking behavior

    OpenAIRE

    Teresa Torres-Coronas; María-Arántzazu Vidal-Blasco

    2017-01-01

    Objective – The purpose of this paper is to obtain evidence of the relation between entrepreneur proactive networking behavior and trait emotional intelligence to support transition towards entrepreneurial careers. Design/methodology/approach – The Trait Emotional Intelligence Questionnaire-Short form (TEIQue-SF), developed by Cooper and Petrides (2010), was used to test hypotheses on the factors that define a proactive use of a professional network and their relationship with the indivi...

  19. Predicting active school travel: The role of planned behavior and habit strength

    Science.gov (United States)

    2012-01-01

    Background Despite strong support for predictive validity of the theory of planned behavior (TPB) substantial variance in both intention and behavior is unaccounted for by the model’s predictors. The present study tested the extent to which habit strength augments the predictive validity of the TPB in relation to a currently under-researched behavior that has important health implications, namely children’s active school travel. Method Participants (N = 126 children aged 8–9 years; 59 % males) were sampled from five elementary schools in the west of Scotland and completed questionnaire measures of all TPB constructs in relation to walking to school and both walking and car/bus use habit. Over the subsequent week, commuting steps on school journeys were measured objectively using an accelerometer. Hierarchical multiple regressions were used to test the predictive utility of the TPB and habit strength in relation to both intention and subsequent behavior. Results The TPB accounted for 41 % and 10 % of the variance in intention and objectively measured behavior, respectively. Together, walking habit and car/bus habit significantly increased the proportion of explained variance in both intention and behavior by 6 %. Perceived behavioral control and both walking and car/bus habit independently predicted intention. Intention and car/bus habit independently predicted behavior. Conclusions The TPB significantly predicts children’s active school travel. However, habit strength augments the predictive validity of the model. The results indicate that school travel is controlled by both intentional and habitual processes. In practice, interventions could usefully decrease the habitual use of motorized transport for travel to school and increase children’s intention to walk (via increases in perceived behavioral control and walking habit, and decreases in car/bus habit). Further research is needed to identify effective strategies for changing these

  20. Predicting dropout in outpatient dialectical behavior therapy with patients with borderline personality disorder receiving psychiatric disability.

    Science.gov (United States)

    Landes, Sara J; Chalker, Samantha A; Comtois, Katherine Anne

    2016-01-01

    Rates of treatment dropout in outpatient Dialectical Behavior Therapy (DBT) in the community can be as high as 24 % to 58 %, making dropout a great concern. The primary purpose of this article was to examine predictors of dropout from DBT in a community mental health setting. Participants were 56 consumers with borderline personality disorder (BPD) who were psychiatrically disabled participating in a larger feasibility trial of Dialectical Behavior Therapy- Accepting the Challenges of Exiting the System. The following variables were examined to see whether they predicted dropout in DBT: age, education level, baseline level of distress, baseline level of non-acceptance of emotional responses, and skills module in which a consumer started DBT skills group. These variables were chosen based on known predictors of dropout in consumers with BPD and in DBT, as well as an interest in what naturally occurring variables might impact dropout. The dropout rate in this sample was 51.8 %. Results of the logistic regression show that younger age, higher levels of baseline distress, and a higher level of baseline non-acceptance of emotional responses were significantly associated with dropout. The DBT skills module in which an individual started group did not predict dropout. The implications of these findings are that knowledge of consumer age and pretreatment levels of distress and non-acceptance of emotional responses can impact providers' choice of commitment and treatment strategies to reduce dropout. Future research should examine these strategies, as well as the impact of predictor variables on outcome and reasons for dropout.

  1. Prediction of the relative toxicity of environmental toxins as a function of behavioral and non-behavioral endpoints

    International Nuclear Information System (INIS)

    Young, R.W.

    1979-01-01

    This study was conducted in order to examine the differential effects of behavioral and non-behavioral endpoints on the prediction of the relative toxicity of an environmental toxin. The effects of ionizing radiation were taken as the model for this evaluation. Forty rhesus monkeys were irradiated in groups of four at five different dose levels of high energy neuton and Bremsstrahlung radiations. Measures of behavioral performance, emesis and mortality were taken for each subject in order to test the hypotheses that behavioral indices would be more sensitive to gamma radiation than would physiological indices and that the physiological indices would be more sensitive to neutron radiations than would behavioral indices. The results supported these hypotheses

  2. Using implicit attitudes of exercise importance to predict explicit exercise dependence symptoms and exercise behaviors.

    Science.gov (United States)

    Forrest, Lauren N; Smith, April R; Fussner, Lauren M; Dodd, Dorian R; Clerkin, Elise M

    2016-01-01

    "Fast" (i.e., implicit) processing is relatively automatic; "slow" (i.e., explicit) processing is relatively controlled and can override automatic processing. These different processing types often produce different responses that uniquely predict behaviors. In the present study, we tested if explicit, self-reported symptoms of exercise dependence and an implicit association of exercise as important predicted exercise behaviors and change in problematic exercise attitudes. We assessed implicit attitudes of exercise importance and self-reported symptoms of exercise dependence at Time 1. Participants reported daily exercise behaviors for approximately one month, and then completed a Time 2 assessment of self-reported exercise dependence symptoms. Undergraduate males and females (Time 1, N = 93; Time 2, N = 74) tracked daily exercise behaviors for one month and completed an Implicit Association Test assessing implicit exercise importance and subscales of the Exercise Dependence Questionnaire (EDQ) assessing exercise dependence symptoms. Implicit attitudes of exercise importance and Time 1 EDQ scores predicted Time 2 EDQ scores. Further, implicit exercise importance and Time 1 EDQ scores predicted daily exercise intensity while Time 1 EDQ scores predicted the amount of days exercised. Implicit and explicit processing appear to uniquely predict exercise behaviors and attitudes. Given that different implicit and explicit processes may drive certain exercise factors (e.g., intensity and frequency, respectively), these behaviors may contribute to different aspects of exercise dependence.

  3. Transient fuel rod behavior prediction with RODEX-3/SIERRA

    Energy Technology Data Exchange (ETDEWEB)

    Billaux, M R; Shann, S H; Swam, L.F. Van [Siemens Power Corp., Richland, WA (United States)

    1997-08-01

    This paper discusses some aspects of the fuel performance code SIERRA (SIEmens Rod Response Analysis). SIERRA, the latest version of the code RODEX-3, has been developed to improve the fuel performance prediction capabilities of the code, both at high burnup and during transient reactor conditions. The paper emphasizes the importance of the mechanical models of the cracked pellet and of the cladding, in the prediction of the transient response of the fuel rod to power changes. These models are discussed in detail. Other aspects of the modelling of high burnup effects are also presented, in particular the modelling of the rim effect and the way it affects the fuel temperature. (author). 12 refs, 5 figs.

  4. Transient fuel rod behavior prediction with RODEX-3/SIERRA

    International Nuclear Information System (INIS)

    Billaux, M.R.; Shann, S.H.; Swam, L.F. Van

    1997-01-01

    This paper discusses some aspects of the fuel performance code SIERRA (SIEmens Rod Response Analysis). SIERRA, the latest version of the code RODEX-3, has been developed to improve the fuel performance prediction capabilities of the code, both at high burnup and during transient reactor conditions. The paper emphasizes the importance of the mechanical models of the cracked pellet and of the cladding, in the prediction of the transient response of the fuel rod to power changes. These models are discussed in detail. Other aspects of the modelling of high burnup effects are also presented, in particular the modelling of the rim effect and the way it affects the fuel temperature. (author). 12 refs, 5 figs

  5. A display to support knowledge based behavior

    International Nuclear Information System (INIS)

    Lindsay, R.W.

    1990-01-01

    A computerized display has been created for the Experimental Breeder Reactor II (EBR-II) that incorporates information from plant sensors in a thermodynamic model display. The display is designed to provide an operator with an overall view of the plant process as a heat engine. The thermodynamics of the plant are depicted through the use of ionic figures, animated by plant signals, that are related to the major plant components and systems such as the reactor, intermediate heat exchanger, secondary system, evaporators, superheaters, steam system, steam drum, and turbine-generator. This display supports knowledge based reasoning for the operator as well as providing the traditional rule and skill based behavior, and includes side benefits such a inherent signal validation

  6. A display to support knowledge based behavior

    International Nuclear Information System (INIS)

    Lindsay, R.W.

    1990-01-01

    This paper reports on a computerized display that has been created for the Experimental Breeder Reactor II that incorporates information from plant sensors in a thermodynamic model display. The display is designed to provide an operator with an overall view of the plant process as a heat engine. The thermodynamics of the plant are depicted through the use of iconic figures, animated by plant signals, that are related to the major plant components and systems such as the reactor, intermediate heat exchanger, secondary system, evaporators, superheaters, steam system, steam drum, and turbine-generator. This display supports knowledge based reasoning for the operator as well as providing data for the traditional rule and skill based behavior, and includes side benefits such as inherent signal validation

  7. Protein structure based prediction of catalytic residues.

    Science.gov (United States)

    Fajardo, J Eduardo; Fiser, Andras

    2013-02-22

    Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation. We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods. We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases.

  8. Interactions of Team Mental Models and Monitoring Behaviors Predict Team Performance in Simulated Anesthesia Inductions

    Science.gov (United States)

    Burtscher, Michael J.; Kolbe, Michaela; Wacker, Johannes; Manser, Tanja

    2011-01-01

    In the present study, we investigated how two team mental model properties (similarity vs. accuracy) and two forms of monitoring behavior (team vs. systems) interacted to predict team performance in anesthesia. In particular, we were interested in whether the relationship between monitoring behavior and team performance was moderated by team…

  9. Burrowing Behavior of a Deposit Feeding Bivalve Predicts Change in Intertidal Ecosystem State

    NARCIS (Netherlands)

    Compton, T.J.; Bodnar, W.; Koolhaas, A.; Dekinga, A.; Holthuijsen, S.; Ten Horn, J.; McSweeney, N.; van Gils, J.A.; Piersma, T.

    2016-01-01

    Behavior has a predictive power that is often underutilized as a tool for signaling ecological change. The burrowing behavior of the deposit feeding bivalve Macoma balthica reflects a typical food-safety trade-off. The choice to live close to the sediment surface comes at a risk of predation and is

  10. Burrowing behavior of a deposit feeding bivalve predicts change in intertidal ecosystem state

    NARCIS (Netherlands)

    Compton, Tanya J.; Bodnar, Wanda; Koolhaas, Anita; Dekinga, Anne; Holthuijsen, Sander; ten Horn, Job; McSweeney, Niamh; van Gils, Jan; Piersma, Theunis

    2016-01-01

    Behavior has a predictive power that is often underutilized as a tool for signaling ecological change. The burrowing behavior of the deposit feeding bivalve Macoma balthica reflects a typical food-safety trade-off. The choice to live close to the sediment surface comes at a risk of predation and is

  11. Using the Theory of Planned Behavior to Predict HPV Vaccination Intentions of College Men

    Science.gov (United States)

    Catalano, Hannah Priest; Knowlden, Adam P.; Birch, David A.; Leeper, James D.; Paschal, Angelia M.; Usdan, Stuart L.

    2017-01-01

    Objective: The purpose of this study was to test Theory of Planned Behavior (TPB) constructs in predicting human papillomavirus (HPV) vaccination behavioral intentions of vaccine-eligible college men. Participants: Participants were unvaccinated college men aged 18-26 years attending a large public university in the southeastern United States…

  12. Behavioral inhibition in childhood predicts smaller hippocampal volume in adolescent offspring of parents with panic disorder

    Science.gov (United States)

    Schwartz, C E; Kunwar, P S; Hirshfeld-Becker, D R; Henin, A; Vangel, M G; Rauch, S L; Biederman, J; Rosenbaum, J F

    2015-01-01

    Behavioral inhibition (BI) is a genetically influenced behavioral profile seen in 15–20% of 2-year-old children. Children with BI are timid with people, objects and situations that are novel or unfamiliar, and are more reactive physiologically to these challenges as evidenced by higher heart rate, pupillary dilation, vocal cord tension and higher levels of cortisol. BI predisposes to the later development of anxiety, depression and substance abuse. Reduced hippocampal volumes have been observed in anxiety disorders, depression and posttraumatic stress disorder. Animal models have demonstrated that chronic stress can damage the hippocampal formation and implicated cortisol in these effects. We, therefore, hypothesized that the hippocampi of late adolescents who had been behaviorally inhibited as children would be smaller compared with those who had not been inhibited. Hippocampal volume was measured with high-resolution structural magnetic resonance imaging in 43 females and 40 males at 17 years of age who were determined to be BI+ or BI− based on behaviors observed in the laboratory as young children. BI in childhood predicted reduced hippocampal volumes in the adolescents who were offspring of parents with panic disorder, or panic disorder with comorbid major depression. We discuss genetic and environmental factors emanating from both child and parent that may explain these findings. To the best of our knowledge, this is the first study to demonstrate a relationship between the most extensively studied form of temperamentally based human trait anxiety, BI, and hippocampal structure. The reduction in hippocampal volume, as reported by us, suggests a role for the hippocampus in human trait anxiety and anxiety disorder that warrants further investigation. PMID:26196438

  13. Effectiveness of link prediction for face-to-face behavioral networks.

    Science.gov (United States)

    Tsugawa, Sho; Ohsaki, Hiroyuki

    2013-01-01

    Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people. However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been explored in depth. To clarify this point, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the history of face-to-face interactions among participants at an academic conference. Our findings were (1) that conventional link prediction techniques predict new link formation with a precision of 0.30-0.45 and a recall of 0.10-0.20, (2) that prolonged observation of social networks often degrades the prediction accuracy, (3) that the proposed decaying weight method leads to higher prediction accuracy than can be achieved by observing all records of communication and simply using them unmodified, and (4) that the prediction accuracy for face-to-face behavioral networks is relatively high compared to that for non-social networks, but not as high as for other types of social networks.

  14. Lack of supportive leadership behavior predicts suboptimal self-rated health independent of job strain after 10 years of follow-up: findings from the population-based MONICA/KORA study.

    Science.gov (United States)

    Schmidt, Burkhard; Herr, Raphael M; Jarczok, Marc N; Baumert, Jens; Lukaschek, Karoline; Emeny, Rebecca T; Ladwig, Karl-Heinz

    2018-04-23

    Emerging cross-sectional research has identified lack of supportive leadership behavior (SLB) as a risk factor for workforce health. However, prospective evidence is hitherto lacking. SLB denotes support in difficult situations, recognition and feedback on work tasks. This study aims to determine the effect of SLB on suboptimal self-rated health (SRH) after 10 years considering potential moderators such as ages, sex, occupation and job strain. The sample included 884 employed participants drawn from the population-based prospective MONICA/KORA Study. SLB, SRH, as well as job strain were assessed by questionnaire. Logistic regressions estimated odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for the effect of SLB at baseline on suboptimal SRH at follow-up. Analyses were adjusted for age, gender, lifestyle (alcohol, smoking, physical activity), socioeconomic status as well as for SRH and job strain at baseline. Lack of SLB was associated with suboptimal SRH at baseline [OR 2.00, (95% CI 1.19-3.46)] and at follow-up [OR 2.33, (95% CI 1.40-3.89)]. Additional adjustment for job strain did not substantially alter this association [OR 2.06, (95% CI 1.20-3.52)]. However, interactions between SLB and job strain as well as gender became evident, indicating moderating influences on the association between SLB and SRH. Lack of supportive leadership was associated with suboptimal SRH at 10 years' follow-up in men, even if SRH at baseline and other risk factors were taken into account. This effect is likely to be moderated by job strain.

  15. Social support for healthy behaviors: Scale psychometrics and prediction of weight loss among women in a behavioral program

    Science.gov (United States)

    Kiernan, Michaela; Moore, Susan D.; Schoffman, Danielle E.; Lee, Katherine; King, Abby C.; Taylor, C. Barr; Kiernan, Nancy Ellen; Perri, Michael G.

    2015-01-01

    Social support could be a powerful weight-loss treatment moderator or mediator but is rarely assessed. We assessed the psychometric properties, initial levels, and predictive validity of a measure of perceived social support and sabotage from friends and family for healthy eating and physical activity (eight subscales). Overweight/obese women randomized to one of two 6-month, group-based behavioral weight-loss programs (N=267; mean BMI 32.1±3.5; 66.3% White) completed subscales at baseline, and weight loss was assessed at 6 months. Internal consistency, discriminant validity, and content validity were excellent for support subscales and adequate for sabotage subscales; qualitative responses revealed novel deliberate instances not reflected in current sabotage items. Most women (>75%) “never” or “rarely” experienced support from friends or family. Using non-parametric classification methods, we identified two subscales—support from friends for healthy eating and support from family for physical activity—that predicted three clinically meaningful subgroups who ranged in likelihood of losing ≥5% of initial weight at 6 months. Women who “never” experienced family support were least likely to lose weight (45.7% lost weight) whereas women who experienced both frequent friend and family support were more likely to lose weight (71.6% lost weight). Paradoxically, women who “never” experienced friend support were most likely to lose weight (80.0% lost weight), perhaps because the group-based programs provided support lacking from friendships. Psychometrics for support subscales were excellent; initial support was rare; and the differential roles of friend versus family support could inform future targeted weight-loss interventions to subgroups at risk. PMID:21996661

  16. Predicting fruit consumption: the role of habits, previous behavior and mediation effects

    NARCIS (Netherlands)

    de Vries, H.; Eggers, S.M.; Lechner, L.; van Osch, L.; van Stralen, M.M.

    2014-01-01

    Background: This study assessed the role of habits and previous behavior in predicting fruit consumption as well as their additional predictive contribution besides socio-demographic and motivational factors. In the literature, habits are proposed as a stable construct that needs to be controlled

  17. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  18. Private traits and attributes are predictable from digital records of human behavior.

    Science.gov (United States)

    Kosinski, Michal; Stillwell, David; Graepel, Thore

    2013-04-09

    We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.

  19. Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions

    Science.gov (United States)

    Nandola, Naresh N.; Rivera, Daniel E.

    2011-01-01

    This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087

  20. Prediction of rat behavior outcomes in memory tasks using functional connections among neurons.

    Directory of Open Access Journals (Sweden)

    Hu Lu

    Full Text Available BACKGROUND: Analyzing the neuronal organizational structures and studying the changes in the behavior of the organism is key to understanding cognitive functions of the brain. Although some studies have indicated that spatiotemporal firing patterns of neuronal populations have a certain relationship with the behavioral responses, the issues of whether there are any relationships between the functional networks comprised of these cortical neurons and behavioral tasks and whether it is possible to take advantage of these networks to predict correct and incorrect outcomes of single trials of animals are still unresolved. METHODOLOGY/PRINCIPAL FINDINGS: This paper presents a new method of analyzing the structures of whole-recorded neuronal functional networks (WNFNs and local neuronal circuit groups (LNCGs. The activity of these neurons was recorded in several rats. The rats performed two different behavioral tasks, the Y-maze task and the U-maze task. Using the results of the assessment of the WNFNs and LNCGs, this paper describes a realization procedure for predicting the behavioral outcomes of single trials. The methodology consists of four main parts: construction of WNFNs from recorded neuronal spike trains, partitioning the WNFNs into the optimal LNCGs using social community analysis, unsupervised clustering of all trials from each dataset into two different clusters, and predicting the behavioral outcomes of single trials. The results show that WNFNs and LNCGs correlate with the behavior of the animal. The U-maze datasets show higher accuracy for unsupervised clustering results than those from the Y-maze task, and these datasets can be used to predict behavioral responses effectively. CONCLUSIONS/SIGNIFICANCE: The results of the present study suggest that a methodology proposed in this paper is suitable for analysis of the characteristics of neuronal functional networks and the prediction of rat behavior. These types of structures in cortical

  1. Environment, behavior and physiology: do birds use barometric pressure to predict storms?

    Science.gov (United States)

    Breuner, Creagh W; Sprague, Rachel S; Patterson, Stephen H; Woods, H Arthur

    2013-06-01

    Severe storms can pose a grave challenge to the temperature and energy homeostasis of small endothermic vertebrates. Storms are accompanied by lower temperatures and wind, increasing metabolic expenditure, and can inhibit foraging, thereby limiting energy intake. To avoid these potential problems, most endotherms have mechanisms for offsetting the energetic risks posed by storms. One possibility is to use cues to predict oncoming storms and to alter physiology and behavior in ways that make survival more likely. Barometric pressure declines predictably before inclement weather, and several lines of evidence indicate that animals alter behavior based on changes in ambient pressure. Here we examined the effects of declining barometric pressure on physiology and behavior in the white-crowned sparrow, Zonotrichia leucophrys. Using field data from a long-term study, we first evaluated the relationship between barometric pressure, storms and stress physiology in free-living white-crowned sparrows. We then manipulated barometric pressure experimentally in the laboratory and determined how it affects activity, food intake, metabolic rates and stress physiology. The field data showed declining barometric pressure in the 12-24 h preceding snowstorms, but we found no relationship between barometric pressure and stress physiology. The laboratory study showed that declining barometric pressure stimulated food intake, but had no effect on metabolic rate or stress physiology. These data suggest that white-crowned sparrows can sense and respond to declining barometric pressure, and we propose that such an ability may be common in wild vertebrates, especially small ones for whom individual storms can be life-threatening events.

  2. Infant Sleep Predicts Attention Regulation and Behavior Problems at 3-4 Years of Age.

    Science.gov (United States)

    Sadeh, Avi; De Marcas, Gali; Guri, Yael; Berger, Andrea; Tikotzky, Liat; Bar-Haim, Yair

    2015-01-01

    This longitudinal study assessed the role of early sleep patterns in predicting attention regulation and behavior problems. Sleep of 43 infants was assessed using actigraphy at 12 months of age and then reassessed when the children were 3-4 years old. During this follow-up, their attention regulation and behavior problems were also assessed using a computerized test and parental reports. Lower quality of sleep in infancy significantly predicted compromised attention regulation and behavior problems. These findings underscore the need to identify and treat early sleep problems.

  3. Predicting Driver Behavior during the Yellow Interval Using Video Surveillance

    Directory of Open Access Journals (Sweden)

    Juan Li

    2016-12-01

    Full Text Available At a signalized intersection, drivers must make a stop/go decision at the onset of the yellow signal. Incorrect decisions would lead to red light running (RLR violations or crashes. This study aims to predict drivers’ stop/go decisions and RLR violations during yellow intervals. Traffic data such as vehicle approaching speed, acceleration, distance to the intersection, and occurrence of RLR violations are gathered by a Vehicle Data Collection System (VDCS. An enhanced Gaussian Mixture Model (GMM is used to extract moving vehicles from target lanes, and the Kalman Filter (KF algorithm is utilized to acquire vehicle trajectories. The data collected from the VDCS are further analyzed by a sequential logit model, and the relationship between drivers’ stop/go decisions and RLR violations is identified. The results indicate that the distance of vehicles to the stop line at the onset of the yellow signal is an important predictor for both drivers’ stop/go decisions and RLR violations. In addition, vehicle approaching speed is a contributing factor for stop/go decisions. Furthermore, the accelerations of vehicles after the onset of the yellow signal are positively related to RLR violations. The findings of this study can be used to predict the probability of drivers’ RLR violations and improve traffic safety at signalized intersections.

  4. In-Session Caregiver Behavior Predicts Symptom Change in Youth Receiving Trauma-Focused Cognitive Behavioral Therapy (TF-CBT)

    Science.gov (United States)

    Yasinski, Carly; Hayes, Adele; Ready, C. Beth; Cummings, Jorden A.; Berman, Ilana S.; McCauley, Thomas; Webb, Charles; Deblinger, Esther

    2016-01-01

    Objective Involving caregivers in trauma-focused treatments for youth has been shown to result in better outcomes, but it is not clear which in-session caregiver behaviors enhance or inhibit this effect. The current study examined the associations between caregiver behaviors during Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) and youth cognitive processes and symptoms. Method Participants were a racially diverse sample of Medicaid-eligible youth (ages 7–17) and their non-offending caregivers (N= 71 pairs) who received TF-CBT through an effectiveness study in a community setting. Caregiver and youth processes were coded from audio-recorded sessions, and outcomes were measured using the Child Behavior Checklist (CBCL) and UCLA PTSD Reaction Index for DSM-IV (UPID) at 3, 6, 9, and 12 months post-intake. Results Piecewise linear growth curve modeling revealed that during the trauma narrative phase of TF-CBT, caregivers’ cognitive-emotional processing of their own and their child's trauma-related reactions predicted decreases in youth internalizing and externalizing symptoms over treatment. Caregiver support predicted lower internalizing symptoms over follow-up. In contrast, caregiver avoidance and blame of the child predicted worsening of youth internalizing and externalizing symptoms over follow-up. Caregiver avoidance early in treatment also predicted worsening of externalizing symptoms over follow-up. During the narrative phase, caregiver blame and avoidance were correlated with more child overgeneralization of trauma beliefs, and blame was also associated with less child accommodation of balanced beliefs. Conclusions The association between in-session caregiver behaviors and youth symptomatology during and after TF-CBT highlights the importance of assessing and targeting these behaviors to improve clinical outcomes. PMID:27618641

  5. In-session caregiver behavior predicts symptom change in youth receiving trauma-focused cognitive behavioral therapy (TF-CBT).

    Science.gov (United States)

    Yasinski, Carly; Hayes, Adele M; Ready, C Beth; Cummings, Jorden A; Berman, Ilana S; McCauley, Thomas; Webb, Charles; Deblinger, Esther

    2016-12-01

    Involving caregivers in trauma-focused treatments for youth has been shown to result in better outcomes, but it is not clear which in-session caregiver behaviors enhance or inhibit this effect. The current study examined the associations between caregiver behaviors during Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) and youth cognitive processes and symptoms. Participants were a racially diverse sample of Medicaid-eligible youth (ages 7-17) and their nonoffending caregivers (N = 71 pairs) who received TF-CBT through an effectiveness study in a community setting. Caregiver and youth processes were coded from audio-recorded sessions, and outcomes were measured using the Child Behavior Checklist (CBCL) and UCLA PTSD Reaction Index for Diagnostic and Statistical Manual for Mental Disorders-Fourth Edition (DSM-IV; UPID) at 3, 6, 9, and 12 months postintake. Piecewise linear growth curve modeling revealed that during the trauma narrative phase of TF-CBT, caregivers' cognitive-emotional processing of their own and their child's trauma-related reactions predicted decreases in youth internalizing and externalizing symptoms over treatment. Caregiver support predicted lower internalizing symptoms over follow-up. In contrast, caregiver avoidance and blame of the child predicted worsening of youth internalizing and externalizing symptoms over follow-up. Caregiver avoidance early in treatment also predicted worsening of externalizing symptoms over follow-up. During the narrative phase, caregiver blame and avoidance were correlated with more child overgeneralization of trauma beliefs, and blame was also associated with less child accommodation of balanced beliefs. The association between in-session caregiver behaviors and youth symptomatology during and after TF-CBT highlights the importance of assessing and targeting these behaviors to improve clinical outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  6. Brief implicit association test: Validity and utility in prediction of voting behavior

    Directory of Open Access Journals (Sweden)

    Pavlović Maša D.

    2013-01-01

    Full Text Available We employed the Brief Implicit Association Test (a recently developed short version of IAT to measure implicit political attitudes toward four political parties running for Serbian parliament. To test its criterion validity, we measured voting intention and actual voting behavior. In addition, we introduced political involvement as a potential moderator of the BIAT’s predictive and incremental validity. The BIAT demonstrated good internal and predictive validity, but lacked incremental validity over self-report measures. Predictive power of the BIAT was moderated by political involvement - the BIAT scores were stronger predictors of voting intention and behavior among voters highly involved in politics. [Projekat Ministarstva nauke Republike Srbije, br. 179018

  7. Using the Theory of Planned Behavior to Explain and Predict Behavior Intentions in Taiwan

    Science.gov (United States)

    Wu, Cheng-Lung

    2015-01-01

    This study aims to use the theory of planned behavior to verify undergraduates' behavioral intentions regarding their participation in aquatic sports. Undergraduates in Taiwan serve as the research subjects and a survey method employs questionnaires. A total of 200 valid questionnaires were received out of 230, thus giving a valid response rate of…

  8. Does Preschool Self-Regulation Predict Later Behavior Problems in General or Specific Problem Behaviors?

    Science.gov (United States)

    Lonigan, Christopher J; Spiegel, Jamie A; Goodrich, J Marc; Morris, Brittany M; Osborne, Colleen M; Lerner, Matthew D; Phillips, Beth M

    2017-11-01

    Findings from prior research have consistently indicated significant associations between self-regulation and externalizing behaviors. Significant associations have also been reported between children's language skills and both externalizing behaviors and self-regulation. Few studies to date, however, have examined these relations longitudinally, simultaneously, or with respect to unique clusters of externalizing problems. The current study examined the influence of preschool self-regulation on general and specific externalizing behavior problems in early elementary school and whether these relations were independent of associations between language, self-regulation, and externalizing behaviors in a sample of 815 children (44% female). Additionally, given a general pattern of sex differences in the presentations of externalizing behavior problems, self-regulation, and language skills, sex differences for these associations were examined. Results indicated unique relations of preschool self-regulation and language with both general externalizing behavior problems and specific problems of inattention. In general, self-regulation was a stronger longitudinal correlate of externalizing behavior for boys than it was for girls, and language was a stronger longitudinal predictor of hyperactive/impulsive behavior for girls than it was for boys.

  9. Using the Personality Assessment Inventory Antisocial and Borderline Features Scales to Predict Behavior Change.

    Science.gov (United States)

    Penson, Brittany N; Ruchensky, Jared R; Morey, Leslie C; Edens, John F

    2016-11-01

    A substantial amount of research has examined the developmental trajectory of antisocial behavior and, in particular, the relationship between antisocial behavior and maladaptive personality traits. However, research typically has not controlled for previous behavior (e.g., past violence) when examining the utility of personality measures, such as self-report scales of antisocial and borderline traits, in predicting future behavior (e.g., subsequent violence). Examination of the potential interactive effects of measures of both antisocial and borderline traits also is relatively rare in longitudinal research predicting adverse outcomes. The current study utilizes a large sample of youthful offenders ( N = 1,354) from the Pathways to Desistance project to examine the separate effects of the Personality Assessment Inventory Antisocial Features (ANT) and Borderline Features (BOR) scales in predicting future offending behavior as well as trends in other negative outcomes (e.g., substance abuse, violence, employment difficulties) over a 1-year follow-up period. In addition, an ANT × BOR interaction term was created to explore the predictive effects of secondary psychopathy. ANT and BOR both explained unique variance in the prediction of various negative outcomes even after controlling for past indicators of those same behaviors during the preceding year.

  10. Prediction-based dynamic load-sharing heuristics

    Science.gov (United States)

    Goswami, Kumar K.; Devarakonda, Murthy; Iyer, Ravishankar K.

    1993-01-01

    The authors present dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30 percent better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50 percent improvements relative to their nonprediction counterpart.

  11. Predicting consumer behavior: using novel mind-reading approaches.

    Science.gov (United States)

    Calvert, Gemma A; Brammer, Michael J

    2012-01-01

    Advances in machine learning as applied to functional magnetic resonance imaging (fMRI) data offer the possibility of pretesting and classifying marketing communications using unbiased pattern recognition algorithms. By using these algorithms to analyze brain responses to brands, products, or existing marketing communications that either failed or succeeded in the marketplace and identifying the patterns of brain activity that characterize success or failure, future planned campaigns or new products can now be pretested to determine how well the resulting brain responses match the desired (successful) pattern of brain activity without the need for verbal feedback. This major advance in signal processing is poised to revolutionize the application of these brain-imaging techniques in the marketing sector by offering greater accuracy of prediction in terms of consumer acceptance of new brands, products, and campaigns at a speed that makes them accessible as routine pretesting tools that will clearly demonstrate return on investment.

  12. Predicting the Retention Behavior of Specific O-Linked Glycopeptides.

    Science.gov (United States)

    Badgett, Majors J; Boyes, Barry; Orlando, Ron

    2017-09-01

    O -Linked glycosylation is a common post-translational modification that can alter the overall structure, polarity, and function of proteins. Reverse-phase (RP) chromatography is the most common chromatographic approach to analyze O -glycosylated peptides and their unmodified counterparts, even though this approach often does not provide adequate separation of these two species. Hydrophilic interaction liquid chromatography (HILIC) can be a solution to this problem, as the polar glycan interacts with the polar stationary phase and potentially offers the ability to resolve the peptide from its modified form(s). In this paper, HILIC is used to separate peptides with O - N -acetylgalactosamine ( O -GalNAc), O - N -acetylglucosamine ( O -GlcNAc), and O -fucose additions from their native forms, and coefficients representing the extent of hydrophilicity were derived using linear regression analysis as a means to predict the retention times of peptides with these modifications.

  13. Contemporaneous and 1-year longitudinal prediction of children's prosocial behavior from sympathy and moral motivation.

    Science.gov (United States)

    Malti, Tina; Gummerum, Michaela; Buchmann, Marlis

    2007-09-01

    The authors investigated the contemporaneous and longitudinal relations of children's (M age = 6.4 years) prosocial behavior to sympathy and moral motivation. Mothers and kindergarten teachers rated children's prosocial behavior. The authors measured sympathy via self- and adult reports. Moral motivation was assessed by children's attribution of emotions to hypothetical victimizers and self-as-victimizers and by moral reasoning after rule violations. Mother-rated prosocial behavior was contemporaneously and longitudinally related to sympathy. Moral motivation moderated the relation of sympathy to mother-rated prosocial behavior. Furthermore, boys' level of mother-rated prosocial behavior increased with level of moral motivation, whereas girls were high in mother-rated prosocial behavior, regardless of their level of moral motivation. Sympathy contemporaneously predicted kindergarten teacher-rated prosocial behavior.

  14. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  15. Predicting violent behavior: The role of violence exposure and future educational aspirations during adolescence.

    Science.gov (United States)

    Stoddard, Sarah A; Heinze, Justin E; Choe, Daniel Ewon; Zimmerman, Marc A

    2015-10-01

    Few researchers have explored future educational aspirations as a promotive factor against exposure to community violence in relation to adolescents' violent behavior over time. The present study examined the direct and indirect effect of exposure to community violence prior to 9th grade on attitudes about violence and violent behavior in 12th grade, and violent behavior at age 22 via 9th grade future educational aspirations in a sample of urban African American youth (n = 681; 49% male). Multi-group SEM was used to test the moderating effect of gender. Exposure to violence was associated with lower future educational aspirations. For boys, attitudes about violence directly predicted violent behavior at age 22. For boys, future educational aspirations indirectly predicted less violent behavior at age 22. Implications of the findings and suggestions for future research are discussed. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  16. Theoretical bases analysis of scientific prediction on marketing principles

    OpenAIRE

    A.S. Rosohata

    2012-01-01

    The article presents an overview categorical apparatus of scientific predictions and theoretical foundations results of scientific forecasting. They are integral part of effective management of economic activities. The approaches to the prediction of scientists in different fields of Social science and the categories modification of scientific prediction, based on principles of marketing are proposed.

  17. Non-Fourier based thermal-mechanical tissue damage prediction for thermal ablation.

    Science.gov (United States)

    Li, Xin; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

    2017-01-02

    Prediction of tissue damage under thermal loads plays important role for thermal ablation planning. A new methodology is presented in this paper by combing non-Fourier bio-heat transfer, constitutive elastic mechanics as well as non-rigid motion of dynamics to predict and analyze thermal distribution, thermal-induced mechanical deformation and thermal-mechanical damage of soft tissues under thermal loads. Simulations and comparison analysis demonstrate that the proposed methodology based on the non-Fourier bio-heat transfer can account for the thermal-induced mechanical behaviors of soft tissues and predict tissue thermal damage more accurately than classical Fourier bio-heat transfer based model.

  18. Extending the Theory of Normative Social Behavior to Predict Hand-Washing among Koreans.

    Science.gov (United States)

    Chung, Minwoong; Lapinski, Maria Knight

    2018-04-10

    The current study tests the predictions of the theory of normative social behavior (TNSB) in a hand-washing context in a Korean sample and extends the theory to examine the role of perceived publicness, a variable believed to activate face concerns, as a moderator of the norm-behavior relationship. The findings show substantial main effects for all of the study variables on behavior. In addition, the descriptive norm-behavior relationship is moderated by perceived publicness and outcome expectations, but the nature of the interactions is not consistent with that evidenced in previous literature on US samples. Implications for normative theory and communication campaigns are discussed.

  19. Health Belief Model and Reasoned Action Theory in Predicting Water Saving Behaviors in Yazd, Iran

    Directory of Open Access Journals (Sweden)

    Mohammad Taghi Ghaneian

    2012-12-01

    Full Text Available Background: People's behaviors and intentions about healthy behaviors depend on their beliefs, values, and knowledge about the issue. Various models of health education are used in deter-mining predictors of different healthy behaviors but their efficacy in cultural behaviors, such as water saving behaviors, are not studied. The study was conducted to explain water saving beha-viors in Yazd, Iran on the basis of Health Belief Model and Reasoned Action Theory. Methods: The cross-sectional study used random cluster sampling to recruit 200 heads of households to collect the data. The survey questionnaire was tested for its content validity and reliability. Analysis of data included descriptive statistics, simple correlation, hierarchical multiple regression. Results: Simple correlations between water saving behaviors and Reasoned Action Theory and Health Belief Model constructs were statistically significant. Health Belief Model and Reasoned Action Theory constructs explained 20.80% and 8.40% of the variances in water saving beha-viors, respectively. Perceived barriers were the strongest Predictor. Additionally, there was a sta-tistically positive correlation between water saving behaviors and intention. Conclusion: In designing interventions aimed at water waste prevention, barriers of water saving behaviors should be addressed first, followed by people's attitude towards water saving. Health Belief Model constructs, with the exception of perceived severity and benefits, is more powerful than is Reasoned Action Theory in predicting water saving behavior and may be used as a framework for educational interventions aimed at improving water saving behaviors.

  20. Parental Behaviors during Family Interactions Predict Changes in Depression and Anxiety Symptoms during Adolescence

    Science.gov (United States)

    Schwartz, Orli S.; Dudgeon, Paul; Sheeber, Lisa B.; Yap, Marie B. H.; Simmons, Julian G.; Allen, Nicholas B.

    2012-01-01

    This study investigated the prospective, longitudinal relations between parental behaviors observed during parent-adolescent interactions, and the development of depression and anxiety symptoms in a community-based sample of 194 adolescents. Positive and negative parental behaviors were examined, with negative behaviors operationalized to…

  1. Predicting Participation in Dual Language Immersion Using Theory of Planned Behavior

    Science.gov (United States)

    Call, Andrea; Domenech Rodríguez, Melanie M.; Vázquez, Alejandro L.; Corralejo, Samantha M.

    2018-01-01

    Dual language immersion programs are increasing in popularity. Yet little is known about what motivates parents to enroll their children in dual language immersion. The theory of planned behavior posits that behavior is based on attitudes, subjective norms, and perceived behavioral control. The current study was an exploratory evaluation of the…

  2. The Implications of Endoscopic Ulcer in Early Gastric Cancer: Can We Predict Clinical Behaviors from Endoscopy?

    Directory of Open Access Journals (Sweden)

    Yoo Jin Lee

    Full Text Available The presence of ulcer in early gastric cancer (EGC is important for the feasibility of endoscopic resection, only a few studies have examined the clinicopathological implications of endoscopic ulcer in EGC.To determine the role of endoscopic ulcer as a predictor of clinical behaviors in EGC.Data of 3,270 patients with EGC who underwent surgery between January 2005 and December 2012 were reviewed. Clinicopathological characteristics were analyzed in relation to the presence and stage of ulcer in EGC. Based on endoscopic findings, the stage of ulcer was categorized as active, healing, or scar. Logistic regression analysis was performed to analyze factors associated with lymph node metastasis (LNM.2,343 (71.7% patients had endoscopic findings of ulceration in EGC. Submucosal (SM invasion, LNM, lymphovascular invasion (LVI, perineural invasion, and undifferentiated-type histology were significantly higher in ulcerative than non-ulcerative EGC. Comparison across different stages of ulcer revealed that SM invasion, LNM, and LVI were significantly associated with the active stage, and that these features exhibited significant stage-based differences, being most common at the active stage, and least common at the scar stage. The presence of endoscopic ulcer and active status of the ulcer were identified as independent risk factors for LNM.Ulcerative EGC detected by endoscopy exhibited more aggressive behaviors than non-ulcerative EGC. Additionally, the endoscopic stage of ulcer may predict the clinicopathological behaviors of EGC. Therefore, the appearance of ulcers should be carefully evaluated to determine an adequate treatment strategy for EGC.

  3. Predicting High-School Students' Bystander Behavior in Simulated Dating Violence Situations.

    Science.gov (United States)

    Jouriles, Ernest N; Rosenfield, David; Yule, Kristen; Sargent, Kelli S; McDonald, Renee

    2016-03-01

    Dating violence among adolescents is associated with a variety of negative health consequences for victims. Bystander programs are being developed and implemented with the intention of preventing such violence, but determinants of high-school students' responsive bystander behavior remain unclear. The present study examines hypothesized determinants of high-school students' bystander behavior in simulated situations of dating violence. Participants were 80 high-school students who completed self-reports of hypothesized determinants of bystander behavior (responsibility, efficacy, and perceived benefits for intervening) at a baseline assessment. A virtual-reality paradigm was used to observationally assess bystander behavior at 1-week and 6-month assessments after baseline. Efficacy for intervening was positively associated with observed bystander behavior at the 1-week and 6-month assessments. Moreover, efficacy predicted bystander behavior over and above feelings of responsibility and perceived benefits for intervening. Contrary to our predictions, neither responsibility nor perceived benefits for intervening were associated with observed bystander behavior. This research advances our understanding of determinants of bystander behavior for high-school students and can inform prevention programming for adolescents. The study also introduces an innovative way to assess high-school students' bystander behavior. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  4. Discrepancy-based and anticipated emotions in behavioral self-regulation.

    Science.gov (United States)

    Brown, Christina M; McConnell, Allen R

    2011-10-01

    Discrepancies between one's current and desired states evoke negative emotions, which presumably guide self-regulation. In the current work we evaluated the function of discrepancy-based emotions in behavioral self-regulation. Contrary to classic theories of self-regulation, discrepancy-based emotions did not predict the degree to which people engaged in self-regulatory behavior. Instead, expectations about how future self-discrepancies would make one feel (i.e., anticipated emotions) predicted self-regulation. However, anticipated emotions were influenced by previous discrepancy-based emotional experiences, suggesting that the latter do not directly motivate self-regulation but rather guide expectations. These findings are consistent with the perspective that emotions do not necessarily direct immediate behavior, but rather have an indirect effect by guiding expectations, which in turn predict goal-directed action.

  5. Functional-Based Assessment of Social Behavior: Introduction and Overview.

    Science.gov (United States)

    Lewis, Timothy J.; Sugai, George

    1994-01-01

    This introduction to and overview of a special issue on social behavior assessment within schools discusses the impact of function-based methodologies on assessment and intervention practices in identification and remediation of challenging social behaviors. (JDD)

  6. AIDS, behavior, and culture: understanding evidence-based prevention

    National Research Council Canada - National Science Library

    Green, Edward C; Ruark, Allison Herling

    2011-01-01

    .... Arguing for a behavior-based approach, the authors make the case that the most effective programs are those that encourage fundamental behavioral changes such as faithfulness, avoidance of concurrent...

  7. Prediction-error of Prediction Error (PPE)-based Reversible Data Hiding

    OpenAIRE

    Wu, Han-Zhou; Wang, Hong-Xia; Shi, Yun-Qing

    2016-01-01

    This paper presents a novel reversible data hiding (RDH) algorithm for gray-scaled images, in which the prediction-error of prediction error (PPE) of a pixel is used to carry the secret data. In the proposed method, the pixels to be embedded are firstly predicted with their neighboring pixels to obtain the corresponding prediction errors (PEs). Then, by exploiting the PEs of the neighboring pixels, the prediction of the PEs of the pixels can be determined. And, a sorting technique based on th...

  8. Environmentally responsible behavior of nature-based tourists: A review

    Directory of Open Access Journals (Sweden)

    Lee, T.H.

    2013-03-01

    Full Text Available This study assesses the conceptualization of environmentally responsible behavior and methods for measuring such behavior based on a review of previous studies. Four major scales for the extent to which an individual’s behavior is responsible behavior are discussed. Various theoretical backgrounds and cultures provide diverse conceptualizations of environmentally responsible behavior. Both general and site-specific environmentally responsible behavior has been identified in the past studies. This study also discusses the precedents of environmentally responsible behavior and with a general overview; it provides insight into improving future research on this subject.

  9. Anxiety Sensitivity Uniquely Predicts Exercise Behaviors in Young Adults Seeking to Increase Physical Activity.

    Science.gov (United States)

    Moshier, Samantha J; Szuhany, Kristin L; Hearon, Bridget A; Smits, Jasper A J; Otto, Michael W

    2016-01-01

    Individuals with elevated levels of anxiety sensitivity (AS) may be motivated to avoid aversive emotional or physical states, and therefore may have greater difficulty achieving healthy behavioral change. This may be particularly true for exercise, which produces many of the somatic sensations within the domain of AS concerns. Cross-sectional studies show a negative association between AS and exercise. However, little is known about how AS may prospectively affect attempts at behavior change in individuals who are motivated to increase their exercise. We recruited 145 young adults who self-identified as having a desire to increase their exercise behavior. Participants completed a web survey assessing AS and additional variables identified as important for behavior change-impulsivity, grit, perceived behavioral control, and action planning-and set a specific goal for exercising in the next week. One week later, a second survey assessed participants' success in meeting their exercise goals. We hypothesized that individuals with higher AS would choose lower exercise goals and would complete less exercise at the second survey. AS was not significantly associated with exercise goal level, but significantly and negatively predicted exercise at Time 2 and was the only variable to offer significant prediction beyond consideration of baseline exercise levels. These results underscore the importance of considering AS in relation to health behavior intentions. This is particularly apt given the absence of prediction offered by other traditional predictors of behavior change. © The Author(s) 2015.

  10. A novel numerical model to predict the morphological behavior of magnetic liquid marbles using coarse grained molecular dynamics concepts

    Science.gov (United States)

    Polwaththe-Gallage, Hasitha-Nayanajith; Sauret, Emilie; Nguyen, Nam-Trung; Saha, Suvash C.; Gu, YuanTong

    2018-01-01

    Liquid marbles are liquid droplets coated with superhydrophobic powders whose morphology is governed by the gravitational and surface tension forces. Small liquid marbles take spherical shapes, while larger liquid marbles exhibit puddle shapes due to the dominance of gravitational forces. Liquid marbles coated with hydrophobic magnetic powders respond to an external magnetic field. This unique feature of magnetic liquid marbles is very attractive for digital microfluidics and drug delivery systems. Several experimental studies have reported the behavior of the liquid marbles. However, the complete behavior of liquid marbles under various environmental conditions is yet to be understood. Modeling techniques can be used to predict the properties and the behavior of the liquid marbles effectively and efficiently. A robust liquid marble model will inspire new experiments and provide new insights. This paper presents a novel numerical modeling technique to predict the morphology of magnetic liquid marbles based on coarse grained molecular dynamics concepts. The proposed model is employed to predict the changes in height of a magnetic liquid marble against its width and compared with the experimental data. The model predictions agree well with the experimental findings. Subsequently, the relationship between the morphology of a liquid marble with the properties of the liquid is investigated. Furthermore, the developed model is capable of simulating the reversible process of opening and closing of the magnetic liquid marble under the action of a magnetic force. The scaling analysis shows that the model predictions are consistent with the scaling laws. Finally, the proposed model is used to assess the compressibility of the liquid marbles. The proposed modeling approach has the potential to be a powerful tool to predict the behavior of magnetic liquid marbles serving as bioreactors.

  11. Health belief model and reasoned action theory in predicting water saving behaviors in yazd, iran.

    Science.gov (United States)

    Morowatisharifabad, Mohammad Ali; Momayyezi, Mahdieh; Ghaneian, Mohammad Taghi

    2012-01-01

    People's behaviors and intentions about healthy behaviors depend on their beliefs, values, and knowledge about the issue. Various models of health education are used in deter¬mining predictors of different healthy behaviors but their efficacy in cultural behaviors, such as water saving behaviors, are not studied. The study was conducted to explain water saving beha¬viors in Yazd, Iran on the basis of Health Belief Model and Reasoned Action Theory. The cross-sectional study used random cluster sampling to recruit 200 heads of households to collect the data. The survey questionnaire was tested for its content validity and reliability. Analysis of data included descriptive statistics, simple correlation, hierarchical multiple regression. Simple correlations between water saving behaviors and Reasoned Action Theory and Health Belief Model constructs were statistically significant. Health Belief Model and Reasoned Action Theory constructs explained 20.80% and 8.40% of the variances in water saving beha-viors, respectively. Perceived barriers were the strongest Predictor. Additionally, there was a sta¬tistically positive correlation between water saving behaviors and intention. In designing interventions aimed at water waste prevention, barriers of water saving behaviors should be addressed first, followed by people's attitude towards water saving. Health Belief Model constructs, with the exception of perceived severity and benefits, is more powerful than is Reasoned Action Theory in predicting water saving behavior and may be used as a framework for educational interventions aimed at improving water saving behaviors.

  12. The interaction between self-regulation and motivation prospectively predicting problem behavior in adolescence.

    Science.gov (United States)

    Rhodes, Jessica D; Colder, Craig R; Trucco, Elisa M; Speidel, Carolyn; Hawk, Larry W; Lengua, Liliana J; Das Eiden, Rina; Wieczorek, William

    2013-01-01

    A large literature suggests associations between self-regulation and motivation and adolescent problem behavior; however, this research has mostly pitted these constructs against one another or tested them in isolation. Following recent neural-systems based theories (e.g., Ernst & Fudge, 2009 ), the present study investigated the interactions between self-regulation and approach and avoidance motivation prospectively predicting delinquency and depressive symptoms in early adolescence. The community sample included 387 adolescents aged 11 to 13 years old (55% female; 17% minority). Laboratory tasks were used to assess self-regulation and approach and avoidance motivation, and adolescent self-reports were used to measure depressive symptoms and delinquency. Analyses suggested that low levels of approach motivation were associated with high levels of depressive symptoms, but only at high levels of self-regulation (p = .01). High levels of approach were associated with high levels of rule breaking, but only at low levels of self-regulation (p theories that posit integration of motivational and self-regulatory individual differences via moderational models to understand adolescent problem behavior.

  13. Applying the Theory of Planned Behavior in Predicting Proenvironmental Behaviour: The Case of Energy Conservation

    Directory of Open Access Journals (Sweden)

    Octav-Ionuţ Macovei

    2015-08-01

    Full Text Available This paper aims to propose and validate a model based on the Theory of Planned Behavior in order to explain consumers’ pro-environmental behaviour regarding energy conservation. The model was constructed using the five variables from Ajzen’s Theory of Planned Behavior (TPB (behaviour, intention, perceived behavioural control, subjective norms and attitude to which a variable adapted from Schwartz’s Norm Activation Theory (NAT was added (“awareness of the consequences and the need” in order to create a unique model adapted for the special case of energy conservation behaviour. Further, a survey was conducted and the data collected were analysed using structural equation modelling. The first step of data analysis confirmed that all the constructs have good reliability, internal consistency and validity. The results of the structural equation analysis validated the proposed model, with all the model fit and quality indices having very good values. In the analysis of consumers’ proenvironmental behaviour regarding energy conservation and their intention to behave in a proenvironmental manner, this model proved to have a strong predictive power. Five of seven hypotheses were validated, the newly introduced variable proving to be a success. The proposed model is unique and will offer companies and organizations a valuable green marketing tool which can be used in the fight for environment protection and energy conservation.

  14. Using Game Theoretic Models to Predict Pilot Behavior in NextGen Merging and Landing Scenario

    Science.gov (United States)

    Yildiz, Yildiray; Lee, Ritchie; Brat, Guillaume

    2012-01-01

    In this paper, we present an implementation of the Semi Network-Form Game framework to predict pilot behavior in a merging and landing scenario. In this scenario, two aircraft are approaching to a freeze horizon with approximately equal distance when they become aware of each other via an ADS-B communication link that will be available in NextGen airspace. Both pilots want to gain advantage over the other by entering the freeze horizon earlier and obtain the first place in landing. They re-adjust their speed accordingly. However, they cannot simply increase their speed to the maximum allowable values since they are concerned with safety, separation distance, effort, possibility of being vectored-off from landing and possibility of violating speed constraints. We present how to model these concerns and the rest of the system using semi network-from game framework. Using this framework, based on certain assumptions on pilot utility functions and on system configuration, we provide estimates of pilot behavior and overall system evolution in time. We also discuss the possible employment of this modeling tool for airspace design optimization. To support this discussion, we provide a case where we investigate the effect of increasing the merging point speed limit on the commanded speed distribution and on the percentage of vectored aircraft.

  15. Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis. Pc Behavior Prediction Models

    Science.gov (United States)

    Vallejo, J.J.; Hejduk, M.D.; Stamey, J. D.

    2015-01-01

    Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

  16. The Political Gender Gap: Gender Bias in Facial Inferences that Predict Voting Behavior

    Science.gov (United States)

    Chiao, Joan Y.; Bowman, Nicholas E.; Gill, Harleen

    2008-01-01

    Background Throughout human history, a disproportionate degree of political power around the world has been held by men. Even in democracies where the opportunity to serve in top political positions is available to any individual elected by the majority of their constituents, most of the highest political offices are occupied by male leaders. What psychological factors underlie this political gender gap? Contrary to the notion that people use deliberate, rational strategies when deciding whom to vote for in major political elections, research indicates that people use shallow decision heuristics, such as impressions of competence solely from a candidate's facial appearance, when deciding whom to vote for. Because gender has previously been shown to affect a number of inferences made from the face, here we investigated the hypothesis that gender of both voter and candidate affects the kinds of facial impressions that predict voting behavior. Methodology/Principal Finding Male and female voters judged a series of male and female political candidates on how competent, dominant, attractive and approachable they seemed based on their facial appearance. Then they saw a series of pairs of political candidates and decided which politician they would vote for in a hypothetical election for President of the United States. Results indicate that both gender of voter and candidate affect the kinds of facial impressions that predict voting behavior. All voters are likely to vote for candidates who appear more competent. However, male candidates that appear more approachable and female candidates who appear more attractive are more likely to win votes. In particular, men are more likely to vote for attractive female candidates whereas women are more likely to vote for approachable male candidates. Conclusions/Significance Here we reveal gender biases in the intuitive heuristics that voters use when deciding whom to vote for in major political elections. Our findings underscore

  17. The political gender gap: gender bias in facial inferences that predict voting behavior.

    Directory of Open Access Journals (Sweden)

    Joan Y Chiao

    Full Text Available BACKGROUND: Throughout human history, a disproportionate degree of political power around the world has been held by men. Even in democracies where the opportunity to serve in top political positions is available to any individual elected by the majority of their constituents, most of the highest political offices are occupied by male leaders. What psychological factors underlie this political gender gap? Contrary to the notion that people use deliberate, rational strategies when deciding whom to vote for in major political elections, research indicates that people use shallow decision heuristics, such as impressions of competence solely from a candidate's facial appearance, when deciding whom to vote for. Because gender has previously been shown to affect a number of inferences made from the face, here we investigated the hypothesis that gender of both voter and candidate affects the kinds of facial impressions that predict voting behavior. METHODOLOGY/PRINCIPAL FINDING: Male and female voters judged a series of male and female political candidates on how competent, dominant, attractive and approachable they seemed based on their facial appearance. Then they saw a series of pairs of political candidates and decided which politician they would vote for in a hypothetical election for President of the United States. Results indicate that both gender of voter and candidate affect the kinds of facial impressions that predict voting behavior. All voters are likely to vote for candidates who appear more competent. However, male candidates that appear more approachable and female candidates who appear more attractive are more likely to win votes. In particular, men are more likely to vote for attractive female candidates whereas women are more likely to vote for approachable male candidates. CONCLUSIONS/SIGNIFICANCE: Here we reveal gender biases in the intuitive heuristics that voters use when deciding whom to vote for in major political elections. Our

  18. Behavior based safety approach towards fire

    International Nuclear Information System (INIS)

    Suresh Kumar, R.

    2009-01-01

    The behavior of the individual who notice fire first is very important because it affect the safety of all occupants of the area. Human behavior on fire depends on variables of the buildings in which fire occurs and by the appearance of the fire when it is detected. Altruistic behavior of human being will help to handle the critical conditions due to fire emergencies. NPCIL have developed a culture of systematic approach to safeguard men and materials from fire by training and awareness. In our Nuclear Power Plants, we have an effective plan and system to test the plans. In each emergency exercises, the behavior of individuals will be monitored and recorded

  19. Can parenting practices predict externalizing behavior problems among children with hearing impairment?

    Science.gov (United States)

    Pino, María J; Castillo, Rosa A; Raya, Antonio; Herruzo, Javier

    2017-11-09

    To identify possible differences in the level of externalizing behavior problems among children with and without hearing impairment and determine whether any relationship exists between this type of problem and parenting practices. The Behavior Assessment System for Children was used to evaluate externalizing variables in a sample of 118 boys and girls divided into two matched groups: 59 with hearing disorders and 59 normal-hearing controls. Significant between-group differences were found in hyperactivity, behavioral problems, and externalizing problems, but not in aggression. Significant differences were also found in various aspects of parenting styles. A model for predicting externalizing behavior problems was constructed, achieving a predicted explained variance of 50%. Significant differences do exist between adaptation levels in children with and without hearing impairment. Parenting style also plays an important role.

  20. Can parenting practices predict externalizing behavior problems among children with hearing impairment?

    Directory of Open Access Journals (Sweden)

    María J. Pino

    2017-11-01

    Full Text Available Objective: To identify possible differences in the level of externalizing behavior problems among children with and without hearing impairment and determine whether any relationship exists between this type of problem and parenting practices. Methods: The Behavior Assessment System for Children was used to evaluate externalizing variables in a sample of 118 boys and girls divided into two matched groups: 59 with hearing disorders and 59 normal-hearing controls. Results: Significant between-group differences were found in hyperactivity, behavioral problems, and externalizing problems, but not in aggression. Significant differences were also found in various aspects of parenting styles. A model for predicting externalizing behavior problems was constructed, achieving a predicted explained variance of 50%. Conclusion: Significant differences do exist between adaptation levels in children with and without hearing impairment. Parenting style also plays an important role.

  1. Modeling and prediction of human word search behavior in interactive machine translation

    Science.gov (United States)

    Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

    2017-12-01

    As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.

  2. Interplay between marital attributions and conflict behavior in predicting depressive symptoms.

    Science.gov (United States)

    Ellison, Jenna K; Kouros, Chrystyna D; Papp, Lauren M; Cummings, E Mark

    2016-03-01

    Marital attributions-that is, causal inferences and explanations spouses make about their partners' behavior-have been implicated as predictors of relationship functioning. Extending previous work, we examined marital attributions as a moderator of the link between marital conflict and depressive symptoms 1 year later. Participants were 284 couples who reported on marital attributions and depressive symptoms. Couples also engaged in a videotaped marital conflict interaction, which was later coded for specific conflict behaviors. The results showed that husbands' and wives' marital attributions about their partner moderated relations between marital conflict behavior and later depressive symptoms, controlling for global marital sentiments. For husbands, positive behavior and affect during marital conflict predicted a decrease in depressive symptoms, but only for husbands' who made low levels of responsibility and causal attributions about their wives. Wives' causal attributions about their partner also moderated relations between positive behavior and affect during marital conflict and husbands' later depressive symptoms. Reflecting an unexpected finding, negative behavior and affect during marital conflict predicted increases in wives' depressive symptoms, but only for wives who made low levels of responsibility attributions about their partner. The findings suggest that, for husbands, low levels of negative marital attributions for spouses may be protective, strengthening the positive effect of constructive conflict behaviors for their mental health, whereas for wives low levels of responsibility attributions about their spouse may be a risk factor, exacerbating the negative effect of negative marital conflict behaviors on their later depressive symptoms. (c) 2016 APA, all rights reserved).

  3. Exploring viewing behavior data from whole slide images to predict correctness of students' answers during practical exams in oral pathology.

    Science.gov (United States)

    Walkowski, Slawomir; Lundin, Mikael; Szymas, Janusz; Lundin, Johan

    2015-01-01

    The way of viewing whole slide images (WSI) can be tracked and analyzed. In particular, it can be useful to learn how medical students view WSIs during exams and how their viewing behavior is correlated with correctness of the answers they give. We used software-based view path tracking method that enabled gathering data about viewing behavior of multiple simultaneous WSI users. This approach was implemented and applied during two practical exams in oral pathology in 2012 (88 students) and 2013 (91 students), which were based on questions with attached WSIs. Gathered data were visualized and analyzed in multiple ways. As a part of extended analysis, we tried to use machine learning approaches to predict correctness of students' answers based on how they viewed WSIs. We compared the results of analyses for years 2012 and 2013 - done for a single question, for student groups, and for a set of questions. The overall patterns were generally consistent across these 3 years. Moreover, viewing behavior data appeared to have certain potential for predicting answers' correctness and some outcomes of machine learning approaches were in the right direction. However, general prediction results were not satisfactory in terms of precision and recall. Our work confirmed that the view path tracking method is useful for discovering viewing behavior of students analyzing WSIs. It provided multiple useful insights in this area, and general results of our analyses were consistent across two exams. On the other hand, predicting answers' correctness appeared to be a difficult task - students' answers seem to be often unpredictable.

  4. Exploring viewing behavior data from whole slide images to predict correctness of students′ answers during practical exams in oral pathology

    Directory of Open Access Journals (Sweden)

    Slawomir Walkowski

    2015-01-01

    Full Text Available The way of viewing whole slide images (WSI can be tracked and analyzed. In particular, it can be useful to learn how medical students view WSIs during exams and how their viewing behavior is correlated with correctness of the answers they give. We used software-based view path tracking method that enabled gathering data about viewing behavior of multiple simultaneous WSI users. This approach was implemented and applied during two practical exams in oral pathology in 2012 (88 students and 2013 (91 students, which were based on questions with attached WSIs. Gathered data were visualized and analyzed in multiple ways. As a part of extended analysis, we tried to use machine learning approaches to predict correctness of students′ answers based on how they viewed WSIs. We compared the results of analyses for years 2012 and 2013 - done for a single question, for student groups, and for a set of questions. The overall patterns were generally consistent across these 3 years. Moreover, viewing behavior data appeared to have certain potential for predicting answers′ correctness and some outcomes of machine learning approaches were in the right direction. However, general prediction results were not satisfactory in terms of precision and recall. Our work confirmed that the view path tracking method is useful for discovering viewing behavior of students analyzing WSIs. It provided multiple useful insights in this area, and general results of our analyses were consistent across two exams. On the other hand, predicting answers′ correctness appeared to be a difficult task - students′ answers seem to be often unpredictable.

  5. How the brain predicts people's behavior in relation to rules and desires. Evidence of a medio-prefrontal dissociation.

    Science.gov (United States)

    Corradi-Dell'Acqua, Corrado; Turri, Francesco; Kaufmann, Laurence; Clément, Fabrice; Schwartz, Sophie

    2015-09-01

    Forming and updating impressions about others is critical in everyday life and engages portions of the dorsomedial prefrontal cortex (dMPFC), the posterior cingulate cortex (PCC) and the amygdala. Some of these activations are attributed to "mentalizing" functions necessary to represent people's mental states, such as beliefs or desires. Evolutionary psychology and developmental studies, however, suggest that interpersonal inferences can also be obtained through the aid of deontic heuristics, which dictate what must (or must not) be done in given circumstances. We used fMRI and asked 18 participants to predict whether unknown characters would follow their desires or obey external rules. Participants had no means, at the beginning, to make accurate predictions, but slowly learned (throughout the experiment) each character's behavioral profile. We isolated brain regions whose activity changed during the experiment, as a neural signature of impression updating: whereas dMPFC was progressively more involved in predicting characters' behavior in relation to their desires, the medial orbitofrontal cortex and the amygdala were progressively more recruited in predicting rule-based behavior. Our data provide evidence of a neural dissociation between deontic inference and theory-of-mind (ToM), and support a differentiation of orbital and dorsal prefrontal cortex in terms of low- and high-level social cognition. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. A Behavioral Economic Reward Index Predicts Drinking Resolutions: Moderation Re-visited and Compared with Other Outcomes

    Science.gov (United States)

    Tucker, Jalie A.; Roth, David L.; Vignolo, Mary J.; Westfall, Andrew O.

    2014-01-01

    Data were pooled from three studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1-2 year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes compared to abstinence or relapse and to be predicted by more balanced pre-resolution monetary allocations between short- and longer-term objectives (i.e., drinking and saving for the future). Standardized odds ratios (OR) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this “Alcohol-Savings Discretionary Expenditure” index predicted higher rates of both abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of pre-resolution spending patterns aids in predicting moderation. PMID:19309182

  7. Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction

    Directory of Open Access Journals (Sweden)

    Quan Liang

    2018-01-01

    Full Text Available This paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel routes and further forecasts the public transport passenger’s behavior choice. The proposed travel behavior graph is composed of macronodes, arcs, and transfer probability. Each macronode corresponds to a travel association map and represents a travel behavior. A travel association map also contains its own nodes. The nodes of a travel association map are created when the processed travel chain data shows significant change. Thus, each node of three layers represents a significant change of spatial travel positions, travel time, and routes, respectively. Since a travel association map represents a travel behavior, the graph can be considered a sequence of travel behaviors. Through integrating travel association map and calculating the probabilities of the arcs, it is possible to construct a unique travel behavior graph for each passenger. The data used in this study are multimode data matched by certain rules based on the data of public transport smart card transactions and network features. The case study results show that graph based method to model the individual travel behavior of public transport passengers is effective and feasible. Travel behavior graphs support customized public transport travel characteristics analysis and demand prediction.

  8. Prediction of inelastic behavior and creep-fatigue life of perforated plates

    International Nuclear Information System (INIS)

    Igari, Toshihide; Yamauchi, Masafumi; Nomura, Shinichi.

    1992-01-01

    Prediction methods of macroscopic and local stress-strain behaviors of perforated plates in plastic and creep regime are proposed in this paper, and are applied to the creep-fatigue life prediction of perforated plates. Both equivalent-solid-plate properties corresponding to the macroscopic behavior and the stress-strain concentration around a hole were obtained by assuming the analogy between plasticity and creep and also by extending the authors' proposal in creep condition. The perforated plates which were made of Hastelloy XR were subjected to the strain-controlled cyclic test at 950degC in air in order to experimentally obtain the macroscopic behavior such as the cyclic stress-strain curve and creep-fatigue life around a hole. The results obtained are summarized as follows. (1) The macroscopic behavior of perforated plates including cyclic stress-strain behavior and relaxation is predictable by using the proposed method in this paper. (2) The creep-fatigue life around a hole can be predicted by using the proposed method for stress-strain concentration around a hole. (author)

  9. The wind power prediction research based on mind evolutionary algorithm

    Science.gov (United States)

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

    2018-04-01

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

  10. Heart rate during conflicts predicts post-conflict stress-related behavior in greylag geese.

    Directory of Open Access Journals (Sweden)

    Claudia A F Wascher

    Full Text Available BACKGROUND: Social stressors are known to be among the most potent stressors in group-living animals. This is not only manifested in individual physiology (heart rate, glucocorticoids, but also in how individuals behave directly after a conflict. Certain 'stress-related behaviors' such as autopreening, body shaking, scratching and vigilance have been suggested to indicate an individual's emotional state. Such behaviors may also alleviate stress, but the behavioral context and physiological basis of those behaviors is still poorly understood. METHODOLOGY/PRINCIPAL FINDINGS: We recorded beat-to-beat heart rates (HR of 22 greylag geese in response to agonistic encounters using fully implanted sensor-transmitter packages. Additionally, for 143 major events we analyzed the behavior shown by our focal animals in the first two minutes after an interaction. Our results show that the HR during encounters and characteristics of the interaction predicted the frequency and duration of behaviors shown after a conflict. CONCLUSIONS/SIGNIFICANCE: To our knowledge this is the first study to quantify the physiological and behavioral responses to single agonistic encounters and to link this to post conflict behavior. Our results demonstrate that 'stress-related behaviors' are flexibly modulated by the characteristics of the preceding aggressive interaction and reflect the individual's emotional strain, which is linked to autonomic arousal. We found no support for the stress-alleviating hypothesis, but we propose that stress-related behaviors may play a role in communication with other group members, particularly with pair-partners.

  11. SCALE DEVELOPMENT FOR MEASURING AND PREDICTING ADOLESCENTS' LEISURE TIME PHYSICAL ACTIVITY BEHAVIOR

    Directory of Open Access Journals (Sweden)

    Silvia Arribas Galarraga

    2009-12-01

    Full Text Available The aim of this study was to develop a scale for assessing and predicting adolescents' physical activity behavior in Spain and Luxembourg using the Theory of Planned Behavior as a framework. The sample was comprised of 613 Spanish (boys = 309, girls = 304; M age =15.28, SD =1.127 and 752 Luxembourgish adolescents (boys = 343, girls = 409; M age = 14.92, SD = 1.198, selected from students of two secondary schools in both countries, with a similar socio-economic status. The initial 43-items were all scored on a 4-point response format using the structured alternative format and translated into Spanish, French and German. In order to ensure the accuracy of the translation, standardized parallel back-translation techniques were employed. Following two pilot tests and subsequent revisions, a second order exploratory factor analysis with oblimin direct rotation was used for factor extraction. Internal consistency and test-retest reliabilities were also tested. The 4-week test-retest correlations confirmed the items' time stability. The same five factors were obtained, explaining 63.76% and 63.64% of the total variance in both samples. Internal consistency for the five factors ranged from α = 0.759 to α = 0. 949 in the Spanish sample and from α = 0.735 to α = 0.952 in the Luxembourgish sample. For both samples, inter-factor correlations were all reported significant and positive, except for Factor 5 where they were significant but negative. The high internal consistency of the subscales, the reported item test-retest reliabilities and the identical factor structure confirm the adequacy of the elaborated questionnaire for assessing the TPB-based constructs when used with a population of adolescents in Spain and Luxembourg. The results give some indication that they may have value in measuring the hypothesized TPB constructs for PA behavior in a cross-cultural context

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

    Science.gov (United States)

    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.

  13. INFANT AVOIDANCE DURING A TACTILE TASK PREDICTS AUTISM SPECTRUM BEHAVIORS IN TODDLERHOOD.

    Science.gov (United States)

    Mammen, Micah A; Moore, Ginger A; Scaramella, Laura V; Reiss, David; Ganiban, Jody M; Shaw, Daniel S; Leve, Leslie D; Neiderhiser, Jenae M

    2015-01-01

    The experience of touch is critical for early communication and social interaction; infants who show aversion to touch may be at risk for atypical development and behavior problems. The current study aimed to clarify predictive associations between infant responses to tactile stimuli and toddler autism spectrum, internalizing, and externalizing behaviors. This study measured 9-month-old infants' (N = 561; 58% male) avoidance and negative affect during a novel tactile task in which parents painted infants' hands and feet and pressed them to paper to make a picture. Parent reports on the Pervasive Developmental Problems (PDP), Internalizing, and Externalizing scales of the Child Behavior Checklist were used to measure toddler behaviors at 18 months. Infant observed avoidance and negative affect were significantly correlated; however, avoidance predicted subsequent PDP scores only, independent of negative affect, which did not predict any toddler behaviors. Findings suggest that incorporating measures of responses to touch in the study of early social interaction may provide an important and discriminating construct for identifying children at greater risk for social impairments related to autism spectrum behaviors. © 2015 Michigan Association for Infant Mental Health.

  14. Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior.

    Science.gov (United States)

    Points, Laurie J; Taylor, James Ward; Grizou, Jonathan; Donkers, Kevin; Cronin, Leroy

    2018-01-30

    Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances to be made in understanding the driving forces behind oil-in-water droplet behaviors. Utilizing >7,000 experiments collected using an autonomous robotic platform, we illustrate how smart automation cannot only help with exploration, optimization, and discovery of new behaviors, but can also be core to developing fundamental understanding of such systems. Using this process, we were able to relate droplet formulation to behavior via predicted physical properties, and to identify and predict more occurrences of a rare collective droplet behavior, droplet swarming. Proton NMR spectroscopic and qualitative pH methods enabled us to better understand oil dissolution, chemical change, phase transitions, and droplet and aqueous phase flows, illustrating the utility of the combination of smart-automation and traditional analytical chemistry techniques. We further extended our study for the simultaneous exploration of both the oil and aqueous phases using a robotic platform. Overall, this work shows that the combination of chemistry, robotics, and artificial intelligence enables discovery, prediction, and mechanistic understanding in ways that no one approach could achieve alone.

  15. Determinants of oral hygiene behavior : a study based on the theory of planned behavior

    NARCIS (Netherlands)

    Buunk-Werkhoven, Y.A.; Dijkstra, Arie; van der Schans, C.P.

    Objective: The aim of this study was to develop an index for oral hygiene behavior (OHB) and to examine potential predictors of this actual behavior based on the theory of planned behavior (TPB). Measures of oral health knowledge (OHK) and the expected effect of having healthy teeth on social

  16. Differential Patterns of Amygdala and Ventral Striatum Activation Predict Gender-Specific Changes in Sexual Risk Behavior

    Science.gov (United States)

    Sansosti, Alexandra A.; Bowman, Hilary C.; Hariri, Ahmad R.

    2015-01-01

    Although the initiation of sexual behavior is common among adolescents and young adults, some individuals express this behavior in a manner that significantly increases their risk for negative outcomes including sexually transmitted infections. Based on accumulating evidence, we have hypothesized that increased sexual risk behavior reflects, in part, an imbalance between neural circuits mediating approach and avoidance in particular as manifest by relatively increased ventral striatum (VS) activity and relatively decreased amygdala activity. Here, we test our hypothesis using data from seventy 18- to 22-year-old university students participating in the Duke Neurogenetics Study. We found a significant three-way interaction between amygdala activation, VS activation, and gender predicting changes in the number of sexual partners over time. Although relatively increased VS activation predicted greater increases in sexual partners for both men and women, the effect in men was contingent on the presence of relatively decreased amygdala activation and the effect in women was contingent on the presence of relatively increased amygdala activation. These findings suggest unique gender differences in how complex interactions between neural circuit function contributing to approach and avoidance may be expressed as sexual risk behavior in young adults. As such, our findings have the potential to inform the development of novel, gender-specific strategies that may be more effective at curtailing sexual risk behavior. PMID:26063921

  17. Does Sedentary Behavior Predict Academic Performance in Adolescents or the Other Way Round? A Longitudinal Path Analysis.

    Science.gov (United States)

    Lizandra, Jorge; Devís-Devís, José; Pérez-Gimeno, Esther; Valencia-Peris, Alexandra; Peiró-Velert, Carmen

    2016-01-01

    This study examined whether adolescents' time spent on sedentary behaviors (academic, technological-based and social-based activities) was a better predictor of academic performance than the reverse. A cohort of 755 adolescents participated in a three-year period study. Structural Equation Modeling techniques were used to test plausible causal hypotheses. Four competing models were analyzed to determine which model best fitted the data. The Best Model was separately tested by gender. The Best Model showed that academic performance was a better predictor of sedentary behaviors than the other way round. It also indicated that students who obtained excellent academic results were more likely to succeed academically three years later. Moreover, adolescents who spent more time in the three different types of sedentary behaviors were more likely to engage longer in those sedentary behaviors after the three-year period. The better the adolescents performed academically, the less time they devoted to social-based activities and more to academic activities. An inverse relationship emerged between time dedicated to technological-based activities and academic sedentary activities. A moderating auto-regressive effect by gender indicated that boys were more likely to spend more time on technological-based activities three years later than girls. To conclude, previous academic performance predicts better sedentary behaviors three years later than the reverse. The positive longitudinal auto-regressive effects on the four variables under study reinforce the 'success breeds success' hypothesis, with academic performance and social-based activities emerging as the strongest ones. Technological-based activities showed a moderating effect by gender and a negative longitudinal association with academic activities that supports a displacement hypothesis. Other longitudinal and covariate effects reflect the complex relationships among sedentary behaviors and academic performance and the

  18. Respiratory Sinus Arrhythmia Activity Predicts Internalizing and Externalizing Behaviors in Non-referred Boys

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2017-09-01

    Full Text Available Atypical respiratory sinus arrhythmia (RSA, a biomarker of emotion dysregulation, is associated with both externalizing and internalizing behaviors. In addition, social adversity and gender may moderate this association. In this study, we investigated if RSA (both resting RSA and RSA reactivity in an emotion regulation task predicts externalizing and/or internalizing behaviors and the extent to which social adversity moderates this relationship. Two hundred and fifty-three children (at Time 1, mean age = 9.05, SD = 0.60, 48% boys and their caregivers from the community participated in this study. Resting RSA and RSA reactivity were assessed, and caregivers reported children’s externalizing and internalizing behaviors at both Time 1 and Time 2 (1 year later. We found that lower resting RSA (but not RSA reactivity at Time 1 was associated with increased externalizing and internalizing behaviors at Time 2 in boys, even after controlling for the effects of Time 1 behavioral problems and Time 2 age. Moreover, there was a significant interaction effect between Time 1 resting RSA and social adversity such that lower resting RSA predicted higher externalizing and internalizing behaviors in boys only under conditions of high social adversity. Follow-up analyses revealed that these predictive effects were stronger for externalizing behavior than for internalizing behavior. No significant effects were found for girls. Our findings provide further evidence that low resting RSA may be a transdiagnostic biomarker of emotion dysregulation and a predisposing risk factor for both types of behavior problems, in particular for boys who grow up in adverse environments. We conclude that biosocial interaction effects and gender differences should be considered when examining the etiological mechanisms of child psychopathology.

  19. Predicting nature-based tourist roles: a life span perspective

    Science.gov (United States)

    James J. Murdy; Heather J. Gibson; Andrew Yiannakis

    2003-01-01

    The concept of stable, clearly identifiable patterns of tourist behavior, or roles, is a relatively recent development. Yiannakis and Gibson (1988, 1992) identified fifteen tourist roles based on leisure travelers' vacation behaviors. Building on this work, Gibson (1994) used discriminant analysis to determine the combination of needs and demographics are...

  20. Size-based predictions of food web patterns

    DEFF Research Database (Denmark)

    Zhang, Lai; Hartvig, Martin; Knudsen, Kim

    2014-01-01

    We employ size-based theoretical arguments to derive simple analytic predictions of ecological patterns and properties of natural communities: size-spectrum exponent, maximum trophic level, and susceptibility to invasive species. The predictions are brought about by assuming that an infinite number...... of species are continuously distributed on a size-trait axis. It is, however, an open question whether such predictions are valid for a food web with a finite number of species embedded in a network structure. We address this question by comparing the size-based predictions to results from dynamic food web...... simulations with varying species richness. To this end, we develop a new size- and trait-based food web model that can be simplified into an analytically solvable size-based model. We confirm existing solutions for the size distribution and derive novel predictions for maximum trophic level and invasion...

  1. Base Oils Biodegradability Prediction with Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    Malika Trabelsi

    2010-02-01

    Full Text Available In this paper, we apply various data mining techniques including continuous numeric and discrete classification prediction models of base oils biodegradability, with emphasis on improving prediction accuracy. The results show that highly biodegradable oils can be better predicted through numeric models. In contrast, classification models did not uncover a similar dichotomy. With the exception of Memory Based Reasoning and Decision Trees, tested classification techniques achieved high classification prediction. However, the technique of Decision Trees helped uncover the most significant predictors. A simple classification rule derived based on this predictor resulted in good classification accuracy. The application of this rule enables efficient classification of base oils into either low or high biodegradability classes with high accuracy. For the latter, a higher precision biodegradability prediction can be obtained using continuous modeling techniques.

  2. Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.

    Science.gov (United States)

    Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

    2014-01-01

    The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

  3. Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

    Directory of Open Access Journals (Sweden)

    Stefano Curcio

    2014-01-01

    Full Text Available The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

  4. Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia.

    Science.gov (United States)

    Dang-Vu, Thien Thanh; Hatch, Benjamin; Salimi, Ali; Mograss, Melodee; Boucetta, Soufiane; O'Byrne, Jordan; Brandewinder, Marie; Berthomier, Christian; Gouin, Jean-Philippe

    2017-11-01

    While cognitive-behavioral therapy for insomnia constitutes the first-line treatment for chronic insomnia, only few reports have investigated how sleep architecture relates to response to this treatment. In this pilot study, we aimed to determine whether pre-treatment sleep spindle density predicts treatment response to cognitive-behavioral therapy for insomnia. Twenty-four participants with chronic primary insomnia participated in a 6-week cognitive-behavioral therapy for insomnia performed in groups of 4-6 participants. Treatment response was assessed using the Pittsburgh Sleep Quality Index and the Insomnia Severity Index measured at pre- and post-treatment, and at 3- and 12-months' follow-up assessments. Secondary outcome measures were extracted from sleep diaries over 7 days and overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stage N2-N3 sleep was extracted from polysomnography at pre-treatment. Hierarchical linear modeling analysis assessed whether sleep spindle density predicted response to cognitive-behavioral therapy. After adjusting for age, sex, and education level, lower spindle density at pre-treatment predicted poorer response over the 12-month follow-up, as reflected by a smaller reduction in Pittsburgh Sleep Quality Index over time. Reduced spindle density also predicted lower improvements in sleep diary sleep efficiency and wake after sleep onset immediately after treatment. There were no significant associations between spindle density and changes in the Insomnia Severity Index or polysomnography variables over time. These preliminary results suggest that inter-individual differences in sleep spindle density in insomnia may represent an endogenous biomarker predicting responsiveness to cognitive-behavioral therapy. Insomnia with altered spindle activity might constitute an insomnia subtype characterized by a neurophysiological vulnerability to sleep disruption associated with impaired responsiveness to

  5. Copula-based prediction of economic movements

    Science.gov (United States)

    García, J. E.; González-López, V. A.; Hirsh, I. D.

    2016-06-01

    In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

  6. Bankruptcy Prediction Based on the Autonomy Ratio

    Directory of Open Access Journals (Sweden)

    Daniel Brîndescu Olariu

    2016-11-01

    Full Text Available The theory and practice of the financial ratio analysis suggest the existence of a negative correlation between the autonomy ratio and the bankruptcy risk. Previous studies conducted on a sample of companies from Timis County (largest county in Romania confirm this hypothesis and recommend the autonomy ratio as a useful tool for measuring the bankruptcy risk two years in advance. The objective of the current research was to develop a methodology for measuring the bankruptcy risk that would be applicable for the companies from the Timis County (specific methodologies are considered necessary for each region. The target population consisted of all the companies from Timis County with annual sales of over 10,000 lei (aprox. 2,200 Euros. The research was performed over all the target population. The study has thus included 53,252 yearly financial statements from the period 2007 – 2010. The results of the study allow for the setting of benchmarks, as well as the configuration of a methodology of analysis. The proposed methodology cannot predict with perfect accuracy the state of the company, but it allows for a valuation of the risk level to which the company is subjected.

  7. Prediction of pipeline corrosion rate based on grey Markov models

    International Nuclear Information System (INIS)

    Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin

    2009-01-01

    Based on the model that combined by grey model and Markov model, the prediction of corrosion rate of nuclear power pipeline was studied. Works were done to improve the grey model, and the optimization unbiased grey model was obtained. This new model was used to predict the tendency of corrosion rate, and the Markov model was used to predict the residual errors. In order to improve the prediction precision, rolling operation method was used in these prediction processes. The results indicate that the improvement to the grey model is effective and the prediction precision of the new model combined by the optimization unbiased grey model and Markov model is better, and the use of rolling operation method may improve the prediction precision further. (authors)

  8. Predicting risk-taking behavior from prefrontal resting-state activity and personality.

    Directory of Open Access Journals (Sweden)

    Bettina Studer

    Full Text Available Risk-taking is subject to considerable individual differences. In the current study, we tested whether resting-state activity in the prefrontal cortex and trait sensitivity to reward and punishment can help predict risk-taking behavior. Prefrontal activity at rest was assessed in seventy healthy volunteers using electroencephalography, and compared to their choice behavior on an economic risk-taking task. The Behavioral Inhibition System/Behavioral Activation System scale was used to measure participants' trait sensitivity to reward and punishment. Our results confirmed both prefrontal resting-state activity and personality traits as sources of individual differences in risk-taking behavior. Right-left asymmetry in prefrontal activity and scores on the Behavioral Inhibition System scale, reflecting trait sensitivity to punishment, were correlated with the level of risk-taking on the task. We further discovered that scores on the Behavioral Inhibition System scale modulated the relationship between asymmetry in prefrontal resting-state activity and risk-taking. The results of this study demonstrate that heterogeneity in risk-taking behavior can be traced back to differences in the basic physiology of decision-makers' brains, and suggest that baseline prefrontal activity and personality traits might interplay in guiding risk-taking behavior.

  9. Role of Procrastination and Motivational Self-Regulation in Predicting Students\\' Behavioral Engagement

    Directory of Open Access Journals (Sweden)

    Abbasi M

    2015-12-01

    Full Text Available Aims: As an important intervening factor to enhance educational and motivational performance of the students, understating the effective factors on behavioral enthusiasm plays a very important role. The aim of this study was to explain the role of motivational self-regulation and procrastination in predicting the students’ behavioral enthusiasm.  Instrument & Methods: In the correlational descriptive cross-sectional study, 311 students of Arak University of Medical Sciences were selected via Available Sampling using Cochran’s Formula in 2014-15 academic year. Data was collected, using Students’ Educational Procrastination Scale, Motivational Self-regulating Scale, and Behavioral Enthusiasm Scale. Data was analyzed in SPSS 19 software using Pearson Correlation Coefficient, and Multiple Regression Analysis. Findings: The highest and the lowest correlations were between procrastination and behavioral enthusiasm and between environmental control and behavioral enthusiasm, respectively (p<0.05. There was a positive and significant correlation between self-regulation and behavioral enthusiasm. In addition, there was a negative and significant correlation between procrastination and behavioral enthusiasm (p<0.001. Totally, procrastination (β=-0.233 and motivational self-regulation (β=0.238 explained 10% of the students’ behavioral enthusiasm variance (p<0.001; R²=0.102. Conclusion: Any reduction in procrastination and any enhancement in motivational self-regulation can enhance the students’ behavioral enthusiasm. 

  10. Predicting Risk-Taking Behavior from Prefrontal Resting-State Activity and Personality

    Science.gov (United States)

    Studer, Bettina; Pedroni, Andreas; Rieskamp, Jörg

    2013-01-01

    Risk-taking is subject to considerable individual differences. In the current study, we tested whether resting-state activity in the prefrontal cortex and trait sensitivity to reward and punishment can help predict risk-taking behavior. Prefrontal activity at rest was assessed in seventy healthy volunteers using electroencephalography, and compared to their choice behavior on an economic risk-taking task. The Behavioral Inhibition System/Behavioral Activation System scale was used to measure participants’ trait sensitivity to reward and punishment. Our results confirmed both prefrontal resting-state activity and personality traits as sources of individual differences in risk-taking behavior. Right-left asymmetry in prefrontal activity and scores on the Behavioral Inhibition System scale, reflecting trait sensitivity to punishment, were correlated with the level of risk-taking on the task. We further discovered that scores on the Behavioral Inhibition System scale modulated the relationship between asymmetry in prefrontal resting-state activity and risk-taking. The results of this study demonstrate that heterogeneity in risk-taking behavior can be traced back to differences in the basic physiology of decision-makers’ brains, and suggest that baseline prefrontal activity and personality traits might interplay in guiding risk-taking behavior. PMID:24116176

  11. Conflict and expectancies interact to predict sexual behavior under the influence among gay and bisexual men

    Science.gov (United States)

    Wells, Brooke E; Starks, Tyrel J; Parsons, Jeffrey T; Golub, Sarit

    2013-01-01

    As the mechanisms of the associations between substance use and risky sex remain unclear, this study investigates the interactive roles of conflicts about casual sex and condom use and expectancies of the sexual effects of substances in those associations among gay men. Conflict interacted with expectancies to predict sexual behavior under the influence; low casual sex conflict coupled with high expectancies predicted the highest number of casual partners, and high condom use conflict and high expectancies predicted the highest number of unprotected sex acts. Results have implications for intervention efforts that aim to improve sexual decision-making and reduce sexual expectancies. PMID:23584507

  12. Churn prediction based on text mining and CRM data analysis

    OpenAIRE

    Schatzmann, Anders; Heitz, Christoph; Münch, Thomas

    2014-01-01

    Within quantitative marketing, churn prediction on a single customer level has become a major issue. An extensive body of literature shows that, today, churn prediction is mainly based on structured CRM data. However, in the past years, more and more digitized customer text data has become available, originating from emails, surveys or scripts of phone calls. To date, this data source remains vastly untapped for churn prediction, and corresponding methods are rarely described in literature. ...

  13. Statistical model based gender prediction for targeted NGS clinical panels

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel

    2017-12-01

    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  14. Threat Interference Biases Predict Socially Anxious Behavior: The Role of Inhibitory Control and Minute of Stressor.

    Science.gov (United States)

    Gorlin, Eugenia I; Teachman, Bethany A

    2015-07-01

    The current study brings together two typically distinct lines of research. First, social anxiety is inconsistently associated with behavioral deficits in social performance, and the factors accounting for these deficits remain poorly understood. Second, research on selective processing of threat cues, termed cognitive biases, suggests these biases typically predict negative outcomes, but may sometimes be adaptive, depending on the context. Integrating these research areas, the current study examined whether conscious and/or unconscious threat interference biases (indexed by the unmasked and masked emotional Stroop) can explain unique variance, beyond self-reported anxiety measures, in behavioral avoidance and observer-rated anxious behavior during a public speaking task. Minute of speech and general inhibitory control (indexed by the color-word Stroop) were examined as within-subject and between-subject moderators, respectively. Highly socially anxious participants (N=135) completed the emotional and color-word Stroop blocks prior to completing a 4-minute videotaped speech task, which was later coded for anxious behaviors (e.g., speech dysfluency). Mixed-effects regression analyses revealed that general inhibitory control moderated the relationship between both conscious and unconscious threat interference bias and anxious behavior (though not avoidance), such that lower threat interference predicted higher levels of anxious behavior, but only among those with relatively weaker (versus stronger) inhibitory control. Minute of speech further moderated this relationship for unconscious (but not conscious) social-threat interference, such that lower social-threat interference predicted a steeper increase in anxious behaviors over the course of the speech (but only among those with weaker inhibitory control). Thus, both trait and state differences in inhibitory control resources may influence the behavioral impact of threat biases in social anxiety. Copyright © 2015

  15. Power Load Prediction Based on Fractal Theory

    OpenAIRE

    Jian-Kai, Liang; Cattani, Carlo; Wan-Qing, Song

    2015-01-01

    The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and...

  16. Decisions among the undecided: implicit attitudes predict future voting behavior of undecided voters.

    Directory of Open Access Journals (Sweden)

    Kristjen B Lundberg

    Full Text Available Implicit attitudes have been suggested as a key to unlock the hidden preferences of undecided voters. Past research, however, offered mixed support for this hypothesis. The present research used a large nationally representative sample and a longitudinal design to examine the predictive utility of implicit and explicit attitude measures in the 2008 U.S. presidential election. In our analyses, explicit attitudes toward candidates predicted voting better for decided than undecided voters, but implicit candidate attitudes were predictive of voting for both decided and undecided voters. Extending our examination to implicit and explicit racial attitudes, we found the same pattern. Taken together, these results provide convergent evidence that implicit attitudes predict voting about as well for undecided as for decided voters. We also assessed a novel explanation for these effects by evaluating whether implicit attitudes may predict the choices of undecided voters, in part, because they are neglected when people introspect about their confidence. Consistent with this idea, we found that the extremity of explicit but not implicit attitudes was associated with greater confidence. These analyses shed new light on the utility of implicit measures in predicting future behavior among individuals who feel undecided. Considering the prior studies together with this new evidence, the data seem to be consistent that implicit attitudes may be successful in predicting the behavior of undecided voters.

  17. Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions.

    Science.gov (United States)

    Hauri, D D; Huss, A; Zimmermann, F; Kuehni, C E; Röösli, M

    2013-10-01

    Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  18. Ensemble-based prediction of RNA secondary structures.

    Science.gov (United States)

    Aghaeepour, Nima; Hoos, Holger H

    2013-04-24

    Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures. Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between

  19. Organic Foods: Do Eco-Friendly Attitudes Predict Eco-Friendly Behaviors?

    Science.gov (United States)

    Dahm, Molly J.; Samonte, Aurelia V.; Shows, Amy R.

    2009-01-01

    Objective: The purpose of this study was to determine whether student awareness and attitudes about organic foods would predict their behaviors with regard to organic food consumption and other healthy lifestyle practices. A secondary purpose was to determine whether attitudes about similar eco-friendly practices would result in socially conscious…

  20. The Role of Life Satisfaction and Parenting Styles in Predicting Delinquent Behaviors among High School Students

    Science.gov (United States)

    Onder, Fulya Cenkseven; Yilmaz, Yasin

    2012-01-01

    The purpose of this study is to determine whether the parenting styles and life satisfaction predict delinquent behaviors frequently or not. Firstly the data were collected from 471 girls and 410 boys, a total of 881 high school students. Then the research was carried out with 502 students showing low (n = 262, 52.2%) and high level of delinquent…

  1. Development of dynamic compartment models for prediction of radionuclide behaviors in rice paddy fields

    International Nuclear Information System (INIS)

    Takahashi, Tomoyuki; Tomita, Ken'ichi; Yamamoto, Kazuhide; Uchida, Shigeo

    2007-01-01

    We are developing dynamic compartment models for prediction of behaviors of some important radionuclides in rice paddy fields for safety assessment of nuclear facilities. For a verification of these models, we report calculations for several different deposition patterns of radionuclides. (author)

  2. When Preferences Are in the Way: Children's Predictions of Goal-Directed Behaviors.

    Science.gov (United States)

    Yang, Fan; Frye, Douglas

    2017-12-18

    Across three studies, we examined 4- to 7-year-olds' predictions of goal-directed behaviors when goals conflict with preferences. In Study 1, when presented with stories in which a character had to act against basic preferences to achieve an interpersonal goal (e.g., playing with a partner), 6- and 7-year-olds were more likely than 4- and 5-year-olds to predict the actor would act in accordance with the goal to play with the partner, instead of fulfilling the basic preference of playing a favored activity. Similar results were obtained in Study 2 with scenarios that each involved a single individual pursuing intrapersonal goals that conflicted with his or her basic preferences. In Study 3, younger children's predictions of goal-directed behaviors did not increase for novel goals and preferences, when the influences of their own preferences, future thinking, or a lack of impulse control were minimized. The results suggest that between ages 4 and 7, children increasingly integrate and give more weight to other sources of motivational information (e.g., goals) in addition to preferences when predicting people's behaviors. This increasing awareness may have implications for children's self-regulatory and goal pursuit behaviors. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  3. Predicting entrepreneurial career intentions: Values and the theory of planned behavior.

    NARCIS (Netherlands)

    M.J. Gorgievski-Duijvesteijn (Marjan); U. Stephan (Ute); M. Laguna (Mariola); J.A. Moriano (Juan)

    2017-01-01

    textabstractIntegrating predictions from the theory of human values with the theory of planned behavior (TPB), our primary goal is to investigate mechanisms through which individual values are related to entrepreneurial career intentions using a sample of 823 students from four European countries.

  4. Extension of a Theory of Predictive Behavior to Immediate Recall by Preschool Children.

    Science.gov (United States)

    Bogartz, Richard S.

    This paper is concerned with memory functions in sequentially structured behavior. Twenty-five 4- and 5-year-old preschool children participated in a prediction experiment in which a stack of cards (each card alternately having a patch of red or green tape on it) was displayed to the child. The child was presented with a card and asked to predict…

  5. Predicting Social Support for Grieving Persons: A Theory of Planned Behavior Perspective

    Science.gov (United States)

    Bath, Debra M.

    2009-01-01

    Research has consistently reported that social support from family, friends, and colleagues is an important factor in the bereaved person's ability to cope after the loss of a loved one. This study used a Theory of Planned Behavior framework to identify those factors that predict a person's intention to interact with, and support, a grieving…

  6. Predicting College Students' Intention to Graduate: A Test of the Theory of Planned Behavior

    Science.gov (United States)

    Sutter, Nate; Paulson, Sharon

    2016-01-01

    The current study examined whether it is possible to increase college students' intention to earn a four-year degree with the Theory of Planned Behavior (TPB). Three research questions were examined: (1) Can the TPB predict traditional undergraduates' graduation intention? (2) Does graduation intention differ by traditional students' year of…

  7. BEHAVE: fire behavior prediction and fuel modeling system--FUEL subsystem

    Science.gov (United States)

    Robert E. Burgan; Richard C. Rothermel

    1984-01-01

    This manual documents the fuel modeling procedures of BEHAVE--a state-of-the-art wildland fire behavior prediction system. Described are procedures for collecting fuel data, using the data with the program, and testing and adjusting the fuel model.

  8. Mothers' Predictions of Their Son's Executive Functioning Skills: Relations to Child Behavior Problems

    Science.gov (United States)

    Johnston, Charlotte

    2011-01-01

    This study examined mothers' ability to accurately predict their sons' performance on executive functioning tasks in relation to the child's behavior problems. One-hundred thirteen mothers and their 4-7 year old sons participated. From behind a one-way mirror, mothers watched their sons perform tasks assessing inhibition and planning skills.…

  9. Ideal Teacher Behaviors: Student Motivation and Self-Efficacy Predict Preferences

    Science.gov (United States)

    Komarraju, Meera

    2013-01-01

    Differences in students' academic self-efficacy and motivation were examined in predicting preferred teacher traits. Undergraduates (261) completed the Teaching Behavior Checklist, Academic Self-Concept scale, and Academic Motivation scale. Hierarchical regression analyses indicated that academic self-efficacy and extrinsic motivation explained…

  10. Personality patterns predict the risk of antisocial behavior in Spanish-speaking adolescents.

    Science.gov (United States)

    Alcázar-Córcoles, Miguel A; Verdejo-García, Antonio; Bouso-Sáiz, José C; Revuelta-Menéndez, Javier; Ramírez-Lira, Ezequiel

    2017-05-01

    There is a renewed interest in incorporating personality variables in criminology theories in order to build models able to integrate personality variables and biological factors with psychosocial and sociocultural factors. The aim of this article is the assessment of personality dimensions that contribute to the prediction of antisocial behavior in adolescents. For this purpose, a sample of adolescents from El Salvador, Mexico, and Spain was obtained. The sample consisted of 1035 participants with a mean age of 16.2. There were 450 adolescents from a forensic population (those who committed a crime) and 585 adolescents from the normal population (no crime committed). All of participants answered personality tests about neuroticism, extraversion, psychoticism, sensation seeking, impulsivity, and violence risk. Principal component analysis of the data identified two independent factors: (i) the disinhibited behavior pattern (PDC), formed by the dimensions of neuroticism, psychoticism, impulsivity and risk of violence; and (ii) the extrovert behavior pattern (PEC), formed by the dimensions of sensation risk and extraversion. Both patterns significantly contributed to the prediction of adolescent antisocial behavior in a logistic regression model which properly classifies a global percentage of 81.9%, 86.8% for non-offense and 72.5% for offense behavior. The classification power of regression equations allows making very satisfactory predictions about adolescent offense commission. Educational level has been classified as a protective factor, while age and gender (male) have been classified as risk factors.

  11. Cyclic behavior of 316L steel predicted by means of finite element computations

    International Nuclear Information System (INIS)

    Liu, J.; Sauzay, M.; Robertson, C.; Liu, J.

    2011-01-01

    The cyclic behavior of 316L steels is predicted based on crystalline elastoplastic constitutive laws. Calculations are performed with the finite element software CAST3M, using a polycrystalline mesh where the individual grains are modeled as cubes, having random crystallographic orientations. At the grain scale, the constitutive law parameters are adjusted using single crystal cyclic stress strain curves (CSSCs) from literature. Calculations are performed for different loading conditions (uniaxial tension-compression, biaxial tension-compression and alternated torsion) and a large range of three remote plastic strain amplitudes. We obtained 3 close macroscopic CSSCs. Somewhat lower stresses are obtained in torsion, particularly at high plastic strain amplitude. Our results are in agreement with all the published experimental data. The mean plastic strain is computed in each grain, yielding a particular polycrystalline mean grain plastic strain distribution for each loading condition and remote plastic strain. The plastic strain scatter increases for decreasing macroscopic strains. The number of cycles to the first micro-crack initiation corresponding to the aforesaid plastic strain distributions is then calculated using a surface roughness based initiation criterion. The effect of the different loading conditions is finally discussed. (authors)

  12. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel

    2006-01-01

    Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...

  13. Prediction based chaos control via a new neural network

    International Nuclear Information System (INIS)

    Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

    2008-01-01

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

  14. Moment based model predictive control for systems with additive uncertainty

    NARCIS (Netherlands)

    Saltik, M.B.; Ozkan, L.; Weiland, S.; Ludlage, J.H.A.

    2017-01-01

    In this paper, we present a model predictive control (MPC) strategy based on the moments of the state variables and the cost functional. The statistical properties of the state predictions are calculated through the open loop iteration of dynamics and used in the formulation of MPC cost function. We

  15. Facial Width-to-Height Ratio Does Not Predict Self-Reported Behavioral Tendencies

    OpenAIRE

    Kosinski, Michal

    2017-01-01

    A growing number of studies have linked facial width-to-height ratio (fWHR) with various antisocial or violent behavioral tendencies. However, those studies have predominantly been laboratory based and low powered. This work reexamined the links between fWHR and behavioral tendencies in a large sample of 137,163 participants. Behavioral tendencies were measured using 55 well-established psychometric scales, including self-report scales measuring intelligence, domains and facets of the five-fa...

  16. When do normative beliefs about aggression predict aggressive behavior? An application of I3 theory.

    Science.gov (United States)

    Li, Jian-Bin; Nie, Yan-Gang; Boardley, Ian D; Dou, Kai; Situ, Qiao-Min

    2015-01-01

    I(3) theory assumes that aggressive behavior is dependent on three orthogonal processes (i.e., Instigator, Impellance, and Inhibition). Previous studies showed that Impellance (trait aggressiveness, retaliation tendencies) better predicted aggression when Instigator was strong and Inhibition was weak. In the current study, we predicted that another Impellance (i.e., normative beliefs about aggression) might predict aggression when Instigator was absent and Inhibition was high (i.e., the perfect calm proposition). In two experiments, participants first completed the normative beliefs about aggression questionnaire. Two weeks later, participants' self-control resources were manipulated either using the Stroop task (study 1, N = 148) or through an "e-crossing" task (study 2, N = 180). Afterwards, with or without being provoked, participants played a game with an ostensible partner where they had a chance to aggress against them. Study 1 found that normative beliefs about aggression negatively and significantly predicted aggressive behavior only when provocation was absent and self-control resources were not depleted. In Study 2, normative beliefs about aggression negatively predicted aggressive behavior at marginal significance level only in the "no-provocation and no-depletion" condition. In conclusion, the current study provides partial support for the perfect calm proposition and I(3) theory. © 2015 Wiley Periodicals, Inc.

  17. Automated Cognitive Health Assessment From Smart Home-Based Behavior Data.

    Science.gov (United States)

    Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

    2016-07-01

    Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behavior in the home and predicting clinical scores of the residents. To accomplish this goal, we propose a clinical assessment using activity behavior (CAAB) approach to model a smart home resident's daily behavior and predict the corresponding clinical scores. CAAB uses statistical features that describe characteristics of a resident's daily activity performance to train machine learning algorithms that predict the clinical scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years. We obtain a statistically significant correlation ( r=0.72) between CAAB-predicted and clinician-provided cognitive scores and a statistically significant correlation ( r=0.45) between CAAB-predicted and clinician-provided mobility scores. These prediction results suggest that it is feasible to predict clinical scores using smart home sensor data and learning-based data analysis.

  18. What Predicts Online Health Information-Seeking Behavior Among Egyptian Adults? A Cross-Sectional Study.

    Science.gov (United States)

    Ghweeba, Mayada; Lindenmeyer, Antje; Shishi, Sobhi; Abbas, Mostafa; Waheed, Amani; Amer, Shaymaa

    2017-06-22

    Over the last decade, the Internet has become an important source of health-related information for a wide range of users worldwide. Yet, little is known about the personal characteristics of Egyptian Internet users who search for online health information (OHI). The aim of the study was to identify the personal characteristics of Egyptian OHI seekers and to determine any associations between their personal characteristics and their health information-seeking behavior.  This cross-sectional questionnaire study was conducted from June to October 2015. A Web-based questionnaire was sent to Egyptian users aged 18 years and older (N=1400) of a popular Arabic-language health information website. The questionnaire included (1) demographic characteristics; (2) self-reported general health status; and (3) OHI-seeking behavior that included frequency of use, different topics sought, and self-reported impact of obtained OHI on health behaviors. Data were analyzed using descriptive statistics and multiple regression analysis. A total of 490 participants completed the electronic questionnaire with a response rate equivalent to 35.0% (490/1400). Regarding personal characteristics, 57.1% (280/490) of participants were females, 63.4% (311/490) had a university level qualification, and 37.1% (182/490) had a chronic health problem. The most commonly sought OHI by the participants was nutrition-related. Results of the multiple regression analysis showed that 31.0% of the variance in frequency of seeking OHI among Egyptian adults can be predicted by personal characteristics. Participants who sought OHI more frequently were likely to be female, of younger age, had higher education levels, and good self-reported general health. Our results provide insights into personal characteristics and OHI-seeking behaviors of Egyptian OHI users. This will contribute to better recognize their needs, highlight ways to increase the availability of appropriate OHI, and may lead to the

  19. An extension of the theory of planned behavior to predict pedestrians' violating crossing behavior using structural equation modeling.

    Science.gov (United States)

    Zhou, Hongmei; Romero, Stephanie Ballon; Qin, Xiao

    2016-10-01

    This paper aimed to examine pedestrians' self-reported violating crossing behavior intentions by applying the theory of planned behavior (TPB). We studied the behavior intentions regarding instrumental attitude, subjective norm, perceived behavioral control, the three basic components of TPB, and extended the theory by adding new factors including descriptive norm, perceived risk and conformity tendency to evaluate their respective impacts on pedestrians' behavior intentions. A questionnaire presented with a scenario that pedestrians crossed the road violating the pedestrian lights at an intersection was designed, and the survey was conducted in Dalian, China. Based on the 260 complete and valid responses, reliability and validity of the data for each question was evaluated. The data were then analyzed by using the structural equation modeling (SEM). The results showed that people had a negative attitude toward the behavior of violating road-crossing rules; they perceived social influences from their family and friends; and they believed that this kind of risky behavior would potentially harm them in a traffic accident. The results also showed that instrumental attitude and subjective norm were significant in the basic TPB model. After adding descriptive norm, subjective norm was no more significant. Other models showed that conformity tendency was a strong predictor, indicating that the presence of other pedestrians would influence behavioral intention. The findings could help to design more effective interventions and safety campaigns, such as changing people's attitude toward this violation behavior, correcting the social norms, increasing their safety awareness, etc. in order to reduce pedestrians' road crossing violations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Updating biological bases of social behavior.

    Science.gov (United States)

    O'Connor, Thomas G

    2014-09-01

    This month's collation of papers deals with social behaviors that operationalize key constructs in fields covered by the journal, including attachment theory and parenting; emotional regulation; psychopathology of several forms; general and specific cognitive abilities. Notably, many examples are offered of how these social behaviors link with biology. That is an obvious and important direction for clinical research insofar as it helps to erase a perceptual chasm and artificial duality between 'behavior' and 'biology'. But, although it must be the case that social behavior has biological connections of one sort or other, identifying reliable connections with practical application has proved to be a non-trivial challenge. In particular, the challenge seems to be in measuring social behavior meaningfully enough that it could be expected to have a biological pulse, and in measuring biological markers systematically enough that emergent-downstream effects would surface. Associations are not especially uncommon, but it has been a frustrating task in constructing a practically broad model from a bricolage of scattered and disconnected parts and findings in the literature. Several reports in this issue offer contrasts that may help move along this line of study. © 2014 Association for Child and Adolescent Mental Health.

  1. Using the Theory of Planned Behavior to predict intention to comply with a food recall message.

    Science.gov (United States)

    Freberg, Karen

    2013-01-01

    The Theory of Planned Behavior (TPB) has provided considerable insight into the public's intention to comply with many different health-related messages, but has not been applied previously to intention to comply with food safety recommendations and recalls ( Hallman & Cuite, 2010 ). Because food recalls can differ from other health messages in their urgency, timing, and cessation, the applicability of the TPB in this domain is unknown. The research reported here attempted to address this gap using a nationally representative consumer panel. Results showed that, consistent with the theory's predictions, attitudes and subjective norms were predictive of the intention to comply with a food recall message, with attitudes having a much greater impact on intent to comply than subjective norms. Perceived behavioral control failed to predict intention to comply. Implications of these results for health public relations and crisis communications and recommendations for future research were discussed.

  2. A predictive model for the behavior of radionuclides in lake systems

    International Nuclear Information System (INIS)

    Monte, L.

    1993-01-01

    This paper describes a predictive model for the behavior of 137Cs in lacustrine systems. The model was tested by comparing its predictions to contamination data collected in various lakes in Europe and North America. The migration of 137Cs from catchment basin and from bottom sediments to lake water was discussed in detail; these two factors influence the time behavior of contamination in lake water. The contributions to the levels of radionuclide concentrations in water, due to the above factors, generally increase in the long run. The uncertainty of the model, used as a generic tool for prediction of the levels of contamination in lake water, was evaluated. Data sets of water contamination analyzed in the present work suggest that the model uncertainty, at a 68% confidence level, is a factor 1.9

  3. Perceived extrinsic mortality risk and reported effort in looking after health: testing a behavioral ecological prediction.

    Science.gov (United States)

    Pepper, Gillian V; Nettle, Daniel

    2014-09-01

    Socioeconomic gradients in health behavior are pervasive and well documented. Yet, there is little consensus on their causes. Behavioral ecological theory predicts that, if people of lower socioeconomic position (SEP) perceive greater personal extrinsic mortality risk than those of higher SEP, they should disinvest in their future health. We surveyed North American adults for reported effort in looking after health, perceived extrinsic and intrinsic mortality risks, and measures of SEP. We examined the relationships between these variables and found that lower subjective SEP predicted lower reported health effort. Lower subjective SEP was also associated with higher perceived extrinsic mortality risk, which in turn predicted lower reported health effort. The effect of subjective SEP on reported health effort was completely mediated by perceived extrinsic mortality risk. Our findings indicate that perceived extrinsic mortality risk may be a key factor underlying SEP gradients in motivation to invest in future health.

  4. Effects of passengers on bus driver celeration behavior and incident prediction.

    Science.gov (United States)

    Af Wåhlberg, A E

    2007-01-01

    Driver celeration (speed change) behavior of bus drivers has previously been found to predict their traffic incident involvement, but it has also been ascertained that the level of celeration is influenced by the number of passengers carried as well as other traffic density variables. This means that the individual level of celeration is not as well estimated as could be the case. Another hypothesized influence of the number of passengers is that of differential quality of measurements, where high passenger density circumstances are supposed to yield better estimates of the individual driver component of celeration behavior. Comparisons were made between different variants of the celeration as predictor of traffic incidents of bus drivers. The number of bus passengers was held constant, and cases identified by their number of passengers per kilometer during measurement were excluded (in 12 samples of repeated measurements). After holding passengers constant, the correlations between celeration behavior and incident record increased very slightly. Also, the selective prediction of incident record of those drivers who had had many passengers when measured increased the correlations even more. The influence of traffic density variables like the number of passengers have little direct influence on the predictive power of celeration behavior, despite the impact upon absolute celeration level. Selective prediction on the other hand increased correlations substantially. This unusual effect was probably due to how the individual propensity for high or low celeration driving was affected by the number of stops made and general traffic density; differences between drivers in this respect were probably enhanced by the denser traffic, thus creating a better estimate of the theoretical celeration behavior parameter C. The new concept of selective prediction was discussed in terms of making estimates of the systematic differences in quality of the individual driver data.

  5. Slope Deformation Prediction Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei JIA

    2013-07-01

    Full Text Available This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM. In the prediction process,explore how to reconstruct the phase space. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.

  6. Trojan detection model based on network behavior analysis

    International Nuclear Information System (INIS)

    Liu Junrong; Liu Baoxu; Wang Wenjin

    2012-01-01

    Based on the analysis of existing Trojan detection technology, this paper presents a Trojan detection model based on network behavior analysis. First of all, we abstract description of the Trojan network behavior, then according to certain rules to establish the characteristic behavior library, and then use the support vector machine algorithm to determine whether a Trojan invasion. Finally, through the intrusion detection experiments, shows that this model can effectively detect Trojans. (authors)

  7. Protein-Based Urine Test Predicts Kidney Transplant Outcomes

    Science.gov (United States)

    ... News Releases News Release Thursday, August 22, 2013 Protein-based urine test predicts kidney transplant outcomes NIH- ... supporting development of noninvasive tests. Levels of a protein in the urine of kidney transplant recipients can ...

  8. Microbial-Host Co-metabolites Are Prodromal Markers Predicting Phenotypic Heterogeneity in Behavior, Obesity, and Impaired Glucose Tolerance

    Directory of Open Access Journals (Sweden)

    Marc-Emmanuel Dumas

    2017-07-01

    Full Text Available The influence of the gut microbiome on metabolic and behavioral traits is widely accepted, though the microbiome-derived metabolites involved remain unclear. We carried out untargeted urine 1H-NMR spectroscopy-based metabolic phenotyping in an isogenic C57BL/6J mouse population (n = 50 and show that microbial-host co-metabolites are prodromal (i.e., early markers predicting future divergence in metabolic (obesity and glucose homeostasis and behavioral (anxiety and activity outcomes with 94%–100% accuracy. Some of these metabolites also modulate disease phenotypes, best illustrated by trimethylamine-N-oxide (TMAO, a product of microbial-host co-metabolism predicting future obesity, impaired glucose tolerance (IGT, and behavior while reducing endoplasmic reticulum stress and lipogenesis in 3T3-L1 adipocytes. Chronic in vivo TMAO treatment limits IGT in HFD-fed mice and isolated pancreatic islets by increasing insulin secretion. We highlight the prodromal potential of microbial metabolites to predict disease outcomes and their potential in shaping mammalian phenotypic heterogeneity.

  9. Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

    Directory of Open Access Journals (Sweden)

    Moumita Saha

    2016-01-01

    Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

  10. Predicting response to incretin-based therapy

    Directory of Open Access Journals (Sweden)

    Agrawal N

    2011-04-01

    Full Text Available Sanjay Kalra1, Bharti Kalra2, Rakesh Sahay3, Navneet Agrawal41Department of Endocrinology, 2Department of Diabetology, Bharti Hospital, Karnal, India; 3Department of Endocrinology, Osmania Medical College, Hyderabad, India; 4Department of Medicine, GR Medical College, Gwalior, IndiaAbstract: There are two important incretin hormones, glucose-dependent insulin tropic polypeptide (GIP and glucagon-like peptide-1 (GLP-1. The biological activities of GLP-1 include stimulation of glucose-dependent insulin secretion and insulin biosynthesis, inhibition of glucagon secretion and gastric emptying, and inhibition of food intake. GLP-1 appears to have a number of additional effects in the gastrointestinal tract and central nervous system. Incretin based therapy includes GLP-1 receptor agonists like human GLP-1 analogs (liraglutide and exendin-4 based molecules (exenatide, as well as DPP-4 inhibitors like sitagliptin, vildagliptin and saxagliptin. Most of the published studies showed a significant reduction in HbA1c using these drugs. A critical analysis of reported data shows that the response rate in terms of target achievers of these drugs is average. One of the first actions identified for GLP-1 was the glucose-dependent stimulation of insulin secretion from islet cell lines. Following the detection of GLP-1 receptors on islet beta cells, a large body of evidence has accumulated illustrating that GLP-1 exerts multiple actions on various signaling pathways and gene products in the ß cell. GLP-1 controls glucose homeostasis through well-defined actions on the islet ß cell via stimulation of insulin secretion and preservation and expansion of ß cell mass. In summary, there are several factors determining the response rate to incretin therapy. Currently minimal clinical data is available to make a conclusion. Key factors appear to be duration of diabetes, obesity, presence of autonomic neuropathy, resting energy expenditure, plasma glucagon levels and

  11. Using the Theory of Planned Behavior and Cheating Justifications to Predict Academic Misconduct

    Science.gov (United States)

    Stone, Thomas H.; Jawahar, I. M.; Kisamore, Jennifer L.

    2009-01-01

    Purpose: The purpose of this paper is to show that academic misconduct appears to be on the rise; some research has linked academic misconduct to unethical workplace behaviors. Unlike previous empirically-driven research, this theory-based study seeks to examine the usefulness of a modification of Ajzen's theory of planned behavior to predict…

  12. Modeling and simulation of adaptive Neuro-fuzzy based intelligent system for predictive stabilization in structured overlay networks

    Directory of Open Access Journals (Sweden)

    Ramanpreet Kaur

    2017-02-01

    Full Text Available Intelligent prediction of neighboring node (k well defined neighbors as specified by the dht protocol dynamism is helpful to improve the resilience and can reduce the overhead associated with topology maintenance of structured overlay networks. The dynamic behavior of overlay nodes depends on many factors such as underlying user’s online behavior, geographical position, time of the day, day of the week etc. as reported in many applications. We can exploit these characteristics for efficient maintenance of structured overlay networks by implementing an intelligent predictive framework for setting stabilization parameters appropriately. Considering the fact that human driven behavior usually goes beyond intermittent availability patterns, we use a hybrid Neuro-fuzzy based predictor to enhance the accuracy of the predictions. In this paper, we discuss our predictive stabilization approach, implement Neuro-fuzzy based prediction in MATLAB simulation and apply this predictive stabilization model in a chord based overlay network using OverSim as a simulation tool. The MATLAB simulation results present that the behavior of neighboring nodes is predictable to a large extent as indicated by the very small RMSE. The OverSim based simulation results also observe significant improvements in the performance of chord based overlay network in terms of lookup success ratio, lookup hop count and maintenance overhead as compared to periodic stabilization approach.

  13. Effects of Nonverbal Behavior on Perceptions of Power Bases.

    Science.gov (United States)

    Aguinis, Herman; Simonsen, Melissa M.; Pierce, Charles A.

    1998-01-01

    Manipulates three types of nonverbal behaviors and examines their effects on perceptions of power bases. Reports that a relaxed facial expression increased the ratings for five of the selected power bases; furthermore, direct eye contact yielded higher credibility ratings. Provides evidence that various nonverbal behaviors have only additive…

  14. Behavioral Activation Is an Evidence-Based Treatment for Depression

    Science.gov (United States)

    Sturmey, Peter

    2009-01-01

    Recent reviews of evidence-based treatment for depression did not identify behavioral activation as an evidence-based practice. Therefore, this article conducted a systematic review of behavioral activation treatment of depression, which identified three meta-analyses, one recent randomized controlled trial and one recent follow-up of an earlier…

  15. Microarray-based cancer prediction using soft computing approach.

    Science.gov (United States)

    Wang, Xiaosheng; Gotoh, Osamu

    2009-05-26

    One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

  16. Forecasting Reading Anxiety for Promoting English-Language Reading Performance Based on Reading Annotation Behavior

    Science.gov (United States)

    Chen, Chih-Ming; Wang, Jung-Ying; Chen, Yong-Ting; Wu, Jhih-Hao

    2016-01-01

    To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners' reading annotation behavior in a collaborative digital reading annotation system (CDRAS). In…

  17. Deep-Learning-Based Approach for Prediction of Algal Blooms

    Directory of Open Access Journals (Sweden)

    Feng Zhang

    2016-10-01

    Full Text Available Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented—a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.

  18. Modeling of Complex Life Cycle Prediction Based on Cell Division

    Directory of Open Access Journals (Sweden)

    Fucheng Zhang

    2017-01-01

    Full Text Available Effective fault diagnosis and reasonable life expectancy are of great significance and practical engineering value for the safety, reliability, and maintenance cost of equipment and working environment. At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. Most of them need to be based on a large amount of data to achieve the problem. For this issue, we propose learning from the mechanism of cell division in the organism. We have established a moderate complexity of life prediction model across studying the complex multifactor correlation life model. In this paper, we model the life prediction of cell division. Experiments show that our model can effectively simulate the state of cell division. Through the model of reference, we will use it for the equipment of the complex life prediction.

  19. Number of Psychosocial Strengths Predicts Reduced HIV Sexual Risk Behaviors Above and Beyond Syndemic Problems Among Gay and Bisexual Men.

    Science.gov (United States)

    Hart, Trevor A; Noor, Syed W; Adam, Barry D; Vernon, Julia R G; Brennan, David J; Gardner, Sandra; Husbands, Winston; Myers, Ted

    2017-10-01

    Syndemics research shows the additive effect of psychosocial problems on high-risk sexual behavior among gay and bisexual men (GBM). Psychosocial strengths may predict less engagement in high-risk sexual behavior. In a study of 470 ethnically diverse HIV-negative GBM, regression models were computed using number of syndemic psychosocial problems, number of psychosocial strengths, and serodiscordant condomless anal sex (CAS). The number of syndemic psychosocial problems correlated with serodiscordant CAS (RR = 1.51, 95% CI 1.18-1.92; p = 0.001). When adding the number of psychosocial strengths to the model, the effect of syndemic psychosocial problems became non-significant, but the number of strengths-based factors remained significant (RR = 0.67, 95% CI 0.53-0.86; p = 0.002). Psychosocial strengths may operate additively in the same way as syndemic psychosocial problems, but in the opposite direction. Consistent with theories of resilience, psychosocial strengths may be an important set of variables predicting sexual risk behavior that is largely missing from the current HIV behavioral literature.

  20. Adolescent anabolic/androgenic steroids: Aggression and anxiety during exposure predict behavioral responding during withdrawal in Syrian hamsters (Mesocricetus auratus).

    Science.gov (United States)

    Ricci, Lesley A; Morrison, Thomas R; Melloni, Richard H

    2013-11-01

    In the U.S. and worldwide anabolic/androgenic steroid use remains high in the adolescent population. This is concerning given that anabolic/androgenic steroid use is associated with a higher incidence of aggressive behavior during exposure and anxiety during withdrawal. This study uses pubertal Syrian hamsters (Mesocricetus auratus) to investigate the hypothesis that an inverse behavioral relationship exists between anabolic/androgenic steroid-induced aggression and anxiety across adolescent exposure and withdrawal. In the first experiment, we examined aggression and anxiety during adolescent anabolic/androgenic steroid exposure and withdrawal. Adolescent anabolic/androgenic steroid administration produced significant increases in aggression and decreases in anxiety during the exposure period followed by significant decreases in aggression and increases in anxiety during anabolic/androgenic steroid withdrawal. In a second experiment, anabolic/androgenic steroid exposed animals were separated into groups based on their aggressive response during the exposure period and then tested for anxiety during exposure and then for both aggression and anxiety during withdrawal. Data were analyzed using a within-subjects repeated measures predictive analysis. Linear regression analysis revealed that the difference in aggressive responding between the anabolic/androgenic steroid exposure and withdrawal periods was a significant predictor of differences in anxiety for both days of testing. Moreover, the combined data suggest that the decrease in aggressive behavior from exposure to withdrawal predicts an increase in anxiety-like responding within these same animals during this time span. Together these findings indicate that early anabolic/androgenic steroid exposure has potent aggression- and anxiety-eliciting effects and that these behavioral changes occur alongside a predictive relationship that exists between these two behaviors over time. © 2013.

  1. An Empirical Test of an Expanded Version of the Theory of Planned Behavior in Predicting Recycling Behavior on Campus

    Science.gov (United States)

    Largo-Wight, Erin; Bian, Hui; Lange, Lori

    2012-01-01

    Background: The study and promotion of environmental health behaviors, such as recycling, is an emerging focus in public health. Purpose: This study was designed to examine the determinants of recycling intention on a college campus. Methods: Undergraduate students (N=189) completed a 35-item web-based survey past findings and an expanded version…

  2. Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs

    Directory of Open Access Journals (Sweden)

    Sungjun Lee

    2016-01-01

    Full Text Available Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user’s next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user’s past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user’s next places than the previous approaches considered in most cases.

  3. Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs.

    Science.gov (United States)

    Lee, Sungjun; Lim, Junseok; Park, Jonghun; Kim, Kwanho

    2016-01-23

    Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user's next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user's past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user's next places than the previous approaches considered in most cases.

  4. Can the MRI signal of aggressive fibromatosis be used to predict its behavior?

    International Nuclear Information System (INIS)

    Castellazzi, G.; Vanel, D.; Le Cesne, A.; Le Pechoux, C.; Caillet, H.; Perona, F.; Bonvalot, S.

    2009-01-01

    Purpose: Aggressive fibromatosis is an invasive non-metastasizing soft-tissue tumor. Until recently, the standard treatment combined surgery and radiation therapy, but new studies reported that conservative strategies with or without medical treatment could be the best management. The aim of this study was to analyze and correlate the size and MR imaging signal features of aggressive fibromatosis with its behavior in order to choose the best treatment. Materials and methods: Between March 1985 and December 2005, 27 patients with at least 2 consecutive MRI examinations and no surgery or radiation therapy in between were recorded. There were 9 men and 18 women, and median age was 31 years. They underwent 107 MRI examinations of 47 lesions, 29 of which were medically treated, while the remaining 18 did not receive any drug administration. The size and signal changes of each lesion were studied over time on T2- and/or T1-weighted sequences after injection of contrast medium. RECIST criteria were used for size: only a 30% decrease or a 20% increase in the size of the main dimension was considered significant. We classified the appearance of the signal into six categories in order of increasing intensity and then we established the related variations over time. Results: The size of 79% of the lesions in the treated group and 82% in the untreated group remained stable. The initial signal of stable lesions or those exhibiting an increase in size was most frequently high. There was a high rate of signal stability over time, whatever the initial signal and size changes. Changes in size were not correlated with the initial MR signal. A decrease in size associated with a decreased signal was observed in three cases exclusively in the treated group. Conclusion: Fibromatoses are a group of soft-tissue tumors with variable characteristics on MRI, but it is not possible to predict their behavior based on the MRI signal

  5. Applying an extended theory of planned behavior to predicting violations at automated railroad crossings.

    Science.gov (United States)

    Palat, Blazej; Paran, Françoise; Delhomme, Patricia

    2017-01-01

    Based on an extended Theory of Planned Behavior (TPB, Ajzen, 1985, 1991), we conducted surveys in order to explain and predict violations at a railroad crossing, among pedestrians (n=153) and car drivers (n=151). Measures were made with respect to three chronologically related railroad crossing situations that varied in risk level. The situations were described in scenarios and depicted on photographs. The participants were recruited in the suburbs of Paris, at two automated railroad crossings with four half-barriers. We found that the pedestrians had stronger crossing intentions than did car drivers, especially at the more congested crossing of the two under study. For both categories of road users, intentions and the amount of intention variance explained by the extended TPB factors decreased significantly with risk level. In the most dangerous situations, risk-taking was the most unlikely and the least predictable Self-reported past frequency of crossing against safety warning devices was the main predictor of the intention to commit this violation again, especially among males, followed by the attitude and the injunctive norm in favor the violation. Moreover, car drivers were influenced in their crossing intentions by the descriptive norm. The presence of another vehicle on the tracks when the safety warning devices were activated was perceived not as facilitating, but as an additional risk factor. The discussion addresses the importance of taking into account these determinants of violations in conceiving countermeasures. Our findings could be especially useful for conceiving risk-communication campaigns. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. D2 receptor genotype and striatal dopamine signaling predict motor cortical activity and behavior in humans.

    Science.gov (United States)

    Fazio, Leonardo; Blasi, Giuseppe; Taurisano, Paolo; Papazacharias, Apostolos; Romano, Raffaella; Gelao, Barbara; Ursini, Gianluca; Quarto, Tiziana; Lo Bianco, Luciana; Di Giorgio, Annabella; Mancini, Marina; Popolizio, Teresa; Rubini, Giuseppe; Bertolino, Alessandro

    2011-02-14

    Pre-synaptic D2 receptors regulate striatal dopamine release and DAT activity, key factors for modulation of motor pathways. A functional SNP of DRD2 (rs1076560 G>T) is associated with alternative splicing such that the relative expression of D2S (mainly pre-synaptic) vs. D2L (mainly post-synaptic) receptor isoforms is decreased in subjects with the T allele with a putative increase of striatal dopamine levels. To evaluate how DRD2 genotype and striatal dopamine signaling predict motor cortical activity and behavior in humans, we have investigated the association of rs1076560 with BOLD fMRI activity during a motor task. To further evaluate the relationship of this circuitry with dopamine signaling, we also explored the correlation between genotype based differences in motor brain activity and pre-synaptic striatal DAT binding measured with [(123)I] FP-CIT SPECT. Fifty healthy subjects, genotyped for DRD2 rs1076560 were studied with BOLD-fMRI at 3T while performing a visually paced motor task with their right hand; eleven of these subjects also underwent [(123)I]FP-CIT SPECT. SPM5 random-effects models were used for statistical analyses. Subjects carrying the T allele had greater BOLD responses in left basal ganglia, thalamus, supplementary motor area, and primary motor cortex, whose activity was also negatively correlated with reaction time at the task. Moreover, left striatal DAT binding and activity of left supplementary motor area were negatively correlated. The present results suggest that DRD2 genetic variation was associated with focusing of responses in the whole motor network, in which activity of predictable nodes was correlated with reaction time and with striatal pre-synaptic dopamine signaling. Our results in humans may help shed light on genetic risk for neurobiological mechanisms involved in the pathophysiology of disorders with dysregulation of striatal dopamine like Parkinson's disease. Copyright © 2010 Elsevier Inc. All rights reserved.

  7. Subjective fear, interference by threat, and fear associations independently predict fear-related behavior in children.

    Science.gov (United States)

    Klein, Anke M; Kleinherenbrink, Annelies V; Simons, Carlijn; de Gier, Erwin; Klein, Steven; Allart, Esther; Bögels, Susan M; Becker, Eni S; Rinck, Mike

    2012-09-01

    Several information-processing models highlight the independent roles of controlled and automatic processes in explaining fearful behavior. Therefore, we investigated whether direct measures of controlled processes and indirect measures of automatic processes predict unique variance components of children's spider fear-related behavior. Seventy-seven children between 8 and 13 years performed an Affective Priming Task (APT) measuring associative bias, a pictorial version of the Emotional Stroop Task (EST) measuring attentional bias, filled out the Spider Anxiety and Disgust Screening for Children (SADS-C) in order to assess self-perceived fear, and took part in a Behavioral Assessment Test (BAT) to measure avoidance of spiders. The SADS-C, EST, and APT did not correlate with each other. Spider fear-related behavior was best explained by SADS-C, APT, and EST together; they explained 51% of the variance in BAT behavior. No children with clinical levels of spider phobia were tested. The direct and the different indirect measures did no correlate with each other. These results indicate that both direct and indirect measures are useful for predicting unique variance components of fear-related behavior in children. The lack of relations between direct and indirect measures may explain why some earlier studies did not find stronger color-naming interference or stronger fear associations in children with high levels of self-reported fear. It also suggests that children with high levels of spider-fearful behavior have different fear-related associations and display higher interference by spider stimuli than children with non-fearful behavior. Copyright © 2012 Elsevier Ltd. All rights reserved.

  8. Predictive Validity of Delay Discounting Behavior in Adolescence: A Longitudinal Twin Study

    Science.gov (United States)

    Isen, Joshua D.; Sparks, Jordan C.; Iacono, William G.

    2014-01-01

    A standard assumption in the delay discounting literature is that individuals who exhibit steeper discounting of hypothetical rewards also experience greater difficulty deferring gratification to real-world rewards. There is ample cross-sectional evidence that delay discounting paradigms reflect a variety of maladaptive psychosocial outcomes, including substance use pathology. We sought to determine whether a computerized assessment of hypothetical delay discounting (HDD) taps into behavioral impulsivity in a community sample of adolescent twins (N = 675). Using a longitudinal design, we hypothesized that greater HDD at age 14–15 predicts real-world impulsive choices and risk for substance use disorders in late adolescence. We also examined the genetic and environmental structure of HDD performance. Individual differences in HDD behavior showed moderate heritability, and were prospectively associated with real-world temporal discounting at age 17–18. Contrary to expectations, HDD was not consistently related to substance use or trait impulsivity. Although a significant association between HDD behavior and past substance use emerged in males, this effect was mediated by cognitive ability. In both sexes, HDD failed to predict a comprehensive index of substance use problems and behavioral disinhibition in late adolescence. In sum, we present some of the first evidence that HDD performance is heritable and predictive of real-world temporal discounting of rewards. Nevertheless, HDD might not serve as a valid marker of substance use disorder risk in younger adolescents, particularly females. PMID:24999868

  9. Aggression, emotional self-regulation, attentional bias, and cognitive inhibition predict risky driving behavior.

    Science.gov (United States)

    Sani, Susan Raouf Hadadi; Tabibi, Zahra; Fadardi, Javad Salehi; Stavrinos, Despina

    2017-12-01

    The present study explored whether aggression, emotional regulation, cognitive inhibition, and attentional bias towards emotional stimuli were related to risky driving behavior (driving errors, and driving violations). A total of 117 applicants for taxi driver positions (89% male, M age=36.59years, SD=9.39, age range 24-62years) participated in the study. Measures included the Ahwaz Aggression Inventory, the Difficulties in emotion regulation Questionnaire, the emotional Stroop task, the Go/No-go task, and the Driving Behavior Questionnaire. Correlation and regression analyses showed that aggression and emotional regulation predicted risky driving behavior. Difficulties in emotion regulation, the obstinacy and revengeful component of aggression, attentional bias toward emotional stimuli, and cognitive inhibition predicted driving errors. Aggression was the only significant predictive factor for driving violations. In conclusion, aggression and difficulties in regulating emotions may exacerbate risky driving behaviors. Deficits in cognitive inhibition and attentional bias toward negative emotional stimuli can increase driving errors. Predisposition to aggression has strong effect on making one vulnerable to violation of traffic rules and crashes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Hemoglobin and hematocrit levels in the prediction of complicated Crohn's disease behavior--a cohort study.

    Science.gov (United States)

    Rieder, Florian; Paul, Gisela; Schnoy, Elisabeth; Schleder, Stephan; Wolf, Alexandra; Kamm, Florian; Dirmeier, Andrea; Strauch, Ulrike; Obermeier, Florian; Lopez, Rocio; Achkar, Jean-Paul; Rogler, Gerhard; Klebl, Frank

    2014-01-01

    Markers that predict the occurrence of a complicated disease behavior in patients with Crohn's disease (CD) can permit a more aggressive therapeutic regimen for patients at risk. The aim of this cohort study was to test the blood levels of hemoglobin (Hgb) and hematocrit (Hct) for the prediction of complicated CD behavior and CD related surgery in an adult patient population. Blood samples of 62 CD patients of the German Inflammatory Bowel Disease-network "Kompetenznetz CED" were tested for the levels of Hgb and Hct prior to the occurrence of complicated disease behavior or CD related surgery. The relation of these markers and clinical events was studied using Kaplan-Meier survival analysis and adjusted COX-proportional hazard regression models. The median follow-up time was 55.8 months. Of the 62 CD patients without any previous complication or surgery 34% developed a complication and/or underwent CD related surgery. Low Hgb or Hct levels were independent predictors of a shorter time to occurrence of the first complication or CD related surgery. This was true for early as well as late occurring complications. Stable low Hgb or Hct during serial follow-up measurements had a higher frequency of complications compared to patients with a stable normal Hgb or Hct, respectively. Determination of Hgb or Hct in complication and surgery naïve CD patients might serve as an additional tool for the prediction of complicated disease behavior.

  11. Predicting behavior change from persuasive messages using neural representational similarity and social network analyses.

    Science.gov (United States)

    Pegors, Teresa K; Tompson, Steven; O'Donnell, Matthew Brook; Falk, Emily B

    2017-08-15

    Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. In-kennel behavior predicts length of stay in shelter dogs.

    Directory of Open Access Journals (Sweden)

    Alexandra Protopopova

    Full Text Available Previous empirical evaluations of training programs aimed at improving dog adoption rates assume that dogs exhibiting certain behaviors are more adoptable. However, no systematic data are available to indicate that the spontaneous behavior of shelter dogs has an effect on adopter preference. The aim of the present study was to determine whether any behaviors that dogs exhibit spontaneously in the presence of potential adopters were associated with the dogs' length of stay in the shelter. A sample of 289 dogs was videotaped for 1 min daily throughout their stay at a county shelter. To account for differences in adopter behavior, experimenters varied from solitary passive observers to pairs of interactive observers. Dogs behaved more attentively to active observers. To account for adopter preference for morphology, dogs were divided into "morphologically preferred" and "non-preferred" groups. Morphologically preferred dogs were small, long coated, ratters, herders, and lap dogs. No theoretically significant differences in behavior were observed between the two different dog morphologies. When accounting for morphological preference, three behaviors were found to have a significant effect on length of stay in all dogs: leaning or rubbing on the enclosure wall (increased median length of stay by 30 days, facing away from the front of the enclosure (increased by 15 days, and standing (increased by 7 days. When combinations of behaviors were assessed, back and forth motion was found to predict a longer stay (increased by 24 days. No consistent behavioral changes were observed due to time spent at the shelter. These findings will allow shelters to focus behavioral modification efforts only on behaviors likely to influence adopters' choices.

  13. Do Dog Behavioral Characteristics Predict the Quality of the Relationship between Dogs and Their Owners?

    Science.gov (United States)

    Hoffman, Christy L.; Chen, Pan; Serpell, James A.; Jacobson, Kristen C.

    2014-01-01

    This paper explores whether dog behavioral characteristics predict the quality of the relationship between dogs and their owners (i.e., owner attachment to dog), and whether relations between dog behavior and owner attachment are moderated by demographic characteristics. In this study, N = 92 children and N = 60 adults from 60 dog-owning families completed questionnaires about their attachment to their pet dog, their level of responsibility for that dog, and their general attitudes toward pets. They also rated their dogs on observable behavioral characteristics. Individuals who held positive attitudes about pets and who provided much of their dog’s care reported stronger attachments to their dogs. The strength of owners’ attachments to their dogs was associated with dog trainability and separation problems. Relationships between owner attachment and both dog excitability and attention-seeking behavior were further moderated by demographic characteristics: for Caucasians but not for non-Caucasians, dog excitability was negatively associated with owner attachment to dog; and for adults, dog attention-seeking behavior was positively associated with owner attachment, but children tended to be highly attached to their dogs, regardless of their dogs’ attention-seeking behaviors. This study demonstrates that certain dog behavioral traits are indeed associated with the strength of owners’ attachments to their dogs. PMID:25685855

  14. Do Dog Behavioral Characteristics Predict the Quality of the Relationship between Dogs and Their Owners?

    Science.gov (United States)

    Hoffman, Christy L; Chen, Pan; Serpell, James A; Jacobson, Kristen C

    This paper explores whether dog behavioral characteristics predict the quality of the relationship between dogs and their owners (i.e., owner attachment to dog), and whether relations between dog behavior and owner attachment are moderated by demographic characteristics. In this study, N = 92 children and N = 60 adults from 60 dog-owning families completed questionnaires about their attachment to their pet dog, their level of responsibility for that dog, and their general attitudes toward pets. They also rated their dogs on observable behavioral characteristics. Individuals who held positive attitudes about pets and who provided much of their dog's care reported stronger attachments to their dogs. The strength of owners' attachments to their dogs was associated with dog trainability and separation problems. Relationships between owner attachment and both dog excitability and attention-seeking behavior were further moderated by demographic characteristics: for Caucasians but not for non-Caucasians, dog excitability was negatively associated with owner attachment to dog; and for adults, dog attention-seeking behavior was positively associated with owner attachment, but children tended to be highly attached to their dogs, regardless of their dogs' attention-seeking behaviors. This study demonstrates that certain dog behavioral traits are indeed associated with the strength of owners' attachments to their dogs.

  15. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

    Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

    2001-01-01

    Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

  16. Meta-path based heterogeneous combat network link prediction

    Science.gov (United States)

    Li, Jichao; Ge, Bingfeng; Yang, Kewei; Chen, Yingwu; Tan, Yuejin

    2017-09-01

    The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

  17. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  18. Online gaming addiction? Motives predict addictive play behavior in massively multiplayer online role-playing games.

    Science.gov (United States)

    Kuss, Daria J; Louws, Jorik; Wiers, Reinout W

    2012-09-01

    Recently, there have been growing concerns about excessive online gaming. Playing Massively Multiplayer Online Role-Playing Games (MMORPGs) appears to be particularly problematic, because these games require a high degree of commitment and time investment from the players to the detriment of occupational, social, and other recreational activities and relations. A number of gaming motives have been linked to excessive online gaming in adolescents and young adults. We assessed 175 current MMORPG players and 90 nonplayers using a Web-based questionnaire regarding their gaming behavior, problems as consequences of gaming, and game motivations and tested their statistical associations. Results indicated that (a) MMORPG players are significantly more likely to experience gaming-related problems relative to nonplayers, and that (b) the gaming motivations escapism and mechanics significantly predicted excessive gaming and appeared as stronger predictors than time investment in game. The findings support the necessity of using measures that distinguish between different types of online games. In addition, this study proves useful regarding the current discussion on establishing (online) gaming addiction as a diagnosis in future categorizations of psychopathology.

  19. Stereotype confirmation concerns predict dropout from cognitive behavioral therapy for social anxiety disorder.

    Science.gov (United States)

    Johnson, Suzanne; Price, Matthew; Mehta, Natasha; Anderson, Page L

    2014-08-19

    There are high attrition rates observed in efficacy studies for social anxiety disorder, and research has not identified consistent nor theoretically meaningful predictors of dropout. Pre-treatment symptom severity and demographic factors, such as age and gender, are sometimes predictive of dropout. The current study examines a theoretically meaningful predictor of attrition based on experiences associated with social group membership rather than differences between social group categories--fear of confirming stereotypes. This is a secondary data analysis of a randomized controlled trial comparing two cognitive behavioral treatments for social anxiety disorder: virtual reality exposure therapy and exposure group therapy. Participants (N = 74) with a primary diagnosis of social anxiety disorder who were eligible to participate in the parent study and who self-identified as either "African American" (n = 31) or "Caucasian" (n = 43) completed standardized self-report measures of stereotype confirmation concerns (SCC) and social anxiety symptoms as part of a pre-treatment assessment battery. Hierarchical logistic regression showed that greater stereotype confirmation concerns were associated with higher dropout from therapy--race, age, gender, and pre-treatment symptom severity were not. Group treatment also was associated with higher dropout. These findings urge further research on theoretically meaningful predictors of attrition and highlight the importance of addressing cultural variables, such as the experience of stereotype confirmation concerns, during treatment of social anxiety to minimize dropout from therapy.

  20. The dopamine receptor D4 gene and familial loading interact with perceived parenting in predicting externalizing behavior problems in early adolescence: the TRacking Adolescents' Individual Lives Survey (TRAILS).

    Science.gov (United States)

    Marsman, Rianne; Oldehinkel, Albertine J; Ormel, Johan; Buitelaar, Jan K

    2013-08-30

    Although externalizing behavior problems show in general a high stability over time, the course of externalizing behavior problems may vary from individual to individual. Our main goal was to investigate the predictive role of parenting on externalizing behavior problems. In addition, we investigated the potential moderating role of gender and genetic risk (operationalized as familial loading of externalizing behavior problems (FLE), and presence or absence of the dopamine receptor D4 (DRD4) 7-repeat and 4-repeat allele, respectively). Perceived parenting (rejection, emotional warmth, and overprotection) and FLE were assessed in a population-based sample of 1768 10- to 12-year-old adolescents. Externalizing behavior problems were assessed at the same age and 212 years later by parent report (CBCL) and self-report (YSR). DNA was extracted from blood samples. Parental emotional warmth predicted lower, and parental overprotection and rejection predicted higher levels of externalizing behavior problems. Whereas none of the parenting factors interacted with gender and the DRD4 7-repeat allele, we did find interaction effects with FLE and the DRD4 4-repeat allele. That is, the predictive effect of parental rejection was only observed in adolescents from low FLE families and the predictive effect of parental overprotection was stronger in adolescents not carrying the DRD4 4-repeat allele. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  1. Recollections of pressure to eat during childhood, but not picky eating, predict young adult eating behavior.

    Science.gov (United States)

    Ellis, Jordan M; Galloway, Amy T; Webb, Rose Mary; Martz, Denise M; Farrow, Claire V

    2016-02-01

    Picky eating is a childhood behavior that vexes many parents and is a symptom in the newer diagnosis of Avoidant/Restrictive Food Intake Disorder (ARFID) in adults. Pressure to eat, a parental controlling feeding practice aimed at encouraging a child to eat more, is associated with picky eating and a number of other childhood eating concerns. Low intuitive eating, an insensitivity to internal hunger and satiety cues, is also associated with a number of problem eating behaviors in adulthood. Whether picky eating and pressure to eat are predictive of young adult eating behavior is relatively unstudied. Current adult intuitive eating and disordered eating behaviors were self-reported by 170 college students, along with childhood picky eating and pressure through retrospective self- and parent reports. Hierarchical regression analyses revealed that childhood parental pressure to eat, but not picky eating, predicted intuitive eating and disordered eating symptoms in college students. These findings suggest that parental pressure in childhood is associated with problematic eating patterns in young adulthood. Additional research is needed to understand the extent to which parental pressure is a reaction to or perhaps compounds the development of problematic eating behavior. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Predicting adolescent perpetration in cyberbullying: an application of the theory of planned behavior.

    Science.gov (United States)

    Heirman, Wannes; Walrave, Michel

    2012-11-01

    This study aims to contribute to the research field on cyberbullying by offering a comprehensive theoretical framework that helps to predict adolescents' perpetration of cyberbullying. One thousand forty-two pupils from 12 to 18 years old in 30 different Belgian secondary schools participated in two surveys within a three-month interval. Structural equation modeling was used to test whether the overall model of theory of planned behavior (TPB) helps to predict adolescents' self-reported perpetration in cyberbullying. Overall, the present study provides strong support for the theoretical utility of the TPB in cyberbullying research. The model accounted for 44.8% of the variance in adolescents' behavioral intention to cyberbully and 33.2% of the variance in self-reported cyberbullying perpetration. We found a strong positive relationship between adolescents' attitude towards cyberbullying and their behavioral intention to perpetrate it. Perceived behavioral control and subjective norm, the other two TPB-constructs, were also significant albeit relatively less important predictors of adolescents' intention to cyberbully. The finding that adolescents' attitude is the most important predictor of perpetration, entails that prevention and intervention strategies should aim at reducing the perceived acceptability of cyberbullying among adolescents by converting neutral or positive attitudes towards this anti-social behavior into negative evaluations.

  3. Perceived Sexual Control, Sex-Related Alcohol Expectancies and Behavior Predict Substance-Related Sexual Revictimization

    Science.gov (United States)

    Walsh, Kate; Messman-Moore, Terri; Zerubavel, Noga; Chandley, Rachel B.; DeNardi, Kathleen A.; Walker, Dave P.

    2013-01-01

    Objectives Although numerous studies have documented linkages between childhood sexual abuse (CSA) and later sexual revictimization, mechanisms underlying revictimization, particularly assaults occurring in the context of substance use, are not well-understood. Consistent with Traumagenic Dynamics theory, the present study tested a path model positing that lowered perceptions of sexual control resulting from CSA may be associated with increased sex-related alcohol expectancies and heightened likelihood of risky sexual behavior, which in turn, may predict adult substance-related rape. Methods Participants were 546 female college students who completed anonymous surveys regarding CSA and adult rape, perceptions of sexual control, sex-related alcohol expectancies, and likelihood of engaging in risky sexual behavior. Results The data fit the hypothesized model well and all hypothesized path coefficients were significant and in the expected directions. As expected, sex-related alcohol expectancies and likelihood of risky sexual behavior only predicted substance-related rape, not forcible rape. Conclusions Findings suggested that low perceived sexual control stemming from CSA is associated with increased sex-related alcohol expectancies and a higher likelihood of engaging in sexual behavior in the context of alcohol use. In turn these proximal risk factors heighten vulnerability to substance-related rape. Programs which aim to reduce risk for substance-related rape could be improved by addressing expectancies and motivations for risky sexual behavior in the context of substance use. Implications and future directions are discussed. PMID:23312991

  4. Factors Predicting the Physical Activity Behavior of Female Adolescents: A Test of the Health Promotion Model

    Directory of Open Access Journals (Sweden)

    Hashem Mohamadian

    2014-01-01

    Full Text Available ObjectivesPhysical activity behavior begins to decline during adolescence and continues to decrease throughout young adulthood. This study aims to explain factors that influence physical activity behavior in a sample of female adolescents using a health promotion model framework.MethodsThis cross-sectional survey was used to explore physical activity behavior among a sample of female adolescents. Participants completed measures of physical activity, perceived self-efficacy, self-esteem, social support, perceived barriers, and perceived affect. Interactions among the variables were examined using path analysis within a covariance modeling framework.ResultsThe final model accounted for an R2 value of 0.52 for physical activity and offered a good model-data fit. The results indicated that physical activity was predicted by self-esteem (β=0.46, p<0.001, perceived self-efficacy (β=0.40, p<0.001, social support (β=0.24, p<0.001, perceived barriers (β=-0.19, p<0.001, and perceived affect (β=0.17, p<0.001.ConclusionsThe findings of this study showed that the health promotion model was useful to predict physical activity behavior among the Iranian female adolescents. Information related to the predictors of physical activity behavior will help researchers plan more tailored culturally relevant health promotion interventions for this population.

  5. NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction.

    Science.gov (United States)

    Pardoe, Heath R; Kuzniecky, Ruben

    2018-01-01

    The availability of cloud computing services has enabled the widespread adoption of the "software as a service" (SaaS) approach for software distribution, which utilizes network-based access to applications running on centralized servers. In this paper we apply the SaaS approach to neuroimaging-based age prediction. Our system, named "NAPR" (Neuroanatomical Age Prediction using R), provides access to predictive modeling software running on a persistent cloud-based Amazon Web Services (AWS) compute instance. The NAPR framework allows external users to estimate the age of individual subjects using cortical thickness maps derived from their own locally processed T1-weighted whole brain MRI scans. As a demonstration of the NAPR approach, we have developed two age prediction models that were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367, age range 6-89 years). The provided age prediction models were trained using (i) relevance vector machines and (ii) Gaussian processes machine learning methods applied to cortical thickness surfaces obtained using Freesurfer v5.3. We believe that this transparent approach to out-of-sample evaluation and comparison of neuroimaging age prediction models will facilitate the development of improved age prediction models and allow for robust evaluation of the clinical utility of these methods.

  6. Prediction of composites behavior undergoing an ATP process through data-mining

    Science.gov (United States)

    Martin, Clara Argerich; Collado, Angel Leon; Pinillo, Rubén Ibañez; Barasinski, Anaïs; Abisset-Chavanne, Emmanuelle; Chinesta, Francisco

    2018-05-01

    The need to characterize composite surfaces for distinct mechanical or physical processes leads to different manners of evaluate the state of the surface. During many manufacturing processes deformation occurs, thus hindering composite classification for fabrication processes. In this work we focus on the challenge of a priori identifying the surfaces' behavior in order to optimize manufacturing. We will propose and validate the curvature of the surface as a reliable parameter and we will develop a tool that allows the prediction of the surface behavior.

  7. The MIDAS touch for Accurately Predicting the Stress-Strain Behavior of Tantalum

    Energy Technology Data Exchange (ETDEWEB)

    Jorgensen, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2016-03-02

    Testing the behavior of metals in extreme environments is not always feasible, so material scientists use models to try and predict the behavior. To achieve accurate results it is necessary to use the appropriate model and material-specific parameters. This research evaluated the performance of six material models available in the MIDAS database [1] to determine at which temperatures and strain-rates they perform best, and to determine to which experimental data their parameters were optimized. Additionally, parameters were optimized for the Johnson-Cook model using experimental data from Lassila et al [2].

  8. Scale Development for Measuring and Predicting Adolescents’ Leisure Time Physical Activity Behavior

    Science.gov (United States)

    Ries, Francis; Romero Granados, Santiago; Arribas Galarraga, Silvia

    2009-01-01

    The aim of this study was to develop a scale for assessing and predicting adolescents’ physical activity behavior in Spain and Luxembourg using the Theory of Planned Behavior as a framework. The sample was comprised of 613 Spanish (boys = 309, girls = 304; M age =15.28, SD =1.127) and 752 Luxembourgish adolescents (boys = 343, girls = 409; M age = 14.92, SD = 1.198), selected from students of two secondary schools in both countries, with a similar socio-economic status. The initial 43-items were all scored on a 4-point response format using the structured alternative format and translated into Spanish, French and German. In order to ensure the accuracy of the translation, standardized parallel back-translation techniques were employed. Following two pilot tests and subsequent revisions, a second order exploratory factor analysis with oblimin direct rotation was used for factor extraction. Internal consistency and test-retest reliabilities were also tested. The 4-week test-retest correlations confirmed the items’ time stability. The same five factors were obtained, explaining 63.76% and 63.64% of the total variance in both samples. Internal consistency for the five factors ranged from α = 0.759 to α = 0. 949 in the Spanish sample and from α = 0.735 to α = 0.952 in the Luxembourgish sample. For both samples, inter-factor correlations were all reported significant and positive, except for Factor 5 where they were significant but negative. The high internal consistency of the subscales, the reported item test-retest reliabilities and the identical factor structure confirm the adequacy of the elaborated questionnaire for assessing the TPB-based constructs when used with a population of adolescents in Spain and Luxembourg. The results give some indication that they may have value in measuring the hypothesized TPB constructs for PA behavior in a cross-cultural context. Key points When using the structured alternative format, weak internal consistency was obtained

  9. Alexander Technique Training Coupled With an Integrative Model of Behavioral Prediction in Teachers With Low Back Pain.

    Science.gov (United States)

    Kamalikhah, Tahereh; Morowatisharifabad, Mohammad Ali; Rezaei-Moghaddam, Farid; Ghasemi, Mohammad; Gholami-Fesharaki, Mohammad; Goklani, Salma

    2016-09-01

    Individuals suffering from chronic low back pain (CLBP) experience major physical, social, and occupational disruptions. Strong evidence confirms the effectiveness of Alexander technique (AT) training for CLBP. The present study applied an integrative model (IM) of behavioral prediction for improvement of AT training. This was a quasi-experimental study of female teachers with nonspecific LBP in southern Tehran in 2014. Group A contained 42 subjects and group B had 35 subjects. In group A, AT lessons were designed based on IM constructs, while in group B, AT lessons only were taught. The validity and reliability of the AT questionnaire were confirmed using content validity (CVR 0.91, CVI 0.96) and Cronbach's α (0.80). The IM constructs of both groups were measured after the completion of training. Statistical analysis used independent and paired samples t-tests and the univariate generalized linear model (GLM). Significant differences were recorded before and after intervention (P < 0.001) for the model constructs of intention, perceived risk, direct attitude, behavioral beliefs, and knowledge in both groups. Direct attitude and behavioral beliefs in group A were higher than in group B after the intervention (P < 0.03). The educational framework provided by IM for AT training improved attitude and behavioral beliefs that can facilitate the adoption of AT behavior and decreased CLBP.

  10. An extension of the Theory of Planned Behavior to predict willingness to pay for the conservation of an urban park.

    Science.gov (United States)

    López-Mosquera, Natalia; García, Teresa; Barrena, Ramo

    2014-03-15

    This paper relates the concept of moral obligation and the components of the Theory of Planned Behavior to determine their influence on the willingness to pay of visitors for park conservation. The sample consists of 190 visitors to an urban Spanish park. The mean willingness to pay estimated was 12.67€ per year. The results also indicated that moral norm was the major factor in predicting behavioral intention, followed by attitudes. The new relations established between the components of the Theory of Planned Behavior show that social norms significantly determine the attitudes, moral norms and perceived behavioral control of individuals. The proportion of explained variance shows that the inclusion of moral norms improves the explanatory power of the original model of the Theory of Planned Behavior (32-40%). Community-based social marketing and local campaigns are the main strategies that should be followed by land managers with the objective of promoting responsible, pro-environmental attitudes as well as a greater willingness to pay for this type of goods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. Prediction of Navigation Satellite Clock Bias Considering Clock's Stochastic Variation Behavior with Robust Least Square Collocation

    Directory of Open Access Journals (Sweden)

    WANG Yupu

    2016-06-01

    Full Text Available In order to better express the characteristic of satellite clock bias (SCB and further improve its prediction precision, a new SCB prediction model is proposed, which can take the physical feature, cyclic variation and stochastic variation behaviors of the space-borne atomic clock into consideration by using a robust least square collocation (LSC method. The proposed model firstly uses a quadratic polynomial model with periodic terms to fit and abstract the trend term and cyclic terms of SCB. Then for the residual stochastic variation part and possible gross errors hidden in SCB data, the model employs a robust LSC method to process them. The covariance function of the LSC is determined by selecting an empirical function and combining SCB prediction tests. Using the final precise IGS SCB products to conduct prediction tests, the results show that the proposed model can get better prediction performance. Specifically, the results' prediction accuracy can enhance 0.457 ns and 0.948 ns respectively, and the corresponding prediction stability can improve 0.445 ns and 1.233 ns, compared with the results of quadratic polynomial model and grey model. In addition, the results also show that the proposed covariance function corresponding to the new model is reasonable.

  12. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non......-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

  13. Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion.

    Science.gov (United States)

    Rosenthal, Sara Brin; Twomey, Colin R; Hartnett, Andrew T; Wu, Hai Shan; Couzin, Iain D

    2015-04-14

    Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.

  14. Predicting change in early adolescent problem behavior in the middle school years: a mesosystemic perspective on parenting and peer experiences.

    Science.gov (United States)

    Véronneau, Marie-Hélène; Dishion, Thomas J

    2010-11-01

    The transition into middle school may be a risky period in early adolescence. In particular, friendships, peer status, and parental monitoring during this developmental period can influence the development of problem behavior. This study examined interrelationships among peer and parenting factors that predict changes in problem behavior over the middle school years. A longitudinal sample (580 boys, 698 girls) was assessed in Grades 6 and 8. Peer acceptance, peer rejection, and their interaction predicted increases in problem behavior. Having high-achieving friends predicted less problem behavior. Parental monitoring predicted less problem behavior in general, but also acted as a buffer for students who were most vulnerable to developing problem behavior on the basis of being well liked by some peers, and also disliked by several others. These findings highlight the importance of studying the family-peer mesosystem when considering risk and resilience in early adolescence, and when considering implications for intervention.

  15. Management of REM sleep behavior disorder: An evidence based review

    OpenAIRE

    Preeti Devnani; Racheal Fernandes

    2015-01-01

    Rapid eye movement (REM) sleep behavior disorder (RBD) is characterized by dream enactment behavior resulting from a loss of REM skeletal muscle atonia. The neurobiology of REM sleep and the characteristic features of REM atonia have an important basis for understanding the aggravating etiologies the proposed pharmacological interventions in its management. This review outlines the evidence for behavioral and therapeutic measures along with evidence-based guidelines for their implementation, ...

  16. Cross-cultural validity of the theory of planned behavior for predicting healthy food choice in secondary school students of Inner Mongolia.

    Science.gov (United States)

    Shimazaki, Takashi; Bao, Hugejiletu; Deli, Geer; Uechi, Hiroaki; Lee, Ying-Hua; Miura, Kayo; Takenaka, Koji

    2017-11-01

    Unhealthy eating behavior is a serious health concern among secondary school students in Inner Mongolia. To predict their healthy food choices and devise methods of correcting unhealthy choices, we sought to confirm the cross-cultural validity of the theory of planned behavior among Inner Mongolian students. A cross-sectional study, conducted between November and December 2014. Overall, 3047 students were enrolled. We devised a questionnaire based on the theory of planned behavior to measure its components (intentions, attitudes, subjective norms, and perceived behavioral control) in relation to healthy food choices; we also assessed their current engagement in healthy food choices. A principal component analysis revealed high contribution rates for the components (69.32%-88.77%). A confirmatory factor analysis indicated that the components of the questionnaire had adequate model fit (goodness of fit index=0.997, adjusted goodness of fit index=0.984, comparative fit index=0.998, and root mean square error of approximation=0.049). Notably, data from participants within the suburbs did not support the theory of planned behavior construction. Several paths did not predict the hypothesis variables. However, attitudes toward healthy food choices strongly predicted behavioral intention (path coefficients 0.49-0.77, ptheory of planned behavior can apply to secondary school students in urban areas. Furthermore, attitudes towards healthy food choices were the best predictor of behavioral intentions to engage in such choices in Inner Mongolian students. Copyright © 2017 Diabetes India. Published by Elsevier Ltd. All rights reserved.

  17. Multi-agent cooperation rescue algorithm based on influence degree and state prediction

    Science.gov (United States)

    Zheng, Yanbin; Ma, Guangfu; Wang, Linlin; Xi, Pengxue

    2018-04-01

    Aiming at the multi-agent cooperative rescue in disaster, a multi-agent cooperative rescue algorithm based on impact degree and state prediction is proposed. Firstly, based on the influence of the information in the scene on the collaborative task, the influence degree function is used to filter the information. Secondly, using the selected information to predict the state of the system and Agent behavior. Finally, according to the result of the forecast, the cooperative behavior of Agent is guided and improved the efficiency of individual collaboration. The simulation results show that this algorithm can effectively solve the cooperative rescue problem of multi-agent and ensure the efficient completion of the task.

  18. Prediction-based Dynamic Energy Management in Wireless Sensor Networks

    Science.gov (United States)

    Wang, Xue; Ma, Jun-Jie; Wang, Sheng; Bi, Dao-Wei

    2007-01-01

    Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

  19. Prediction-based Dynamic Energy Management in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Dao-Wei Bi

    2007-03-01

    Full Text Available Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

  20. Effect of Cognitive-Behavioral-Theory-Based Skill Training on Academic Procrastination Behaviors of University Students

    Science.gov (United States)

    Toker, Betül; Avci, Rasit

    2015-01-01

    This study examined the effectiveness of a cognitive-behavioral theory (CBT) psycho-educational group program on the academic procrastination behaviors of university students and the persistence of any training effect. This was a quasi-experimental research based on an experimental and control group pretest, posttest, and followup test model.…

  1. Empathy and nonattachment independently predict peer nominations of prosocial behavior of adolescents.

    Science.gov (United States)

    Sahdra, Baljinder K; Ciarrochi, Joseph; Parker, Philip D; Marshall, Sarah; Heaven, Patrick

    2015-01-01

    There is a plethora of research showing that empathy promotes prosocial behavior among young people. We examined a relatively new construct in the mindfulness literature, nonattachment, defined as a flexible way of relating to one's experiences without clinging to or suppressing them. We tested whether nonattachment could predict prosociality above and beyond empathy. Nonattachment implies high cognitive flexibility and sufficient mental resources to step out of excessive self-cherishing to be there for others in need. Multilevel Poisson models using a sample of 15-year olds (N = 1831) showed that empathy and nonattachment independently predicted prosocial behaviors of helpfulness and kindness, as judged by same-sex and opposite-sex peers, except for when boys nominated girls. The effects of nonattachment remained substantial in more conservative models including self-esteem and peer nominations of liking.

  2. The Role of Metacognition and Negative Emotions on Prediction of Abuse Behaviors

    Directory of Open Access Journals (Sweden)

    M.A Mohammadyfar

    2014-11-01

    Full Text Available Objective: The aim of present research was determination of the role of metacognition and negative emotions on prediction of abuse behaviors. Method: In correlational research design which is categorized as descriptive research design, 200 participants selected by available sampling in abandonment clinics in Shahrod city. Out of 200 participants, 128 were addicted and 72 were non addicted persons. Metacognition, anxiety, depression, and stress questionnaires were administered among selected samples. Results: The results of regression analysis showed both variables could be significant predictors in prediction of abuse behaviors. Of metacognition subscales, negative believes about not controlling and risk, and cognitive confidence also of negative emotion subscales depression and anxiety were significant predictors. Conclusion: By consideration of results it could be said by intervention of significant variables the probability of suffering of substance abuse and its relapse could be down.

  3. Gender and developmental differences in exercise beliefs among youth and prediction of their exercise behavior.

    Science.gov (United States)

    Garcia, A W; Broda, M A; Frenn, M; Coviak, C; Pender, N J; Ronis, D L

    1995-08-01

    This study examined gender and developmental differences in exercise-related beliefs and exercise behaviors of 286 racially diverse youth and explored factors predictive of exercise. Compared to males, females reported less prior and current exercise, lower self-esteem, poorer health status, and lower exercise self-schema. Adolescents, in contrast to pre-adolescents, reported less social support for exercise and fewer exercise role models. In a path model, gender, the benefits/barriers differential, and access to exercise facilities and programs directly predicted exercise. Effects of grade, perceived health status, exercise self-efficacy, social support for exercise, and social norms for exercise on exercise behavior, were mediated through the benefits/barriers differential. Effect of race on exercise was mediated by access to exercise facilities and programs. Continued exploration of gender and developmental differences in variables influencing physical activity can yield valuable information for tailoring exercise promotion interventions to the unique needs of youth.

  4. Low-complexity Behavioral Model for Predictive Maintenance of Railway Turnouts

    DEFF Research Database (Denmark)

    Barkhordari, Pegah; Galeazzi, Roberto; Tejada, Alejandro de Miguel

    2017-01-01

    together with the Eigensystem Realization Algorithm – a type of subspace identification – to identify a fourth order model of the infrastructure. The robustness and predictive capability of the low-complexity behavioral model to reproduce track responses under different types of train excitations have been......Maintenance of railway infrastructures represents a major cost driver for any infrastructure manager since reliability and dependability must be guaranteed at all times. Implementation of predictive maintenance policies relies on the availability of condition monitoring systems able to assess...... the infrastructure health state. The core of any condition monitoring system is the a-priori knowledge about the process to be monitored, in the form of either mathematical models of different complexity or signal features characterizing the healthy/faulty behavior. This study investigates the identification...

  5. Time-sensitive Customer Churn Prediction based on PU Learning

    OpenAIRE

    Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong

    2018-01-01

    With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...

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

    Directory of Open Access Journals (Sweden)

    Milan Vukićević

    2014-01-01

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

  7. Artificial neural network based particle size prediction of polymeric nanoparticles.

    Science.gov (United States)

    Youshia, John; Ali, Mohamed Ehab; Lamprecht, Alf

    2017-10-01

    Particle size of nanoparticles and the respective polydispersity are key factors influencing their biopharmaceutical behavior in a large variety of therapeutic applications. Predicting these attributes would skip many preliminary studies usually required to optimize formulations. The aim was to build a mathematical model capable of predicting the particle size of polymeric nanoparticles produced by a pharmaceutical polymer of choice. Polymer properties controlling the particle size were identified as molecular weight, hydrophobicity and surface activity, and were quantified by measuring polymer viscosity, contact angle and interfacial tension, respectively. A model was built using artificial neural network including these properties as input with particle size and polydispersity index as output. The established model successfully predicted particle size of nanoparticles covering a range of 70-400nm prepared from other polymers. The percentage bias for particle prediction was 2%, 4% and 6%, for the training, validation and testing data, respectively. Polymer surface activity was found to have the highest impact on the particle size followed by viscosity and finally hydrophobicity. Results of this study successfully highlighted polymer properties affecting particle size and confirmed the usefulness of artificial neural networks in predicting the particle size and polydispersity of polymeric nanoparticles. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Directory of Open Access Journals (Sweden)

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  9. Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning

    OpenAIRE

    Roysden, Nathaniel; Wright, Adam

    2015-01-01

    Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient’s first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection ...

  10. Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning.

    Science.gov (United States)

    Roysden, Nathaniel; Wright, Adam

    2015-01-01

    Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient's first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection factor analysis.

  11. Observed fearlessness and positive parenting interact to predict childhood callous-unemotional behaviors among low-income boys.

    Science.gov (United States)

    Waller, Rebecca; Shaw, Daniel S; Hyde, Luke W

    2017-03-01

    Callous-unemotional behaviors identify children at risk for severe and chronic antisocial behavior. Research is needed to establish pathways from temperament and parenting factors that give rise to callous-unemotional behaviors, including interactions of positive versus harsh parenting with child fearlessness. Multimethod data, including parent reports and observations of parent and child behavior, were drawn from a prospective, longitudinal sample of low-income boys (N = 310) with assessments at 18, 24, and 42 months, and at ages 10-12 years old. Parent-reported callous-unemotional, oppositional, and attention-deficit factors were separable at 42 months. Callous-unemotional behaviors at 42 months predicted callous-unemotional behaviors at ages 10-12, accounting for earlier oppositional and attention-deficit behaviors and self-reported child delinquency at ages 10-12. Observations of fearlessness at 24 months predicted callous-unemotional behaviors at 42 months, but only when parents exhibited low observed levels of positive parenting. The interaction of fearlessness and low positive parenting indirectly predicted callous-unemotional behaviors at 10-12 via callous-unemotional behaviors at 42 months. Early fearlessness interacts with low positive parenting to predict early callous-unemotional behaviors, with lasting effects of this person-by-context interaction on callous-unemotional behaviors into late childhood. © 2016 Association for Child and Adolescent Mental Health.

  12. Predicting Self-Management Behaviors in Familial Hypercholesterolemia Using an Integrated Theoretical Model: the Impact of Beliefs About Illnesses and Beliefs About Behaviors.

    Science.gov (United States)

    Hagger, Martin S; Hardcastle, Sarah J; Hingley, Catherine; Strickland, Ella; Pang, Jing; Watts, Gerald F

    2016-06-01

    Patients with familial hypercholesterolemia (FH) are at markedly increased risk of coronary artery disease. Regular participation in three self-management behaviors, physical activity, healthy eating, and adherence to medication, can significantly reduce this risk in FH patients. We aimed to predict intentions to engage in these self-management behaviors in FH patients using a multi-theory, integrated model that makes the distinction between beliefs about illness and beliefs about self-management behaviors. Using a cross-sectional, correlational design, patients (N = 110) diagnosed with FH from a clinic in Perth, Western Australia, self-completed a questionnaire that measured constructs from three health behavior theories: the common sense model of illness representations (serious consequences, timeline, personal control, treatment control, illness coherence, emotional representations); theory of planned behavior (attitudes, subjective norms, perceived behavioral control); and social cognitive theory (self-efficacy). Structural equation models for each self-management behavior revealed consistent and statistically significant effects of attitudes on intentions across the three behaviors. Subjective norms predicted intentions for health eating only and self-efficacy predicted intentions for physical activity only. There were no effects for the perceived behavioral control and common sense model constructs in any model. Attitudes feature prominently in determining intentions to engage in self-management behaviors in FH patients. The prominence of these attitudinal beliefs about self-management behaviors, as opposed to illness beliefs, suggest that addressing these beliefs may be a priority in the management of FH.

  13. Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

    Science.gov (United States)

    Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

    2018-02-23

    Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

  14. Facial Width-to-Height Ratio Does Not Predict Self-Reported Behavioral Tendencies.

    Science.gov (United States)

    Kosinski, Michal

    2017-11-01

    A growing number of studies have linked facial width-to-height ratio (fWHR) with various antisocial or violent behavioral tendencies. However, those studies have predominantly been laboratory based and low powered. This work reexamined the links between fWHR and behavioral tendencies in a large sample of 137,163 participants. Behavioral tendencies were measured using 55 well-established psychometric scales, including self-report scales measuring intelligence, domains and facets of the five-factor model of personality, impulsiveness, sense of fairness, sensational interests, self-monitoring, impression management, and satisfaction with life. The findings revealed that fWHR is not substantially linked with any of these self-reported measures of behavioral tendencies, calling into question whether the links between fWHR and behavior generalize beyond the small samples and specific experimental settings that have been used in past fWHR research.

  15. Encoded exposure to tobacco use in social media predicts subsequent smoking behavior.

    Science.gov (United States)

    Depue, Jacob B; Southwell, Brian G; Betzner, Anne E; Walsh, Barbara M

    2015-01-01

    Assessing the potential link between smoking behavior and exposure to mass media depictions of smoking on social networking Web sites. A representative longitudinal panel of 200 young adults in Connecticut. Telephone surveys were conducted by using computer assisted telephone interviewing technology and electronic dialing for random digit dialing and listed samples. Connecticut residents aged 18 to 24 years. To measure encoded exposure, respondents were asked whether or not they had smoked a cigarette in the past 30 days and about how often they had seen tobacco use on television, in movies, and in social media content. Respondents were also asked about cigarette use in the past 30 days, and a series of additional questions that have been shown to be predictive of tobacco use. Logistic regression was used to test for our main prediction that reported exposure to social media tobacco depictions at time 1 would influence time 2 smoking behavior. Encoded exposure to social media tobacco depictions (B = .47, p media depictions of tobacco use predict future smoking tendency, over and above the influence of TV and movie depictions of smoking. This is the first known study to specifically assess the role of social media in informing tobacco behavior.

  16. A prediction method for long-term behavior of prestressed concrete containment vessels

    International Nuclear Information System (INIS)

    Ozaki, M.; Abe, T.; Watanabe, Y.; Kato, A.; Yamaguchi, T.; Yamamoto, M.

    1995-01-01

    This paper presents results of studies on the long-term behavior of PCCVs at Taruga Unit No 2 and Ohi Unit No 3/4 power stations. The objective of this study is to evaluate the measured strain in the concrete and reduction force in the tendons, and to establish the prediction methods for long-term PCCVs behavior. Comparing the measured strains with those calculated due to creep and shrinkage of the concrete, those in contrast were investigated. Furthermore, the reduced tendon forces are calculated considering losses in elasticity, relaxation, creep and shrinkage. The measured reduction in the tendon forces is compared with the calculated. Considering changes in temperature and humidity, the measured strains and tendon forces were in good agreement with those calculated. From the above results, it was confirmed that the residual pre stresses in the PCCVs maintain the predicted values at the design stage, and that the prediction method of long-term behaviors has sufficient reliability. (author). 10 refs., 8 figs., 3 tabs

  17. Prediction of Chinese Drivers' Intentions to Park Illegally in Emergency Lanes: An Application of the Theory of Planned Behavior.

    Science.gov (United States)

    Zheng, Yubing; Ma, Yang; Guo, Lixin; Cheng, Jianchuan; Zhang, Yunlong

    2018-06-21

    Illegal parking in emergency lanes (paved highway shoulders) is becoming a serious road safety issue in China. The aim of this study was: 1) to examine the utility of the theory of planned behavior (TPB) extended with descriptive norm, past behavior, facilitating and deterring circumstances, sensation seeking and invulnerability in predicting Chinese drivers' intentions in illegal emergency lane parking; 2) to investigate whether respondents' demographic characteristics would impact their views towards the behavior and predictive patterns of intentions; 3) to identify significant predictors of intentions. In this cross-sectional study, eligible respondents were all qualified Chinese drivers. A self-administered questionnaire was employed to collect data including demographic information, descriptive norm, past behavior, facilitating and deterring circumstances, sensation seeking and scenario-based invulnerability combined with TPB constructs. Descriptive statistics, MANOVAs and a series of hierarchical multiple linear regression analyses were conducted in SPSS. A total of 435 qualified drivers (234 males and 201 females) with a mean age of 35.2 years (S.D.=10.3) were included in analysis. The descriptive analysis showed that most participants reported weak intentions (M = 2.35) to park illegally in emergency lanes with negative attitude (M = 3.19), low perceived support (M = 2.91) and high control (M = 5.08) over the behavior. The model succeeded in explaining 64% of the variance in intentions for the whole sample, and principal TPB components accounted for 21% of variance in intentions after demographic variables were controlled. MANOVAs revealed that significant differences of respondents' opinions towards illegal emergency lane parking were only found between better-educated drivers (with college education background) and less-educated ones. Separate regression analyses revealed that predictive pattern of better-educated participants also

  18. A clinical study of autogenic training-based behavioral treatment for panic disorder.

    Science.gov (United States)

    Sakai, M

    1996-03-01

    The present study investigated the effect of autogenic training-based behavioral treatment for panic disorder and identified the predictors of treatment outcome. Thirty-four patients meeting DSM-III-R criteria for panic disorder received autogenic training-based behavioral treatment from October 1981 to December 1994. They were treated individually by the author. The medical records of the patients were investigated for the purpose of this study. The results showed that this autogenic training-based behavioral treatment had successful results. Fifteen patients were cured, nine much improved, five improved, and five unchanged at the end of the treatment. Improvement trends were found as for the severity of panic attack and the severity of agoraphobic avoidance. No consistent findings about predictors emerged when such pretreatment variables as demographics and severity of symptoms were used to predict the outcome. Also, three treatment variables showed useful predictive power. First, practicing the second standard autogenic training exercise satisfactorily predicted better outcomes. Second, application of in vivo exposure was found to be positively associated with the treatment outcome in patients with agoraphobic avoidance. Third, longer treatment periods were associated with better outcomes. These findings suggested that the autogenic training-based behavioral treatment could provide relief to the majority of panic disorder patients.

  19. A time series based sequence prediction algorithm to detect activities of daily living in smart home.

    Science.gov (United States)

    Marufuzzaman, M; Reaz, M B I; Ali, M A M; Rahman, L F

    2015-01-01

    The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included. The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge. The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED. Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.

  20. Investigations and technical reviews on the reliability of prediction for migration behavior of radionuclides (H15)

    International Nuclear Information System (INIS)

    Tachikawa, Hirokazu

    2004-02-01

    The research plan of the validation on effects of colloids and organic materials drawn up by the Japan Nuclear Fuel Cycle Development Institute and its' research outcome were reviewed comprehensively by an expert committee established in the Nuclear Safety Research Association. Additionally, experimental investigations for the migration behavior of actinide elements and fission products in engineering barrier and natural barrier medias, and for solution chemistry of them were carried out and discussed by the committee, in order to enhance the reliability of prediction for migration behavior of radionuclides. The subjects investigated by the expert committee are as follows: (1) Research on solubility products of An(III) hydroxide. (2) Diffusion and electromigration behavior of plutonium in buffer material. (3) Analysis of the nuclide solubility in compacted bentonite. (4) Survey of the actual contamination by alpha emitters in steel materials. (author)

  1. Artificial neural networks in prediction of mechanical behavior of concrete at high temperature

    International Nuclear Information System (INIS)

    Mukherjee, A.; Nag Biswas, S.

    1997-01-01

    The behavior of concrete structures that are exposed to extreme thermo-mechanical loading is an issue of great importance in nuclear engineering. The mechanical behavior of concrete at high temperature is non-linear. The properties that regulate its response are highly temperature dependent and extremely complex. In addition, the constituent materials, e.g. aggregates, influence the response significantly. Attempts have been made to trace the stress-strain curve through mathematical models and rheological models. However, it has been difficult to include all the contributing factors in the mathematical model. This paper examines a new programming paradigm, artificial neural networks, for the problem. Implementing a feedforward network and backpropagation algorithm the stress-strain relationship of the material is captured. The neural networks for the prediction of uniaxial behavior of concrete at high temperature has been presented here. The results of the present investigation are very encouraging. (orig.)

  2. Predicting health-promoting self-care behaviors in people with pre-diabetes by applying Bandura social learning theory.

    Science.gov (United States)

    Chen, Mei-Fang; Wang, Ruey-Hsia; Hung, Shu-Ling

    2015-11-01

    The aim of this study was to apply Bandura social learning theory in a model for identifying personal and environmental factors that predict health-promoting self-care behaviors in people with pre-diabetes. The theoretical basis of health-promoting self-care behaviors must be examined to obtain evidence-based knowledge that can help improve the effectiveness of pre-diabetes care. However, such behaviors are rarely studied in people with pre-diabetes. This quantitative, cross-sectional survey study was performed in a convenience sample of two hospitals in southern Taiwan. Two hundred people diagnosed with pre-diabetes at a single health examination center were recruited. A questionnaire survey was performed to collect data regarding personal factors (i.e., participant characteristics, pre-diabetes knowledge, and self-efficacy) and data regarding environmental factors (i.e., social support and perceptions of empowerment process) that may have associations with health-promoting self-care behaviors in people with pre-diabetes. Multiple linear regression showed that the factors that had the largest influence on the practice of health-promoting self-care behaviors were self-efficacy, diabetes history, perceptions of empowerment process, and pre-diabetes knowledge. These factors explained 59.3% of the variance in health-promoting self-care behaviors. To prevent the development of diabetes in people with pre-diabetes, healthcare professionals should consider both the personal and the environmental factors identified in this study when assessing health promoting self-care behaviors in patients with pre-diabetes and when selecting the appropriate interventions. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. Bimanual coordination positively predicts episodic memory: A combined behavioral and MRI investigation.

    Science.gov (United States)

    Lyle, Keith B; Dombroski, Brynn A; Faul, Leonard; Hopkins, Robin F; Naaz, Farah; Switala, Andrew E; Depue, Brendan E

    2017-11-01

    Some people remember events more completely and accurately than other people, but the origins of individual differences in episodic memory are poorly understood. One way to advance understanding is by identifying characteristics of individuals that reliably covary with memory performance. Recent research suggests motor behavior is related to memory performance, with individuals who consistently use a single preferred hand for unimanual actions performing worse than individuals who make greater use of both hands. This research has relied on self-reports of behavior. It is unknown whether objective measures of motor behavior also predict memory performance. Here, we tested the predictive power of bimanual coordination, an important form of manual dexterity. Bimanual coordination, as measured objectively on the Purdue Pegboard Test, was positively related to correct recall on the California Verbal Learning Test-II and negatively related to false recall. Furthermore, MRI data revealed that cortical surface area in right lateral prefrontal regions was positively related to correct recall. In one of these regions, cortical thickness was negatively related to bimanual coordination. These results suggest that individual differences in episodic memory may partially reflect morphological variation in right lateral prefrontal cortex and suggest a relationship between neural correlates of episodic memory and motor behavior. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Personality Makes a Difference: Attachment Orientation Moderates Theory of Planned Behavior Prediction of Cardiac Medication Adherence.

    Science.gov (United States)

    Peleg, Shira; Vilchinsky, Noa; Fisher, William A; Khaskia, Abed; Mosseri, Morris

    2017-12-01

    To achieve a comprehensive understanding of patients' adherence to medication following acute coronary syndrome (ACS), we assessed the possible moderating role played by attachment orientation on the effects of attitudes, subjective norms, and perceived behavioral control (PBC), as derived from the Theory of Planned Behavior (TPB; Ajzen, 1991), on intention and reported adherence. A prospective longitudinal design was employed. During hospitalization, ACS male patients (N = 106) completed a set of self-report questionnaires including sociodemographic variables, attachment orientation, and measures of TPB constructs. Six months post-discharge, 90 participants completed a questionnaire measuring adherence to medication. Attachment orientations moderated some of the predictions of the TPB model. PBC predicted intention and reported adherence, but these associations were found to be significant only among individuals with lower, as opposed to higher, attachment anxiety. The association between attitudes and intention was stronger among individuals with higher, as opposed to lower, attachment anxiety. Only among individuals with higher attachment avoidance, subjective norms were negatively associated with intention to take medication. Cognitive variables appear to explain both adherence intention and behavior, but differently, depending on individuals' attachment orientations. Integrating personality and cognitive models may prove effective in understanding patients' health behaviors. © 2016 Wiley Periodicals, Inc.

  5. The Cognitive Processes underlying Affective Decision-making Predicting Adolescent Smoking Behaviors in a Longitudinal Study

    Directory of Open Access Journals (Sweden)

    Lin eXiao

    2013-10-01

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

  6. Which dimension of parenting predicts the change of callous unemotional traits in children with disruptive behavior disorder?

    Science.gov (United States)

    Muratori, Pietro; Lochman, John E; Lai, Elisa; Milone, Annarita; Nocentini, Annalaura; Pisano, Simone; Righini, Elisabetta; Masi, Gabriele

    2016-08-01

    Several studies suggested that in addition to child-driven factors (i.e., temperamental style), parenting behavior can, at least in part, influence the maintenance of Callous Unemotional (CU) traits in children; however, more information is needed to distinguish which styles (negative parenting or lack of positive parenting) predict increased levels of CU traits. The aim of the present treatment study was to examine which components of parenting are longitudinally associated with levels of CU traits in children with a disruptive behavior disorder diagnosis. The current study examined cross-lagged reciprocal effects models between positive and negative parenting practices, and the levels of child CU traits over three time points, including both positive and negative dimensions of parenting in the same model. Participants were 126 Italian children with diagnosis of disruptive behavior disorder (oppositional defiant disorder or conduct disorder), 113 boys and 13 girls, 110 Caucasian, 48 with conduct disorder, and 78 with oppositional defiant disorder, treated with a multi-component intervention, based on cognitive behavioral principles and practices. Participants were all 9-10 years of age at the beginning of the study, and were followed-up until the age of 11-12 years (24 months in total, the first 12 under treatment) using parent report (Alabama Parenting Questionnaire and Child Behavior Check List) and child report (Inventory of Callous-Unemotional Traits) measures. No significant cross-lagged path was found between negative parenting and CU traits; these two variables were also unrelated when positive parenting was considered in the same model. In contrast, reciprocal effects between positive parenting and CU were found: higher levels of positive parenting predicted lower levels of CU traits. The current findings suggest that the positive dimension of parenting may need to be targeted in the treatment of DBD children with higher CU traits. Copyright © 2016

  7. The effect of genealogy-based haplotypes on genomic prediction

    DEFF Research Database (Denmark)

    Edriss, Vahid; Fernando, Rohan L.; Su, Guosheng

    2013-01-01

    on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. Methods A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using...... local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (pi) of the haplotype covariates had zero effect......, i.e. a Bayesian mixture method. Results About 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some...

  8. Girls’ childhood trajectories of disruptive behavior predict adjustment problems in early adolescence

    Science.gov (United States)

    van der Molen, Elsa; Blokland, Arjan A. J.; Hipwell, Alison E.; Vermeiren, Robert R.J.M.; Doreleijers, Theo A.H.; Loeber, Rolf

    2014-01-01

    Background It is widely recognized that early onset of disruptive behavior is linked to a variety of detrimental outcomes in males later in life. In contrast, little is known about the association between girls’ childhood trajectories of disruptive behavior and adjustment problems in early adolescence. Methods The current study used 9 waves of data from the ongoing Pittsburgh Girls Study. A semi-parametric group based model was used to identify trajectories of disruptive behavior in 1,513 girls from age 6 to 12 years. Adjustment problems were characterized by depression, self-harm, PTSD, substance use, interpersonal aggression, sexual behavior, affiliation with delinquent peers, and academic achievement at ages 13 and 14. Results Three trajectories of childhood disruptive behavior were identified: low, medium, and high. Girls in the high group were at increased risk for depression, self-harm, PTSD, illegal substance use, interpersonal aggression, early and risky sexual behavior, and lower academic achievement. The likelihood of multiple adjustment problems increased with trajectories reflecting higher levels of disruptive behavior. Conclusion Girls following the high childhood trajectory of disruptive behavior require early intervention programs to prevent multiple, adverse outcomes in adolescence and further escalation in adulthood. PMID:25302849

  9. A Parameter-based Model for Generating Culturally Adaptive Nonverbal Behaviors in Embodied Conversational Agents

    DEFF Research Database (Denmark)

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

    2009-01-01

    The goal of this paper is to integrate culture as a computational term in embodied conversational agents by employing an empirical data-driven approach as well as a theoretical model-driven approach. We propose a parameter-based model that predicts nonverbal expressions appropriate for specific...... cultures. First, we introduce the Hofstede theory to describe socio-cultural characteristics of each country. Then, based on the previous studies in cultural differences of nonverbal behaviors, we propose expressive parameters to characterize nonverbal behaviors. Finally, by integrating socio-cultural...

  10. Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model.

    Science.gov (United States)

    Xin, Jingzhou; Zhou, Jianting; Yang, Simon X; Li, Xiaoqing; Wang, Yu

    2018-01-19

    Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing

  11. Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

    Directory of Open Access Journals (Sweden)

    Jingzhou Xin

    2018-01-01

    Full Text Available Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA, and generalized autoregressive conditional heteroskedasticity (GARCH. Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS deformation monitoring system demonstrated that: (1 the Kalman filter is capable of denoising the bridge deformation monitoring data; (2 the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3 in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity; the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data

  12. Cognitive Model of Trust Dynamics Predicts Human Behavior within and between Two Games of Strategic Interaction with Computerized Confederate Agents.

    Science.gov (United States)

    Collins, Michael G; Juvina, Ion; Gluck, Kevin A

    2016-01-01

    When playing games of strategic interaction, such as iterated Prisoner's Dilemma and iterated Chicken Game, people exhibit specific within-game learning (e.g., learning a game's optimal outcome) as well as transfer of learning between games (e.g., a game's optimal outcome occurring at a higher proportion when played after another game). The reciprocal trust players develop during the first game is thought to mediate transfer of learning effects. Recently, a computational cognitive model using a novel trust mechanism has been shown to account for human behavior in both games, including the transfer between games. We present the results of a study in which we evaluate the model's a priori predictions of human learning and transfer in 16 different conditions. The model's predictive validity is compared against five model variants that lacked a trust mechanism. The results suggest that a trust mechanism is necessary to explain human behavior across multiple conditions, even when a human plays against a non-human agent. The addition of a trust mechanism to the other learning mechanisms within the cognitive architecture, such as sequence learning, instance-based learning, and utility learning, leads to better prediction of the empirical data. It is argued that computational cognitive modeling is a useful tool for studying trust development, calibration, and repair.

  13. Prediction of elastic and acoustic behaviors of calcarenite used for construction of historical monuments of Rabat, Morocco

    Directory of Open Access Journals (Sweden)

    Abdelaali Rahmouni

    2017-02-01

    Full Text Available Natural materials (e.g. rocks and soils are porous media, whose microstructures present a wide diversity. They generally consist of a heterogeneous solid phase and a porous phase which may be fully or partially saturated with one or more fluids. The prediction of elastic and acoustic properties of porous materials is very important in many fields, such as physics of rocks, reservoir geophysics, civil engineering, construction field and study of the behavior of historical monuments. The aim of this work is to predict the elastic and acoustic behaviors of isotropic porous materials of a solid matrix containing dry, saturated and partially saturated spherical pores. For this, a homogenization technique based on the Mori–Tanaka model is presented to connect the elastic and acoustic properties to porosity and degree of water saturation. Non-destructive ultrasonic technique is used to determine the elastic properties from measurements of P-wave velocities. The results obtained show the influence of porosity and degree of water saturation on the effective properties. The various predictions of Mori–Tanaka model are then compared with experimental results for the elastic and acoustic properties of calcarenite.

  14. Individual-Based Compulsive Sexual Behavior Scale: Its Development and Importance in Examining Compulsive Sexual Behavior.

    Science.gov (United States)

    Efrati, Yaniv; Mikulincer, Mario

    2018-04-03

    Compulsive sexual behavior comprises individual-based (e.g., sexual fantasies, compulsive sexual thoughts, masturbation) and partnered (e.g., interpersonal sexual conquests, repeated infidelity) facets. Most instruments for assessing compulsive sexual behavior, however, focus less on the individual-based facet and specifically on fantasies and compulsive thoughts. In the current research, we developed and validated an individual-based compulsive sexual behavior scale (I-CSB). In Study 1 (N = 492), the factorial structure of the I-CSB was examined. In Study 2 (N = 406), we assessed I-CSB's convergent validity. In Study 3 (N = 112), we examined whether the I-CSB differentiates between individuals who suffer from compulsive sexual behavior and those who do not. Results revealed a four-factor structure for individual-based compulsive sexual behavior that is associated with an intense inner conflict regarding sexuality (high arousal contrasting with high sexual anxiety), and that accounts for approximately 75% of the differences between people with compulsive sexual behavior and controls. Results are discussed in light of the need for a broader understanding of compulsive sexual behavior.

  15. Theory of Planned Behavior Predicts Graduation Intentions of Canadian and Israeli Postsecondary Students with and without Learning Disabilities/Attention Deficit Hyperactivity Disorder

    Science.gov (United States)

    Fichten, Catherine S.; Heiman, Tali; Jorgensen, Mary; Nguyen, Mai Nhu; Havel, Alice; King, Laura; Budd, Jillian; Amsel, Rhonda

    2016-01-01

    We tested the ability of Ajzen's Theory of Planned Behavior (TPB) model to predict intention to graduate among Canadian and Israeli students with and without a learning disability/attention deficit hyperactivity disorder (LD/ADHD). Results based on 1486 postsecondary students show that the model's predictors (i.e., attitude, subjective norms,…

  16. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

  17. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  18. A variable capacitance based modeling and power capability predicting method for ultracapacitor

    Science.gov (United States)

    Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang

    2018-01-01

    Methods of accurate modeling and power capability predicting for ultracapacitors are of great significance in management and application of lithium-ion battery/ultracapacitor hybrid energy storage system. To overcome the simulation error coming from constant capacitance model, an improved ultracapacitor model based on variable capacitance is proposed, where the main capacitance varies with voltage according to a piecewise linear function. A novel state-of-charge calculation approach is developed accordingly. After that, a multi-constraint power capability prediction is developed for ultracapacitor, in which a Kalman-filter-based state observer is designed for tracking ultracapacitor's real-time behavior. Finally, experimental results verify the proposed methods. The accuracy of the proposed model is verified by terminal voltage simulating results under different temperatures, and the effectiveness of the designed observer is proved by various test conditions. Additionally, the power capability prediction results of different time scales and temperatures are compared, to study their effects on ultracapacitor's power capability.

  19. Static Formation Temperature Prediction Based on Bottom Hole Temperature

    Directory of Open Access Journals (Sweden)

    Changwei Liu

    2016-08-01

    Full Text Available Static formation temperature (SFT is required to determine the thermophysical properties and production parameters in geothermal and oil reservoirs. However, it is not easy to determine SFT by both experimental and physical methods. In this paper, a mathematical approach to predicting SFT, based on a new model describing the relationship between bottom hole temperature (BHT and shut-in time, has been proposed. The unknown coefficients of the model were derived from the least squares fit by the particle swarm optimization (PSO algorithm. Additionally, the ability to predict SFT using a few BHT data points (such as the first three, four, or five points of a data set was evaluated. The accuracy of the proposed method to predict SFT was confirmed by a deviation percentage less than ±4% and a high regression coefficient R2 (>0.98. The proposed method could be used as a practical tool to predict SFT in both geothermal and oil wells.

  20. Prediction on carbon dioxide emissions based on fuzzy rules

    Science.gov (United States)

    Pauzi, Herrini; Abdullah, Lazim

    2014-06-01

    There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

  1. Maternal factors predicting cognitive and behavioral characteristics of children with fetal alcohol spectrum disorders.

    Science.gov (United States)

    May, Philip A; Tabachnick, Barbara G; Gossage, J Phillip; Kalberg, Wendy O; Marais, Anna-Susan; Robinson, Luther K; Manning, Melanie A; Blankenship, Jason; Buckley, David; Hoyme, H Eugene; Adnams, Colleen M

    2013-06-01

    To provide an analysis of multiple predictors of cognitive and behavioral traits for children with fetal alcohol spectrum disorders (FASDs). Multivariate correlation techniques were used with maternal and child data from epidemiologic studies in a community in South Africa. Data on 561 first-grade children with fetal alcohol syndrome (FAS), partial FAS (PFAS), and not FASD and their mothers were analyzed by grouping 19 maternal variables into categories (physical, demographic, childbearing, and drinking) and used in structural equation models (SEMs) to assess correlates of child intelligence (verbal and nonverbal) and behavior. A first SEM using only 7 maternal alcohol use variables to predict cognitive/behavioral traits was statistically significant (B = 3.10, p < .05) but explained only 17.3% of the variance. The second model incorporated multiple maternal variables and was statistically significant explaining 55.3% of the variance. Significantly correlated with low intelligence and problem behavior were demographic (B = 3.83, p < .05) (low maternal education, low socioeconomic status [SES], and rural residence) and maternal physical characteristics (B = 2.70, p < .05) (short stature, small head circumference, and low weight). Childbearing history and alcohol use composites were not statistically significant in the final complex model and were overpowered by SES and maternal physical traits. Although other analytic techniques have amply demonstrated the negative effects of maternal drinking on intelligence and behavior, this highly controlled analysis of multiple maternal influences reveals that maternal demographics and physical traits make a significant enabling or disabling contribution to child functioning in FASD.

  2. Objectively Quantified Physical Activity and Sedentary Behavior in Predicting Visceral Adiposity and Liver Fat

    Directory of Open Access Journals (Sweden)

    Shelley E. Keating

    2016-01-01

    Full Text Available Objective. Epidemiologic studies suggest an inverse relationship between nonalcoholic fatty liver disease (NAFLD, visceral adipose tissue (VAT, and self-reported physical activity levels. However, subjective measurements can be inaccurate and prone to reporter bias. We investigated whether objectively quantified physical activity levels predicted liver fat and VAT in overweight/obese adults. Methods. Habitual physical activity was measured by triaxial accelerometry for four days (n=82. Time spent in sedentary behavior (MET < 1.6 and light (MET 1.6 < 3, moderate (MET 3 < 6, and vigorous (MET 6 < 9 physical activity was quantified. Magnetic resonance imaging and spectroscopy were used to quantify visceral and liver fat. Bivariate correlations and hierarchical multiple regression analyses were performed. Results. There were no associations between physical activity or sedentary behavior and liver lipid. Sedentary behavior and moderate and vigorous physical activity accounted for just 3% of variance for VAT (p=0.14 and 0.003% for liver fat (p=0.96. Higher levels of VAT were associated with time spent in moderate activity (r=0.294, p=0.007, but there was no association with sedentary behavior. Known risk factors for obesity-related NAFLD accounted for 62% and 40% of variance in VAT and liver fat, respectively (p<0.01. Conclusion. Objectively measured levels of habitual physical activity and sedentary behavior did not influence VAT or liver fat.

  3. Exploitation of genetic interaction network topology for the prediction of epistatic behavior

    KAUST Repository

    Alanis Lobato, Gregorio

    2013-10-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

  4. Exploitation of genetic interaction network topology for the prediction of epistatic behavior

    KAUST Repository

    Alanis Lobato, Gregorio; Cannistraci, Carlo; Ravasi, Timothy

    2013-01-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

  5. Drug-target interaction prediction from PSSM based evolutionary information.

    Science.gov (United States)

    Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-01-01

    The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. Weight-based stigmatization, psychological distress, & binge eating behavior among obese treatment-seeking adults.

    Science.gov (United States)

    Ashmore, Jamile A; Friedman, Kelli E; Reichmann, Simona K; Musante, Gerard J

    2008-04-01

    To evaluate the associations between weight-based stigmatization, psychological distress, and binge eating behavior in a treatment-seeking obese sample. Ninety-three obese adults completed three questionnaires: 1) Stigmatizing Situations Inventory, 2) Brief Symptoms Inventory, and 3) Binge Eating Questionnaire. Correlational analyses were used to evaluate the association between stigmatizing experiences, psychological distress and binge eating behavior. Stigmatizing experiences predicted both binge eating behavior (R(2)=.20, p<.001) and overall psychological distress (R(2)=.18, p<.001). A substantial amount of the variance in binge eating predicted by weight-based stigmatization was due to the effect of psychological distress. Specifically, of the 20% of the variance in binge eating accounted for by stigmatizing experiences, between 7% and 34% (p<.01) was due to the effects of various indicators of psychological distress. These data suggest that weight-based stigmatization predicts binge eating behavior and that psychological distress associated with stigmatizing experiences may be an important mediating factor.

  7. Stability of executive function and predictions to adaptive behavior from middle childhood to pre-adolescence

    Directory of Open Access Journals (Sweden)

    Madeline eHarms

    2014-04-01

    Full Text Available The shift from childhood to adolescence is characterized by rapid remodeling of the brain and increased risk-taking behaviors. Current theories hypothesize that developmental enhancements in sensitivity to affective environmental cues in adolescence may undermine executive function (EF and increase the likelihood of problematic behaviors. In the current study, we examined the extent to which EF in childhood predicts EF in early adolescence. We also tested whether individual differences in neural responses to affective cues (rewards/punishments in childhood serve as a biological marker for EF, sensation-seeking, academic performance, and social skills in early adolescence. At age 8, 84 children completed a gambling task while event-related potentials (ERPs were recorded. We examined the extent to which selections resulting in rewards or losses in this task elicited (i the P300, a post-stimulus waveform reflecting the allocation of attentional resources toward a stimulus, and (ii the SPN, a pre-stimulus anticipatory waveform reflecting a neural representation of a hunch about an outcome that originates in insula and ventromedial PFC. Children also completed a Dimensional Change Card-Sort (DCCS and Flanker task to measure EF. At age 12, 78 children repeated the DCCS and Flanker and completed a battery of questionnaires. Flanker and DCCS accuracy at age 8 predicted Flanker and DCCS performance at age 12, respectively. Individual differences in the magnitude of P300 (to losses vs. rewards and SPN (preceding outcomes with a high probability of punishment at age 8 predicted self-reported sensation seeking (lower and teacher-rated academic performance (higher at age 12. We suggest there is stability in EF from age 8 to 12, and that childhood neural sensitivity to reward and punishment predicts individual differences in sensation seeking and adaptive behaviors in children entering adolescence.

  8. Hierarchical Status Predicts Behavioral Vulnerability and Nucleus Accumbens Metabolic Profile Following Chronic Social Defeat Stress.

    Science.gov (United States)

    Larrieu, Thomas; Cherix, Antoine; Duque, Aranzazu; Rodrigues, João; Lei, Hongxia; Gruetter, Rolf; Sandi, Carmen

    2017-07-24

    Extensive data highlight the existence of major differences in individuals' susceptibility to stress [1-4]. While genetic factors [5, 6] and exposure to early life stress [7, 8] are key components for such neurobehavioral diversity, intriguing observations revealed individual differences in response to stress in inbred mice [9-12]. This raised the possibility that other factors might be critical in stress vulnerability. A key challenge in the field is to identify non-invasively risk factors for vulnerability to stress. Here, we investigated whether behavioral factors, emerging from preexisting dominance hierarchies, could predict vulnerability to chronic stress [9, 13-16]. We applied a chronic social defeat stress (CSDS) model of depression in C57BL/6J mice to investigate the predictive power of hierarchical status to pinpoint which individuals will exhibit susceptibility to CSDS. Given that the high social status of dominant mice would be the one particularly challenged by CSDS, we predicted and found that dominant individuals were the ones showing a strong susceptibility profile as indicated by strong social avoidance following CSDS, while subordinate mice were not affected. Data from 1 H-NMR spectroscopy revealed that the metabolic profile in the nucleus accumbens (NAc) relates to social status and vulnerability to stress. Under basal conditions, subordinates show lower levels of energy-related metabolites compared to dominants. In subordinates, but not dominants, levels of these metabolites were increased after exposure to CSDS. To the best of our knowledge, this is the first study that identifies non-invasively the origin of behavioral risk factors predictive of stress-induced depression-like behaviors associated with metabolic changes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Students academic performance based on behavior

    Science.gov (United States)

    Maulida, Juwita Dien; Kariyam

    2017-12-01

    Utilization of data in an information system that can be used for decision making that utilizes existing data warehouse to help dig useful information to make decisions correctly and accurately. Experience API (xAPI) is one of the enabling technologies for collecting data, so xAPI can be used as a data warehouse that can be used for various needs. One software application whose data is collected in xAPI is LMS. LMS is a software used in an electronic learning process that can handle all aspects of learning, by using LMS can also be known how the learning process and the aspects that can affect learning achievement. One of the aspects that can affect the learning achievement is the background of each student, which is not necessarily the student with a good background is an outstanding student or vice versa. Therefore, an action is needed to anticipate this problem. Prediction of student academic performance using Naive Bayes algorithm obtained accuracy of 67.7983% and error 32.2917%.

  10. Repeated Predictable Stress Causes Resilience against Colitis-Induced Behavioral Changes in Mice

    Directory of Open Access Journals (Sweden)

    Ahmed M Hassan

    2014-11-01

    Full Text Available Inflammatory bowel disease is associated with an increased risk of mental disorders and can be exacerbated by stress. In this study which was performed with male 10-week old C57Bl/6N mice, we used dextran sulfate sodium (DSS-induced colitis to evaluate behavioral changes caused by intestinal inflammation, to assess the interaction between repeated psychological stress (water avoidance stress, WAS and colitis in modifying behavior, and to analyze neurochemical correlates of this interaction. A 7-day treatment with DSS (2 % in drinking water decreased locomotion and enhanced anxiety-like behavior in the open field test and reduced social interaction. Repeated exposure to WAS for 7 days had little influence on behavior but prevented the DSS-induced behavioral disturbances in the open field and social interaction tests. In contrast, repeated WAS did not modify colon length, colonic myeloperoxidase content and circulating proinflammatory cytokines, parameters used to assess colitis severity. DSS-induced colitis was associated with an increase in circulating neuropeptide Y (NPY, a rise in the hypothalamic expression of cyclooxygenase-2 mRNA and a decrease in the hippocampal expression of NPY mRNA, brain-derived neurotrophic factor mRNA and mineralocorticoid receptor mRNA. Repeated WAS significantly decreased the relative expression of corticotropin-releasing factor mRNA in the hippocampus. The effect of repeated WAS to blunt the DSS-evoked behavioral disturbances was associated with a rise of circulating corticosterone and an increase in the expression of hypothalamic NPY mRNA. These results show that experimental colitis leads to a particular range of behavioral alterations which can be prevented by repeated WAS, a model of predictable chronic stress, while the severity of colitis remains unabated. We conclude that the mechanisms underlying the resilience effect of repeated WAS involves hypothalamic NPY and the hypothalamic-pituitary-adrenal axis.

  11. Applicability of the Theory of Planned Behavior in Predicting Supportive Behaviors by Parents and Adult Siblings of Immediate Relatives with Intellectual Disability

    Science.gov (United States)

    Rimmerman, Arie; Chen, Ariel

    2012-01-01

    This research examines the applicability of the Theory of Planned Behavior in predicting supportive behaviors by parents and adult siblings of immediate relatives with intellectual disability. Participants were 67 parents and 63 siblings whose immediate relatives with intellectual disability resided in two institutional care facilities. Three…

  12. Predicting supportive behavior of parents and siblings to a family member with intellectual disability living in institutional care.

    Science.gov (United States)

    Rimmerman, Arie; Chen, Ariel

    2012-01-01

    This feasibility study examines whether the theory of planned behavior can predict supportive behavior provided by either parents to their offspring--or adult siblings to their brothers and sisters--with an intellectual disability living in 2 Israeli institutional care facilities. Participants were 67 parents and 63 siblings who were interviewed at baseline regarding their intentions to visit their offspring or sibling in the institutional care facility, to contact the caregiving staff, and to accept visits at home. Parents' and siblings' behavior regarding visitation and supportive behavior was examined after 6 months by caregiving staff. Core findings indicated that subjective norms in siblings and parents predicted frequency of home visits. Perceived behavioral control predicted frequency of contact between siblings and staff. Differences between parents and siblings regarding their supportive behaviors are discussed with respect to social work practice.

  13. Predictive Effects of Good Self-Control and Poor Regulation on Alcohol-Related Outcomes: Do Protective Behavioral Strategies Mediate?

    Science.gov (United States)

    Pearson, Matthew R.; Kite, Benjamin A.; Henson, James M.

    2016-01-01

    In the present study, we examined whether use of protective behavioral strategies mediated the relationship between self-control constructs and alcohol-related outcomes. According to the two-mode model of self-control, good self-control (planfulness; measured with Future Time Perspective, Problem Solving, and Self-Reinforcement) and poor regulation (impulsivity; measured with Present Time Perspective, Poor Delay of Gratification, Distractibility) are theorized to be relatively independent constructs rather than opposite ends of a single continuum. The analytic sample consisted of 278 college student drinkers (68% women) who responded to a battery of surveys at a single time point. Using a structural equation model based on the two-mode model of self-control, we found that good self-control predicted increased use of three types of protective behavioral strategies (Manner of Drinking, Limiting/Stopping Drinking, and Serious Harm Reduction). Poor regulation was unrelated to use of protective behavioral strategies, but had direct effects on alcohol use and alcohol problems. Further, protective behavioral strategies mediated the relationship between good self-control and alcohol use. The clinical implications of these findings are discussed. PMID:22663345

  14. Predicting Oral Health Behavior using the Health Promotion Model among School Students: a Cross-sectional Survey

    Directory of Open Access Journals (Sweden)

    Abdurrahman Charkazi

    2016-07-01

    Full Text Available teeth and T=permanent teeth has been increasing from 1957 to 2015 years in Iran. The current survey aimed to test the power of health promotion model for predicting the oral health behavior among high-school students.  Materials and Methods: A cross-sectional study was conducted on 482 high school students in Gorgan city, Iran. Multi-cluster sampling was used to recruit the samples. A researcher-made questionnaire based on HPM was implemented to collect data. To analyze, SPSS-18 and statistical tests, including t-test, Pearson correlation coefficient and univariate and multivariate regression models were used. Results: A total of 482 high-school students including 255 (52.9% male and 227 (47.1% with mean age of 16.02 ± 0.5 were investigated. The highest and lowest prevalent positive oral health behavior were tooth brushing (73% and using fluidized oral irrigator (3.6%, respectively. Except for perceived barriers (with negative correlation, all constructs of HBM were positively related to oral health behaviors. Self-efficacy was the strongest predictor of oral health behavior (β=0.653 (r=0.541, P

  15. Application of Theory of Planned Behavior in Predicting Factors of Substance Abuse in Adolescents

    Directory of Open Access Journals (Sweden)

    Saeid Bashirian

    2012-12-01

    Full Text Available Background and Objective: Adolescence is the most critical period of life as regards commencing drug abuse. The social cost and damage caused by drug abuse in adolescence are enormous, necessitating interventional programs to prevent this behavior. The theory of planned behavior (TPB is perhaps the most influential theory for the prediction of social and health behaviors such as drug abuse.Materials and Methods: In this descriptive analytical study, samples were collected from male students in four high schools in different regions of Hamedan. The survey was carried out via random cluster sampling of 650 students. Data were collected using the standard self-report questionnaires and were analyzed using SPSS16, chi-squared test, correlation coefficient, and logistic regression analysis.Results: Among the adolescents participating in this study, 11.1% had the experience of cigarette smoking, 3.4% had the experience of drug abuse, and 12% had the experience of intention to abuse drugs. There was a significant relationship between drug abuse and the following variables: smoking experience (p value =0.001, OR=27.238; having drug user parents (p value =0.001, OR=8.630; having friends who had experienced drug abuse (p value =0.001, OR=11.060; having best friends who had experienced drug abuse (p value = 0.001, OR=11.931; family with drug abuse (p value = 0.001, OR=4.311; and having a sibling who abused drugs (p value=0.001, OR=15.815. According to the logistic regression analysis, attitude, subjective norms, and perceived behavior control were the most influential predictors of intention to abuse drugs.Conclusion: The use of TPB is beneficial in the predicting and planning for high-risk behaviors. TPB can be used for planning and implementing drug abuse prevention programs in adolescents.

  16. Adolescents' expectations for the future predict health behaviors in early adulthood.

    Science.gov (United States)

    McDade, Thomas W; Chyu, Laura; Duncan, Greg J; Hoyt, Lindsay T; Doane, Leah D; Adam, Emma K

    2011-08-01

    Health-related behaviors in adolescence establish trajectories of risk for obesity and chronic degenerative diseases, and they represent an important pathway through which socio-economic environments shape patterns of morbidity and mortality. Most behaviors that promote health involve making choices that may not pay off until the future, but the factors that predict an individual's investment in future health are not known. In this paper we consider whether expectations for the future in two domains relevant to adolescents in the U.S.-perceived chances of living to middle age and perceived chances of attending college-are associated with an individual's engagement in behaviors that protect health in the long run. We focus on adolescence as an important life stage during which habits formed may shape trajectories of disease risk later in life. We use data from a large, nationally representative sample of American youth (the US National Longitudinal Study of Adolescent Health) to predict levels of physical activity, fast food consumption, and cigarette smoking in young adulthood in relation to perceived life chances in adolescence, controlling for baseline health behaviors and a wide range of potentially confounding factors. We found that adolescents who rated their chances of attending college more highly exercised more frequently and smoked fewer cigarettes in young adulthood. Adolescents with higher expectations of living to age 35 smoked fewer cigarettes as young adults. Parental education was a significant predictor of perceived life chances, as well as health behaviors, but for each outcome the effects of perceived life chances were independent of, and often stronger than, parental education. Perceived life chances in adolescence may therefore play an important role in establishing individual trajectories of health, and in contributing to social gradients in population health. Copyright © 2011 Elsevier Ltd. All rights reserved.

  17. Situational cues and momentary food environment predict everyday eating behavior in adults with overweight and obesity.

    Science.gov (United States)

    Elliston, Katherine G; Ferguson, Stuart G; Schüz, Natalie; Schüz, Benjamin

    2017-04-01

    Individual eating behavior is a risk factor for obesity and highly dependent on internal and external cues. Many studies also suggest that the food environment (i.e., food outlets) influences eating behavior. This study therefore examines the momentary food environment (at the time of eating) and the role of cues simultaneously in predicting