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

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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

Statistical Models for Predicting Threat Detection From Human Behavior

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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

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

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

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

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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.

Leveraging Call Center Logs for Customer Behavior Prediction

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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.

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.

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

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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.

Multiphase, multicomponent phase behavior prediction

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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

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

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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.

Individual laboratory-measured discount rates predict field behavior.

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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.

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

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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.

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

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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.

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

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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.

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

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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…

Automated Clinical Assessment from Smart home-based Behavior Data

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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

Automated Cognitive Health Assessment From Smart Home-Based Behavior Data.

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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.

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

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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

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.

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

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

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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.

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

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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…

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

Predicting sun protection behaviors using protection motivation variables.

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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.

Body odor quality predicts behavioral attractiveness in humans.

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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.

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

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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

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

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

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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.

Trait Impressions as Heuristics for Predicting Future Behavior.

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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…

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

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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.

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

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...

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.

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

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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.

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

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

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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

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

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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.

Weight-based stigmatization, psychological distress, & binge eating behavior among obese treatment-seeking adults.

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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.

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

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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.

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

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

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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.

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.

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.

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

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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.

A clinical study of autogenic training-based behavioral treatment for panic disorder.

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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.

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

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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

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.

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

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)

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.

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.

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

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)

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.

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.

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.

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.

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.

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.

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.

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.

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)

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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

African Journals Online (AJOL)

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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.

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...

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...

“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.

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...

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.

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

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.

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)

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.)

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

Predicting Motivation: Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease.

Science.gov (United States)

Vassena, Eliana; Deraeve, James; Alexander, William H

2017-10-01

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338-1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO

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.

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

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

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

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.

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

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.

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.

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.

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

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.

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.

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)

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

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.

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).

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).

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.…

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…

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.

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…

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

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

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.

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.

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.

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.

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.

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

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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

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.

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

Data-Based Predictive Control with Multirate Prediction Step

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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.

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

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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.

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

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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.

Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions.

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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.

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

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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

Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia.

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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

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.

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

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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

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.

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.

Microbial-Host Co-metabolites Are Prodromal Markers Predicting Phenotypic Heterogeneity in Behavior, Obesity, and Impaired Glucose Tolerance

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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.

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.

When Preferences Are in the Way: Children's Predictions of Goal-Directed Behaviors.

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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).

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

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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.

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

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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…

Bidirectional associations between activity-related parenting practices, and child physical activity, sedentary screen-based behavior and body mass index: a longitudinal analysis.

Science.gov (United States)

Sleddens, Ester F C; Gubbels, Jessica S; Kremers, Stef P J; van der Plas, Eline; Thijs, Carel

2017-07-06

It has been generally assumed that activity-related parenting practices influence children's activity behavior and weight status. However, vice versa parents may also change their parenting behaviors in response to their perceptions of their child's activity behavior and weight status. This study examined the bidirectional relationships between activity-related parenting practices, and physical activity, sedentary screen-based behavior, and body mass index (BMI) between children's age of 5 and 7 years. Three scales of the Activity-related Parenting Questionnaire (i.e. 'restriction of sedentary behavior', 'stimulation of physical activity', and 'monitoring of physical activity') were completed by 1694 parents of the Dutch KOALA Birth Cohort Study at the child's age of around 5 and again around age 7. Physical activity, sedentary screen-based behavior and BMI were measured at both ages as well. Linear regression models were used to estimate the bidirectional associations between each parenting practice and the child's physical activity levels, sedentary screen-based behavior and BMI z-scores. Several parenting practices at age 5 predicted child physical activity, sedentary screen-based behavior, and BMI z-scores at age 7. Restriction of sedentary behavior positively predicted child BMI and sedentary screen-based behavior, whereas this practice negatively predicted child physical activity. In addition, stimulation of physical activity at age 5 was significantly associated with higher levels of child physical activity at age 7. The following child factors at age 5 predicted parenting practices at age 7: Child physical activity positively predicted parental stimulation of physical activity and monitoring activities. Sedentary screen-based behavior was associated with lower parental stimulation to be active. Findings generally revealed that parents and children mutually influence each other's behavior. A reinforcing feedback loop was present between parental stimulation

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

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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.

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.

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.

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

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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

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.

Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs.

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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.

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

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.

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.

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…

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.

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.

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

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.

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...

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.

Modeling Driving Behavior at Roundabouts: Impact of Roundabout Layout and Surrounding Traffic on Driving Behavior

OpenAIRE

Zhao, Min; Käthner, David; Söffker, Dirk; Jipp, Meike; Lemmer, Karsten

2017-01-01

Driving behavior prediction at roundabouts is an important challenge to improve driving safety by supporting drivers with intelligent assistance systems. To predict the driving behavior effciently steering wheel status was proven to have robust predictability based on a Support Vector Machine algorithm. Previous research has not considered potential effects of roundabout layout and surrounding traffic on driving behavior, but that consideration can certainly improve the prediction results....

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.

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

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.

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

Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs

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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.

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.

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.

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

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…

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...

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

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.

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.

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, ...

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).

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.

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

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.

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.

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.

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

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.

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.

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.

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...

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.

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.

Goal inferences about robot behavior : goal inferences and human response behaviors

NARCIS (Netherlands)

Broers, H.A.T.; Ham, J.R.C.; Broeders, R.; De Silva, P.; Okada, M.

2014-01-01

This explorative research focused on the goal inferences human observers draw based on a robot's behavior, and the extent to which those inferences predict people's behavior in response to that robot. Results show that different robot behaviors cause different response behavior from people.

A novel unified dislocation density-based model for hot deformation behavior of a nickel-based superalloy under dynamic recrystallization conditions

International Nuclear Information System (INIS)

Lin, Y.C.; Wen, Dong-Xu; Chen, Xiao-Min; Chen, Ming-Song

2016-01-01

In this study, a novel unified dislocation density-based model is presented for characterizing hot deformation behaviors in a nickel-based superalloy under dynamic recrystallization (DRX) conditions. In the Kocks-Mecking model, a new softening item is proposed to represent the impacts of DRX behavior on dislocation density evolution. The grain size evolution and DRX kinetics are incorporated into the developed model. Material parameters of the developed model are calibrated by a derivative-free method of MATLAB software. Comparisons between experimental and predicted results confirm that the developed unified dislocation density-based model can nicely reproduce hot deformation behavior, DRX kinetics, and grain size evolution in wide scope of initial grain size, strain rate, and deformation temperature. Moreover, the developed unified dislocation density-based model is well employed to analyze the time-variant forming processes of the studied superalloy. (orig.)

A novel unified dislocation density-based model for hot deformation behavior of a nickel-based superalloy under dynamic recrystallization conditions

Energy Technology Data Exchange (ETDEWEB)

Lin, Y.C. [Central South University, School of Mechanical and Electrical Engineering, Changsha (China); Light Alloy Research Institute of Central South University, Changsha (China); State Key Laboratory of High Performance Complex Manufacturing, Changsha (China); Wen, Dong-Xu; Chen, Xiao-Min [Central South University, School of Mechanical and Electrical Engineering, Changsha (China); Chen, Ming-Song [Central South University, School of Mechanical and Electrical Engineering, Changsha (China); State Key Laboratory of High Performance Complex Manufacturing, Changsha (China)

2016-09-15

In this study, a novel unified dislocation density-based model is presented for characterizing hot deformation behaviors in a nickel-based superalloy under dynamic recrystallization (DRX) conditions. In the Kocks-Mecking model, a new softening item is proposed to represent the impacts of DRX behavior on dislocation density evolution. The grain size evolution and DRX kinetics are incorporated into the developed model. Material parameters of the developed model are calibrated by a derivative-free method of MATLAB software. Comparisons between experimental and predicted results confirm that the developed unified dislocation density-based model can nicely reproduce hot deformation behavior, DRX kinetics, and grain size evolution in wide scope of initial grain size, strain rate, and deformation temperature. Moreover, the developed unified dislocation density-based model is well employed to analyze the time-variant forming processes of the studied superalloy. (orig.)

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.

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.

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.

The status and predictors of hypertension preventive nutritional behaviors in adolescents based on the constructs of the Theory of Planned Behavior.

Science.gov (United States)

Matlabi, Mohammad; Esmaeili, Reza; Moshki, Mahdi; Ranaei, Afsaneh; Haji, Alireza; Mehrabi, Rahele

2018-01-01

Malnutrition is an important factor affecting hypertensive incidence. Since the unhealthiest nutritional behaviors are rooted in childhood attitudes and experiences, applying educational interventions to these age groups will be most useful in the formation of preventive nutritional behaviors. To determine the predictive power of the TPB on hypertension in junior high-school students. The present cross-sectional study was conducted on 160 junior high-school students in Kashmar, Iran in academic year commencing 2-13-2014, selected through random sampling. The participants completed a researcher-made questionnaire consisting of a demographic information form and a section to evaluate the constructs of the TPB. The data collected were analyzed in SPSS-16 using the correlation Wilcoxon statistics test, the one-way ANOVA and multiple linear regression analysis. The mean age of the students was 13.51. A total of 47% of the students had snacked on potato chips and cheese puffs, 45% had eaten high-fat foods and 51.2% had eaten cookies and chocolates within the past week. The variable of behavioral intention predicted 32% of the variations in preventive nutritional behaviors by itself. The Pearson product-moment correlation analysis found that hypertension preventive nutritional behaviors were significantly correlated with attitude (peducation interventions should be developed based on variables such as behavioral intention and its determinants, i.e. attitude, perceived behavioral control and subjective norms.

Determinants of responsibility for health, spiritual health and interpersonal relationship based on theory of planned behavior in high school girl students.

Science.gov (United States)

Rezazadeh, Afsaneh; Solhi, Mahnaz; Azam, Kamal

2015-01-01

Adolescence is a sensitive period of acquiring normal and abnormal habits for all oflife. The study investigates determinants of responsibility for health, spiritual health and interpersonal relations and predictive factors based on the theory of planned behavior in high school girl students in Tabriz. In this Cross-sectional study, 340 students were selected thorough multi-stage sampling. An author-made questionnaire based on standard questionnaires of Health Promotion and Lifestyle II (HPLPII), spiritual health standards (Palutzian & Ellison) and components of the theory of planned behavior (attitudes, subjective norms, perceived behavioral control, and behavioral intention) was used for data collection. The questionnaire was validated in a pilot study. Data were analyzed using SPSS v.15 and descriptive and analytical tests (Chi-square test, Pearson correlation co-efficient and liner regression test in backward method). Students' responsibility for health, spiritual health, interpersonal relationships, and concepts of theory of planned behavior was moderate. We found a significant positive correlation (ptheory of planned behavior. Attitude and perceived behavioral control predicted 35% of intention of behavioral change (pbehavioral control predicted 74% of behavioral change in accountability for health (pbehavioral change in spiritual health (pbehavioral change in interpersonal relationship (pbehavioral intention and its determinants such as perceived behavioral control should be noted in promoting intervention programs.

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…

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

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

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.

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.

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 ...

Internet Connection Control based on Idle Time Using User Behavior Pattern Analysis

Directory of Open Access Journals (Sweden)

Fadilah Fahrul Hardiansyah

2014-12-01

Full Text Available The increase of smartphone ability is rapidly increasing the power consumption. Many methods have been proposed to reduce smartphone power consumption. Most of these methods use the internet connection control based on the availability of the battery power level regardless of when and where a waste of energy occurs. This paper proposes a new approach to control the internet connection based on idle time using user behavior pattern analysis. User behavior patterns are used to predict idle time duration. Internet connection control performed during idle time. During idle time internet connection periodically switched on and off by a certain time interval. This method effectively reduces a waste of energy. Control of the internet connection does not interfere the user because it is implemented on idle time. Keywords: Smartphone, User Behavior, Pattern Recognition, Idle Time, Internet Connection Control

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.

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.

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.

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

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.

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.

Predictive control strategies for wind turbine system based on permanent magnet synchronous generator.

Science.gov (United States)

Maaoui-Ben Hassine, Ikram; Naouar, Mohamed Wissem; Mrabet-Bellaaj, Najiba

2016-05-01

In this paper, Model Predictive Control and Dead-beat predictive control strategies are proposed for the control of a PMSG based wind energy system. The proposed MPC considers the model of the converter-based system to forecast the possible future behavior of the controlled variables. It allows selecting the voltage vector to be applied that leads to a minimum error by minimizing a predefined cost function. The main features of the MPC are low current THD and robustness against parameters variations. The Dead-beat predictive control is based on the system model to compute the optimum voltage vector that ensures zero-steady state error. The optimum voltage vector is then applied through Space Vector Modulation (SVM) technique. The main advantages of the Dead-beat predictive control are low current THD and constant switching frequency. The proposed control techniques are presented and detailed for the control of back-to-back converter in a wind turbine system based on PMSG. Simulation results (under Matlab-Simulink software environment tool) and experimental results (under developed prototyping platform) are presented in order to show the performances of the considered control strategies. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Theoretical models to predict the mechanical behavior of thick composite tubes

Directory of Open Access Journals (Sweden)

Volnei Tita

2012-02-01

Full Text Available This paper shows theoretical models (analytical formulations to predict the mechanical behavior of thick composite tubes and how some parameters can influence this behavior. Thus, firstly, it was developed the analytical formulations for a pressurized tube made of composite material with a single thick ply and only one lamination angle. For this case, the stress distribution and the displacement fields are investigated as function of different lamination angles and reinforcement volume fractions. The results obtained by the theoretical model are physic consistent and coherent with the literature information. After that, the previous formulations are extended in order to predict the mechanical behavior of a thick laminated tube. Both analytical formulations are implemented as a computational tool via Matlab code. The results obtained by the computational tool are compared to the finite element analyses, and the stress distribution is considered coherent. Moreover, the engineering computational tool is used to perform failure analysis, using different types of failure criteria, which identifies the damaged ply and the mode of failure.

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.

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.

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

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....

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

Observed parenting behaviors interact with a polymorphism of the brain-derived neurotrophic factor gene to predict the emergence of oppositional defiant and callous-unemotional behaviors at age 3 years.

Science.gov (United States)

Willoughby, Michael T; Mills-Koonce, Roger; Propper, Cathi B; Waschbusch, Daniel A

2013-11-01

Using the Durham Child Health and Development Study, this study (N = 171) tested whether observed parenting behaviors in infancy (6 and 12 months) and toddlerhood/preschool (24 and 36 months) interacted with a child polymorphism of the brain-derived neurotrophic factor gene to predict oppositional defiant disorder (ODD) and callous-unemotional (CU) behaviors at age 3 years. Child genotype interacted with observed harsh and intrusive (but not sensitive) parenting to predict ODD and CU behaviors. Harsh-intrusive parenting was more strongly associated with ODD and CU for children with a methionine allele of the brain-derived neurotrophic factor gene. CU behaviors were uniquely predicted by harsh-intrusive parenting in infancy, whereas ODD behaviors were predicted by harsh-intrusive parenting in both infancy and toddlerhood/preschool. The results are discussed from the perspective of the contributions of caregiving behaviors as contributing to distinct aspects of early onset disruptive behavior.

A Game Theoretic Approach to Cyber Attack Prediction

Energy Technology Data Exchange (ETDEWEB)

Peng Liu

2005-11-28

The area investigated by this project is cyber attack prediction. With a focus on correlation-based prediction, current attack prediction methodologies overlook the strategic nature of cyber attack-defense scenarios. As a result, current cyber attack prediction methodologies are very limited in predicting strategic behaviors of attackers in enforcing nontrivial cyber attacks such as DDoS attacks, and may result in low accuracy in correlation-based predictions. This project develops a game theoretic framework for cyber attack prediction, where an automatic game-theory-based attack prediction method is proposed. Being able to quantitatively predict the likelihood of (sequences of) attack actions, our attack prediction methodology can predict fine-grained strategic behaviors of attackers and may greatly improve the accuracy of correlation-based prediction. To our best knowledge, this project develops the first comprehensive framework for incentive-based modeling and inference of attack intent, objectives, and strategies; and this project develops the first method that can predict fine-grained strategic behaviors of attackers. The significance of this research and the benefit to the public can be demonstrated to certain extent by (a) the severe threat of cyber attacks to the critical infrastructures of the nation, including many infrastructures overseen by the Department of Energy, (b) the importance of cyber security to critical infrastructure protection, and (c) the importance of cyber attack prediction to achieving cyber security.

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.

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.

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.

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.

Prediction of Happy-Sad mood from daily behaviors and previous sleep history.

Science.gov (United States)

Sano, Akane; Yu, Amy Z; McHill, Andrew W; Phillips, Andrew J K; Taylor, Sara; Jaques, Natasha; Klerman, Elizabeth B; Picard, Rosalind W

2015-01-01

We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants for ~30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.

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…

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.

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.

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

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

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

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

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.

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.

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.

Distributed Recurrent Neural Forward Models with Synaptic Adaptation and CPG-based control for Complex Behaviors of Walking Robots

Directory of Open Access Journals (Sweden)

Sakyasingha eDasgupta

2015-09-01

Full Text Available Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures with the underlying neural mechanisms. The neural mechanisms consist of 1 central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2 distributed (at each leg recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3 searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles. Furthermore we demonstrate that the newly developed recurrent network based approach to sensorimotor prediction outperforms the previous state of the art adaptive neuron

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

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.

Coupled Interfacial Tension and Phase Behavior Model Based on Micellar Curvatures

KAUST Repository

Torrealba, V. A.

2017-11-08

This article introduces a consistent and robust model that predicts interfacial tensions for all microemulsion Winsor types and overall compositions. The model incorporates film bending arguments and Huh\\'s equation and is coupled to phase behavior so that simultaneous tuning of both interfacial tension (IFT) and phase behavior is possible. The oil-water interfacial tension and characteristic length are shown to be related to each other through the hydrophilic-lipophilic deviation (HLD). The phase behavior is tied to the micelle curvatures, without the need for using the net average curvature (NAC). The interfacial tension model is related to solubilization ratios in order to introduce a coupled interfacial tension-phase behavior model for all phase environments. The approach predicts two- and three-phase interfacial tensions and phase behavior (i.e., tie lines and tie triangles) for changes in composition and HLD input parameters, such as temperature, pressure, surfactant structure, and oil equivalent alkane carbon number. Comparisons to experimental data show excellent fits and predictive capability.

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.

Using Formal Grammars to Predict I/O Behaviors in HPC: The Omnisc'IO Approach

Energy Technology Data Exchange (ETDEWEB)

Dorier, Matthieu; Ibrahim, Shadi; Antoniu, Gabriel; Ross, Rob

2016-08-01

The increasing gap between the computation performance of post-petascale machines and the performance of their I/O subsystem has motivated many I/O optimizations including prefetching, caching, and scheduling. In order to further improve these techniques, modeling and predicting spatial and temporal I/O patterns of HPC applications as they run has become crucial. In this paper we present Omnisc'IO, an approach that builds a grammar-based model of the I/O behavior of HPC applications and uses it to predict when future I/O operations will occur, and where and how much data will be accessed. To infer grammars, Omnisc'IO is based on StarSequitur, a novel algorithm extending Nevill-Manning's Sequitur algorithm. Omnisc'IO is transparently integrated into the POSIX and MPI I/O stacks and does not require any modification in applications or higher-level I/O libraries. It works without any prior knowledge of the application and converges to accurate predictions of any N future I/O operations within a couple of iterations. Its implementation is efficient in both computation time and memory footprint.

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.

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...

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.

Decisions among the undecided: implicit attitudes predict future voting behavior of undecided voters.

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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.

Predicting the behavior of techno-social systems.

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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.

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.

Predictive modeling and reducing cyclic variability in autoignition engines

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Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob

2016-08-30

Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

Prediction of composites behavior undergoing an ATP process through data-mining

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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.

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...

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

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

Esophageal cancer prediction based on qualitative features using adaptive fuzzy reasoning method

Directory of Open Access Journals (Sweden)

Raed I. Hamed

2015-04-01

Full Text Available Esophageal cancer is one of the most common cancers world-wide and also the most common cause of cancer death. In this paper, we present an adaptive fuzzy reasoning algorithm for rule-based systems using fuzzy Petri nets (FPNs, where the fuzzy production rules are represented by FPN. We developed an adaptive fuzzy Petri net (AFPN reasoning algorithm as a prognostic system to predict the outcome for esophageal cancer based on the serum concentrations of C-reactive protein and albumin as a set of input variables. The system can perform fuzzy reasoning automatically to evaluate the degree of truth of the proposition representing the risk degree value with a weight value to be optimally tuned based on the observed data. In addition, the implementation process for esophageal cancer prediction is fuzzily deducted by the AFPN algorithm. Performance of the composite model is evaluated through a set of experiments. Simulations and experimental results demonstrate the effectiveness and performance of the proposed algorithms. A comparison of the predictive performance of AFPN models with other methods and the analysis of the curve showed the same results with an intuitive behavior of AFPN models.

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

PredPsych: A toolbox for predictive machine learning-based approach in experimental psychology research.

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Koul, Atesh; Becchio, Cristina; Cavallo, Andrea

2017-12-12

Recent years have seen an increased interest in machine learning-based predictive methods for analyzing quantitative behavioral data in experimental psychology. While these methods can achieve relatively greater sensitivity compared to conventional univariate techniques, they still lack an established and accessible implementation. The aim of current work was to build an open-source R toolbox - "PredPsych" - that could make these methods readily available to all psychologists. PredPsych is a user-friendly, R toolbox based on machine-learning predictive algorithms. In this paper, we present the framework of PredPsych via the analysis of a recently published multiple-subject motion capture dataset. In addition, we discuss examples of possible research questions that can be addressed with the machine-learning algorithms implemented in PredPsych and cannot be easily addressed with univariate statistical analysis. We anticipate that PredPsych will be of use to researchers with limited programming experience not only in the field of psychology, but also in that of clinical neuroscience, enabling computational assessment of putative bio-behavioral markers for both prognosis and diagnosis.

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.

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.

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

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.

Empathy and nonattachment independently predict peer nominations of prosocial behavior of adolescents.

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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.

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.

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.

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.

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…

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

Sexting Leads to "Risky" Sex? An Analysis of Sexting Behaviors in a Nonuniversity-Based, Older Adult Population.

Science.gov (United States)

Currin, Joseph M; Hubach, Randolph D; Sanders, Carissa; Hammer, Tonya R

2017-10-03

Since few researchers have analyzed sexting behaviors in nonuniversity-based adult samples, we sought to determine if sexting is associated with negative psychological correlates and risky sexual behaviors in this population. Analysis of individuals who indicated having vaginal or anal sex in the past 12 months and who identified as single (n = 377) showed that condomless sex is independent of sexting behaviors. Results for those in committed relationships (n = 374) and having had vaginal or anal sex in the past 12 months also demonstrated condomless sex and sexting behaviors were not related. Furthermore, alcohol consumption and relational health were predictive of sexting behaviors in adults in committed relationships. These findings demonstrate that while risky sexual behavior and negative psychological correlates are associated with sexting and younger populations, the same might not be true for a nonuniversity-based, older adult sample.

Utility of the theory of reasoned action and theory of planned behavior for predicting Chinese adolescent smoking.

Science.gov (United States)

Guo, Qian; Johnson, C Anderson; Unger, Jennifer B; Lee, Liming; Xie, Bin; Chou, Chih-Ping; Palmer, Paula H; Sun, Ping; Gallaher, Peggy; Pentz, MaryAnn

2007-05-01

One third of smokers worldwide live in China. Identifying predictors of smoking is important for prevention program development. This study explored whether the Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB) predict adolescent smoking in China. Data were obtained from 14,434 middle and high school students (48.6% boys, 51.4% girls) in seven geographically varied cities in China. TRA and TPB were tested by multilevel mediation modeling, and compared by multilevel analyses and likelihood ratio tests. Perceived behavioral control was tested as a main effect in TPB and a moderation effect in TRA. The mediation effects of smoking intention were supported in both models (p<0.001). TPB accounted for significantly more variance than TRA (p<0.001). Perceived behavioral control significantly interacted with attitudes and social norms in TRA (p<0.001). Therefore, TRA and TPB are applicable to China to predict adolescent smoking. TPB is superior to TRA for the prediction and TRA can better predict smoking among students with lower than higher perceived behavioral control.

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.

Predicting change in early adolescent problem behavior in the middle school years: a mesosystemic perspective on parenting and peer experiences.

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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.

Place-based attributes predict community membership in a mobile phone communication network.

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T Trevor Caughlin

Full Text Available Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97 between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

Place-based attributes predict community membership in a mobile phone communication network.

Science.gov (United States)

Caughlin, T Trevor; Ruktanonchai, Nick; Acevedo, Miguel A; Lopiano, Kenneth K; Prosper, Olivia; Eagle, Nathan; Tatem, Andrew J

2013-01-01

Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

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

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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…

Parent Cortisol and Family Relatedness Predict Anxious Behavior in Emerging Adults

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Johnson, Vanessa Kahen; Gans, Susan E.

2016-01-01

Emerging adult cortisol response during family interaction predicts change in EA anxious behavior during the transition to college (Gans & Johnson, in press). In the present study, we take an additional step toward integrating family systems research and physiology by including assessment of parent physiology. We collect salivary cortisol from parents and emerging-adults during triadic family interaction. Emerging adults (N = 101) between the ages of 17 and 19 were assessed at three time points across their first college year: the summer before college, fall and spring semesters. Two parents accompanied the emerging adult child to the summer assessment; all family members provided four saliva samples each at 20-minute intervals. Later assessments of emerging adults included measures of internalizing behaviors. Parents’ cortisol secretion patterns during family interaction predict their emerging adult child’s cortisol secretion pattern, parent perceptions of the family environment, and emerging adult children’s internalizing behavior during the college transition. Different patterns of results emerged for mothers’ and fathers’ cortisol response to family interaction, and for families with sons or with daughters. The approach taken by this study provides a first step toward understanding how interrelationships among elements of physiology and family functioning contribute to adjustment during major life transitions. PMID:27536860

In-kennel behavior predicts length of stay in shelter dogs.

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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.

Modeling study of droplet behavior during blowdown period of large break LOCA based on experimental data

International Nuclear Information System (INIS)

Sakaba, Hiroshi; Umezawa, Shigemitsu; Teramae, Tetsuya; Furukawa, Yuji

2004-01-01

During LOCA (Loss Of Coolant Accident) in PWR, droplets behavior during blowdown period is one of the important phenomena. For example, the spattering from falling liquid film that flows from upper plenum generates those droplets in core region. The behavior of droplets in such flow has strong effect for cladding temperature behavior because these droplets are able to remove heat from a reactor core by its direct contact on fuel rods and its evaporation at the surface. For safety analysis of LOCA in PWR, it is necessary to evaluate droplet diameter precisely in order to predict fuel cladding temperature changing by the calculation code. Based on the test results, a new droplet behavior model was developed for the MCOBRA/TRC code that predicts the droplet behavior during such LOCA events. Furthermore, the verification calculations that simulated some blowdown tests were performed using by the MCOBRA/TRAC code. These results indicated the validity of this droplet model during blow down cooling period. The experiment was focused on investigating the Weber number of steady droplet in the blow down phenomenon of large break LOCA. (author)

Applicability of the Theory of Planned Behavior in Predicting Supportive Behaviors by Parents and Adult Siblings of Immediate Relatives with Intellectual Disability

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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…

Predicting the potential for risky behavior among those "too young" to drink as the result of appealing advertising.

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Austin, E W; Knaus, C

2000-01-01

A survey of 273 children in Washington state used a predrinking behavior index as a behavioral outcome to assess media effects on precursors to drinking among children for whom alcohol consumption is not yet occurring. It also examined age trends in relevant beliefs and behaviors. Perceptions of advertising desirability, the extent to which it seemed appealing, increased steadily from third to ninth grade, whereas identification with portrayals, the degree to which individuals wanted to emulate portrayals, leveled off after sixth grade. Expectancies, positive social benefits perceived to be associated with drinking alcohol, also increased with age, particularly between sixth and ninth grade. When demographics and grade level were controlled, desirability predicted identification, and both predicted expectancies, which is consistent with media decision-making theory. Expectancies correlated with alcohol predrinking behavior, and expectancies predicted risky behavior, with demographics and grade level controlled. Predrinking behavior and reported risky behavior were correlated. The results provide cross-sectional support for the view that beliefs and desires developing by third grade prime children for future decisions regarding substance use.

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

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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.

A Risk Prediction Model for Sporadic CRC Based on Routine Lab Results.

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Boursi, Ben; Mamtani, Ronac; Hwang, Wei-Ting; Haynes, Kevin; Yang, Yu-Xiao

2016-07-01

Current risk scores for colorectal cancer (CRC) are based on demographic and behavioral factors and have limited predictive values. To develop a novel risk prediction model for sporadic CRC using clinical and laboratory data in electronic medical records. We conducted a nested case-control study in a UK primary care database. Cases included those with a diagnostic code of CRC, aged 50-85. Each case was matched with four controls using incidence density sampling. CRC predictors were examined using univariate conditional logistic regression. Variables with p value CRC prediction models which included age, sex, height, obesity, ever smoking, alcohol dependence, and previous screening colonoscopy had an AUC of 0.58 (0.57-0.59) with poor goodness of fit. A laboratory-based model including hematocrit, MCV, lymphocytes, and neutrophil-lymphocyte ratio (NLR) had an AUC of 0.76 (0.76-0.77) and a McFadden's R2 of 0.21 with a NRI of 47.6 %. A combined model including sex, hemoglobin, MCV, white blood cells, platelets, NLR, and oral hypoglycemic use had an AUC of 0.80 (0.79-0.81) with a McFadden's R2 of 0.27 and a NRI of 60.7 %. Similar results were shown in an internal validation set. A laboratory-based risk model had good predictive power for sporadic CRC risk.

BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

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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.

Forecasting behavior in smart homes based on sleep and wake patterns.

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Williams, Jennifer A; Cook, Diane J

2017-01-01

The goal of this research is to use smart home technology to assist people who are recovering from injuries or coping with disabilities to live independently. We introduce an algorithm to model and forecast wake and sleep behaviors that are exhibited by the participant. Furthermore, we propose that sleep behavior is impacted by and can be modeled from wake behavior, and vice versa. This paper describes the Behavior Forecasting (BF) algorithm. BF consists of 1) defining numeric values that reflect sleep and wake behavior, 2) forecasting wake and sleep values from past behavior, 3) analyzing the effect of wake behavior on sleep and vice versa, and 4) improving prediction performance by using both wake and sleep scores. The BF method was evaluated with data collected from 20 smart homes. We found that regardless of the forecasting method utilized, wake behavior and sleep behavior can be modeled with a minimum accuracy of 84%. Additionally, normalizing the wake and sleep scores drastically improves the accuracy to 99%. The results show that we can effectively model wake and sleep behaviors in a smart environment. Furthermore, wake behaviors can be predicted from sleep behaviors and vice versa.

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...

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

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Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

2016-02-01

A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

Variables that predict academic procrastination behavior in prospective primary school teachers

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Asuman Seda SARACALOĞLU

2016-04-01

Full Text Available This study aimed to examine the variables predicting academic procrastination behavior of prospective primary school teachers and is conducted using the correlational survey model. The study group is composed of 294 undergraduate students studying primary school teaching programs in faculties of education at Adnan Menderes, Pamukkale, and Muğla Sıtkı Koçman Universities in Turkey. The data collection instruments used were the Procrastination Assessment Scale Students (PASS, Academic Self-Efficacy Scale (ASES, and Academic Motivation Scale (AMS. While analyzing the gathered data, descriptive analysis techniques were utilized. Moreover, while analyzing the data, power of variables namely reasons of academic procrastination, academic motivation, and academic efficacy to predict prospective primary school teachers’ academic procrastination tendencies were tested. For that purpose, stepwise regression analysis was employed. It was found that nearly half of the prospective primary school teachers displayed no academic procrastination behavior. Participants’ reasons for procrastination were fear of failure, laziness, taking risks, and rebellion against control. An average level significant correlation was found between participants’ academic procrastination and other variables. As a result, it was identified that prospective primary school teachers had less academic procrastination than reported in literature and laziness, fear of failure, academic motivation predicted academic procrastination.

Implicit attitudes toward eating stimuli differentiate eating disorder and non-eating disorder groups and predict eating disorder behaviors.

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Smith, April R; Forrest, Lauren N; Velkoff, Elizabeth A; Ribeiro, Jessica D; Franklin, Joseph

2018-04-01

The current study tested whether people with and without eating disorders (EDs) varied in their implicit attitudes toward ED-relevant stimuli. Additionally, the study tested whether implicit evaluations of ED-relevant stimuli predicted ED symptoms and behaviors over a 4-week interval. Participants were people without EDs (N = 85) and people seeking treatment for EDs (N = 92). All participants completed self-report questionnaires and a version of the affect misattribution procedure (AMP) at baseline. The AMP indexed implicit evaluations of average body stimuli, eating stimuli, and ED-symptom stimuli. Participants with EDs completed weekly follow-up measures of ED symptoms and behaviors for 4 weeks. Contrary to predictions, the anorexia nervosa (AN) group did not differ from the no ED group on implicit attitudes toward ED-symptom stimuli, and the bulimia nervosa (BN) group had less positive implicit attitudes toward ED-symptom stimuli relative to the no ED group. In line with predictions, people with AN and BN had more negative implicit attitudes toward average body and eating stimuli relative to the no ED group. In addition, among the ED group more negative implicit attitudes toward eating stimuli predicted ED symptoms and behaviors 4 weeks later, over and above baseline ED symptoms and behaviors. Taken together, implicit evaluations of eating stimuli differentiated people with AN and BN from people without EDs and longitudinally predicted ED symptoms and behaviors. Interventions that increase implicit liking of eating-related stimuli may reduce ED behaviors. © 2018 Wiley Periodicals, Inc.

It's the nature of the work: examining behavior-based sources of work-family conflict across occupations.

Science.gov (United States)

Dierdorff, Erich C; Ellington, J Kemp

2008-07-01

The consequences of work-family conflict for both individuals and organizations have been well documented, and the various sources of such conflict have received substantial attention. However, the vast majority of extant research has focused on only time- and strain-based sources, largely neglecting behavior-based sources. Integrating two nationally representative databases, the authors examine 3 behavior-based antecedents of work-family conflict linked specifically to occupational work role requirements (interdependence, responsibility for others, and interpersonal conflict). Results from multilevel analysis indicate that significant variance in work-family conflict is attributable to the occupation in which someone works. Interdependence and responsibility for others predict work-family conflict, even after controlling for several time- and strain-based sources.

The Role of Metacognition and Negative Emotions on Prediction of Abuse Behaviors

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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.

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

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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.

The Regulation of Behavioral Plasticity by Performance-Based Feedback and an Experimental Test with Avian Egg Production.

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Sockman, Keith W

2016-05-01

Optimizing plasticity in behavioral performances requires the abilities to regulate physiological effort and to estimate the effects of the environment. To describe how performance-based feedback could play a role in regulating recursive or continuous behavioral performances, I developed two models, one (environmental feedback) that assumes an initial ability to regulate effort but not to predict the effects of the environment and the other (effort feedback) that assumes an initial ability to predict the effects of the environment but not to regulate effort. I tested them by manipulating feedback on egg production, using an egg-substitution experiment in wild, free-ranging Lincoln's sparrows (Melospiza lincolnii). I discovered that females adjusted the size of their clutches' third laid eggs in response to the size of an experimentally substituted first laid egg, such that the size of the third laid egg increased with the size of the substitute. Results were largely consistent with the environmental feedback model, though small portions of the response surface were consistent with the effort feedback model or with neither. Regardless, such feedback-based regulation predicted by either model may help females maximize net benefits of egg production and may be a basis for mechanisms regulating a wide range of other behavioral performances, as well.

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.

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

Directory of Open Access Journals (Sweden)

Alejandro Baldominos Gómez

2016-03-01

Full Text Available This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies.

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.

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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.

Comparing the utility of the theory of planned behavior between boys and girls for predicting snack food consumption: implications for practice.

Science.gov (United States)

Branscum, Paul; Sharma, Manoj

2014-01-01

The purpose of this study was to use the theory of planned behavior to explain two types of snack food consumption among boys and girls (girls n = 98; boys n = 69), which may have implications for future theory-based health promotion interventions. Between genders, there was a significant difference for calorie-dense/nutrient-poor snacks (p = .002), but no difference for fruit and vegetable snacks. Using stepwise multiple regression, attitudes, perceived behavioral control, and subjective norms accounted for a large amount of the variance of intentions (girls = 43.3%; boys = 55.9%); however, for girls, subjective norms accounted for the most variance, whereas for boys, attitudes accounted for the most variance. Calories from calorie-dense/nutrient-poor snacks and fruit and vegetable snacks were also predicted by intentions. For boys, intentions predicted 6.4% of the variance for fruit and vegetable snacks (p = .03) but was not significant for calorie-dense/nutrient-poor snacks, whereas for girls, intentions predicted 6.0% of the variance for fruit and vegetable snacks (p = .007), and 7.2% of the variance for calorie-dense/nutrient-poor snacks (p = .004). Results suggest that the theory of planned behavior is a useful framework for predicting snack foods among children; however, there are important differences between genders that should be considered in future health promotion interventions.

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.

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…

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

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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.

Forecasting Behavior in Smart Homes Based on Sleep and Wake Patterns

Science.gov (United States)

Williams, Jennifer A.; Cook, Diane J.

2017-01-01

Background The goal of this research is to use smart home technology to assist people who are recovering from injuries or coping with disabilities to live independently. Objective We introduce an algorithm to model and forecast wake and sleep behaviors that are exhibited by the participant. Furthermore, we propose that sleep behavior is impacted by and can be modeled from wake behavior, and vice versa. Methods This paper describes the Behavior Forecasting (BF) algorithm. BF consists of 1) defining numeric values that reflect sleep and wake behavior, 2) forecasting wake and sleep values from past behavior, 3) analyzing the effect of wake behavior on sleep and vice versa, and 4) improving prediction performance by using both wake and sleep scores. Results The BF method was evaluated with data collected from 20 smart homes. We found that regardless of the forecasting method utilized, wake behavior and sleep behavior can be modeled with a minimum accuracy of 84%. Additionally, normalizing the wake and sleep scores drastically improves the accuracy to 99%. Conclusions The results show that we can effectively model wake and sleep behaviors in a smart environment. Furthermore, wake behaviors can be predicted from sleep behaviors and vice versa. PMID:27689555

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.

Casting and stress-strain simulations of a cast ductile iron component using microstructure based mechanical behavior

International Nuclear Information System (INIS)

Olofsson, Jakob; Svensson, Ingvar L

2012-01-01

The industrial demand for increased component performance with concurrent reductions in component weight, development times and verifications using physical prototypes drives the need to use the full potential of casting and Finite Element Method (FEM) simulations to correctly predict the mechanical behavior of cast components in service. The mechanical behavior of the component is determined by the casting process, and factors as component geometry and casting process parameters are known to affect solidification and microstructure formation throughout the component and cause local variations in mechanical behavior as well as residual stresses. Though residual stresses are known to be an important factor in the mechanical behavior of the component, the importance of local mechanical behavior is not well established and the material is typically considered homogeneous throughout the component. This paper deals with the influence of solidification and solid state transformation on microstructure formation and the effect of local microstructure variations on the mechanical behavior of the cast component in service. The current work aims to investigate the coupling between simulation of solidification, microstructure and local variations in mechanical behavior and stress-strain simulation. This is done by performing several simulations of a ductile iron component using a recently developed simulation strategy, a closed chain of simulations for cast components, able to predict and describe the local variations in not only elastic but also plastic behavior throughout the component by using microstructural parameters determined by simulations of microstructural evolution in the component during the casting process. In addition the residual stresses are considered. The results show that the FEM simulation results are significantly affected by including microstructure based mechanical behavior. When the applied load is low and the component is subjected to stress levels

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

Measures of behavioral function predict duration of video game play: Utilization of the Video Game Functional Assessment - Revised.

Science.gov (United States)

Buono, Frank D; Griffiths, Mark D; Sprong, Matthew E; Lloyd, Daniel P; Sullivan, Ryan M; Upton, Thomas D

2017-12-01

Background Internet gaming disorder (IGD) was introduced in the DSM-5 as a way of identifying and diagnosing problematic video game play. However, the use of the diagnosis is constrained, as it shares criteria with other addictive orders (e.g., pathological gambling). Aims Further work is required to better understand IGD. One potential avenue of investigation is IGD's relationship to the primary reinforcing behavioral functions. This study explores the relationship between duration of video game play and the reinforcing behavioral functions that may motivate or maintain video gaming. Methods A total of 499 video game players began the online survey, with complete data from 453 participants (85% white and 28% female), were analyzed. Individuals were placed into five groups based on self-reported hours of video gaming per week, and completed the Video Game Functional Assessment - Revised (VGFA-R). Results The results demonstrated the escape and social attention function were significant in predicting duration of video game play, whereas sensory and tangible were not significant. Conclusion Future implications of the VGFA-R and behaviorally based research are discussed.

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...

An investigation of the efficacy of acceptance-based behavioral therapy for academic procrastination.

Science.gov (United States)

Glick, Debra M; Orsillo, Susan M

2015-04-01

Procrastination among college students is both prevalent and troublesome, harming both academic performance and physical health. Unfortunately, no "gold standard" intervention exists. Research suggests that psychological inflexibility may drive procrastination. Accordingly, interventions using acceptance and mindfulness methods to increase psychological flexibility may decrease procrastination. This study compared time management and acceptance-based behavioral interventions. College students' predictions of how much assigned reading they should complete were compared to what they did complete. Procrastination, anxiety, psychological flexibility, and academic values were also measured. Although a trend suggested that time management intervention participants completed more reading, no group differences in procrastination were revealed. The acceptance-based behavioral intervention was most effective for participants who highly valued academics. Clinical implications and future research are discussed. (c) 2015 APA, all rights reserved).

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. 

A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations

Directory of Open Access Journals (Sweden)

Zhenghui Sha

2018-01-01

Full Text Available Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes are deciding factors in customers’ consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products would influence such decisions. This paper presents a network-based approach based on Exponential Random Graph Models to study customers’ consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers’ consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers’ decision-making.

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

Direct-to-consumer advertising of predictive genetic tests: a health belief model based examination of consumer response.

Science.gov (United States)

Rollins, Brent L; Ramakrishnan, Shravanan; Perri, Matthew

2014-01-01

Direct-to-consumer (DTC) advertising of predictive genetic tests (PGTs) has added a new dimension to health advertising. This study used an online survey based on the health belief model framework to examine and more fully understand consumers' responses and behavioral intentions in response to a PGT DTC advertisement. Overall, consumers reported moderate intentions to talk with their doctor and seek more information about PGTs after advertisement exposure, though consumers did not seem ready to take the advertised test or engage in active information search. Those who perceived greater threat from the disease, however, had significantly greater behavioral intentions and information search behavior.

Modeling Patient No-Show History and Predicting Future Outpatient Appointment Behavior in the Veterans Health Administration.

Science.gov (United States)

Goffman, Rachel M; Harris, Shannon L; May, Jerrold H; Milicevic, Aleksandra S; Monte, Robert J; Myaskovsky, Larissa; Rodriguez, Keri L; Tjader, Youxu C; Vargas, Dominic L

2017-05-01

Missed appointments reduce the efficiency of the health care system and negatively impact access to care for all patients. Identifying patients at risk for missing an appointment could help health care systems and providers better target interventions to reduce patient no-shows. Our aim was to develop and test a predictive model that identifies patients that have a high probability of missing their outpatient appointments. Demographic information, appointment characteristics, and attendance history were drawn from the existing data sets from four Veterans Affairs health care facilities within six separate service areas. Past attendance behavior was modeled using an empirical Markov model based on up to 10 previous appointments. Using logistic regression, we developed 24 unique predictive models. We implemented the models and tested an intervention strategy using live reminder calls placed 24, 48, and 72 hours ahead of time. The pilot study targeted 1,754 high-risk patients, whose probability of missing an appointment was predicted to be at least 0.2. Our results indicate that three variables were consistently related to a patient's no-show probability in all 24 models: past attendance behavior, the age of the appointment, and having multiple appointments scheduled on that day. After the intervention was implemented, the no-show rate in the pilot group was reduced from the expected value of 35% to 12.16% (p value < 0.0001). The predictive model accurately identified patients who were more likely to miss their appointments. Applying the model in practice enables clinics to apply more intensive intervention measures to high-risk patients. Reprint & Copyright © 2017 Association of Military Surgeons of the U.S.

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].

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

OpenAIRE

Alejandro Baldominos Gómez; Esperanza Albacete; Ignacio Merrero; Yago Saez

2016-01-01

This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user's actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to ...

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…

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.

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,…

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

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

Measurement and Prediction of Time-independent and Time-dependent Rheological Behavior of Waxy Crude Oil

Directory of Open Access Journals (Sweden)

Yavar Karimi

2017-01-01

Full Text Available Wax deposition phenomenon changes the rheological behavior of waxy crude oil completely. In the current work, the rheological time-dependent and time-independent behaviors of waxy crude oil samples are studied and flow curve and compliance function are measured for the oil samples with various wax contents at different temperatures. A decrease in temperature and an increase in wax content lead to an increase in the viscosity and yield stress but a significant drop in compliance function. A modified Burger model is developed to predict the behavior of the compliance function and a modified Casson model is used to predict the flow curve of the waxy crude oil samples within a vast range of wax contents and temperatures. The proposed Burger and Casson models match with experimental results with R2 of 99.7% and 97.33% respectively.

Initial sociometric impressions of attention-deficit hyperactivity disorder and comparison boys: predictions from social behaviors and from nonbehavioral variables.

Science.gov (United States)

Erhardt, Drew; Hinshaw, Stephen P

1994-08-01

This study systematically compared the influence of naturalistic social behaviors and nonbehavioral variables on the development of peer status in 49 previously unfamiliar boys, aged 6-12 years, who attended a summer research program. Twenty-five boys with attention-deficit hyperactivity disorder (ADHD) and 24 comparison boys participated. Physical attractiveness, motor competence, intelligence, and academic achievement constituted the nonbehavioral variables; social behaviors included noncompliance, aggression, prosocial actions, and isolation, measured by live observations of classroom and playground interactions. As early as the first day of interaction, ADHD and comparison boys displayed clear differences in social behaviors, and the ADHD youngsters were overwhelmingly rejected. Whereas prosocial behavior independently predicted friendship ratings during the first week, the magnitude of prediction was small. In contrast, the boys' aggression (or noncompliance) strongly predicted negative nominations, even with nonbehavioral factors, group status (ADHD versus comparison), and other social behaviors controlled statistically. Implications for understanding and remediating negative peer reputations are discussed.

Impairments in goal-directed actions predict treatment response to cognitive-behavioral therapy in social anxiety disorder.

Directory of Open Access Journals (Sweden)

Gail A Alvares

Full Text Available Social anxiety disorder is characterized by excessive fear and habitual avoidance of social situations. Decision-making models suggest that patients with anxiety disorders may fail to exhibit goal-directed control over actions. We therefore investigated whether such biases may also be associated with social anxiety and to examine the relationship between such behavior with outcomes from cognitive-behavioral therapy. Patients diagnosed with social anxiety and controls completed an instrumental learning task in which two actions were performed to earn food outcomes. After outcome devaluation, where one outcome was consumed to satiety, participants were re-tested in extinction. Results indicated that, as expected, controls were goal-directed, selectively reducing responding on the action that previously delivered the devalued outcome. Patients with social anxiety, however, exhibited no difference in responding on either action. This loss of a devaluation effect was associated with greater symptom severity and poorer response to therapy. These findings indicate that variations in goal-directed control in social anxiety may represent both a behavioral endophenotype and may be used to predict individuals who will respond to learning-based therapies.

Profit Driven Decision Trees for Churn Prediction

OpenAIRE

Höppner, Sebastiaan; Stripling, Eugen; Baesens, Bart; Broucke, Seppe vanden; Verdonck, Tim

2017-01-01

Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are built with the purpose of accurately predicting the probability of a customer defecting. The predictive churn models are then commonly selected based on accuracy related performan...

Review of time-dependent fatigue behavior and life prediction for 2 1/4 Cr-1 Mo steel

International Nuclear Information System (INIS)

Booker, M.K.; Majumdar, S.

1982-01-01

Available data on creep-fatigue life and fracture behavior of 2 1/4 Cr-1 Mo steel are reviewed. Whereas creep-fatigue interaction is important for Type 304 stainless steel, oxidation effects appear to dominate the time-dependent fatigue behavior of 2 1/4 Cr-1 Mo steel. Four of the currently available predictive methods - the Linear Damage Rule, Frequency Separation Equation, Strain Range Partitioning Equation, and Damage Rate Equation - are evaluated for their predictive capability. Variations in the parameters for the various predictive methods with temperature, heat of material, heat treatment, and environment are investigated. Relative trends in the lives predicted by the various methods as functions of test duration, waveshape, etc., are discussed. The predictive methods will need modification in order to account for oxidation and aging effects in the 2 1/4 Cr-1 Mo steel. Future tests that will emphasize the difference between the various predictive methods are proposed

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

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.

Creep Crack Initiation and Growth Behavior for Ni-Base Superalloys

Science.gov (United States)

Nagumo, Yoshiko; Yokobori, A. Toshimitsu, Jr.; Sugiura, Ryuji; Ozeki, Go; Matsuzaki, Takashi

The structural components which are used in high temperature gas turbines have various shapes which may cause the notch effect. Moreover, the site of stress concentration might have the heterogeneous microstructural distribution. Therefore, it is necessary to clarify the creep fracture mechanism for these materials in order to predict the life of creep fracture with high degree of accuracy. In this study, the creep crack growth tests were performed using in-situ observational testing machine with microscope to observe the creep damage formation and creep crack growth behavior. The materials used are polycrystalline Ni-base superalloy IN100 and directionally solidified Ni-base superalloy CM247LC which were developed for jet engine turbine blades and gas turbine blades in electric power plants, respectively. The microstructural observation of the test specimens was also conducted using FE-SEM/EBSD. Additionally, the analyses of two-dimensional elastic-plastic creep finite element using designed methods were conducted to understand the effect of microstructural distribution on creep damage formation. The experimental and analytical results showed that it is important to determine the creep crack initiation and early crack growth to predict the life of creep fracture and it is indicated that the highly accurate prediction of creep fracture life could be realized by measuring notch opening displacement proposed as the RNOD characteristic.

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…

Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

Science.gov (United States)

Jelínek, Jan; Škoda, Petr; Hoksza, David

2017-12-06

Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.

Characterizing and modeling the pressure- and rate-dependent elastic-plastic-damage behaviors of polypropylene-based polymers

KAUST Repository

Pulungan, Ditho Ardiansyah

2018-02-24

Polymers in general exhibit pressure- and rate-dependent behavior. Modeling such behavior requires extensive, costly and time-consuming experimental work. Common simplifications may lead to severe inaccuracy when using the model for predicting the failure of structures. Here, we propose a viscoelastic viscoplastic damage model for polypropylene-based polymers. Such a set of constitutive equations can be used to describe the response of polypropylene under various strain-rates and stress-triaxiality conditions. Our model can also be applied to a broad range of thermoplastic polymers. We detail the experimental campaign that is needed to identify every parameter of the model at best. We validated the proposed model by performing 3-point bending tests at different loading speeds, where the load-displacement response of polypropylene beam up to failure was accurately predicted.

QSAR models for prediction of chromatographic behavior of homologous Fab variants.

Science.gov (United States)

Robinson, Julie R; Karkov, Hanne S; Woo, James A; Krogh, Berit O; Cramer, Steven M

2017-06-01

While quantitative structure activity relationship (QSAR) models have been employed successfully for the prediction of small model protein chromatographic behavior, there have been few reports to date on the use of this methodology for larger, more complex proteins. Recently our group generated focused libraries of antibody Fab fragment variants with different combinations of surface hydrophobicities and electrostatic potentials, and demonstrated that the unique selectivities of multimodal resins can be exploited to separate these Fab variants. In this work, results from linear salt gradient experiments with these Fabs were employed to develop QSAR models for six chromatographic systems, including multimodal (Capto MMC, Nuvia cPrime, and two novel ligand prototypes), hydrophobic interaction chromatography (HIC; Capto Phenyl), and cation exchange (CEX; CM Sepharose FF) resins. The models utilized newly developed "local descriptors" to quantify changes around point mutations in the Fab libraries as well as novel cluster descriptors recently introduced by our group. Subsequent rounds of feature selection and linearized machine learning algorithms were used to generate robust, well-validated models with high training set correlations (R 2  > 0.70) that were well suited for predicting elution salt concentrations in the various systems. The developed models then were used to predict the retention of a deamidated Fab and isotype variants, with varying success. The results represent the first successful utilization of QSAR for the prediction of chromatographic behavior of complex proteins such as Fab fragments in multimodal chromatographic systems. The framework presented here can be employed to facilitate process development for the purification of biological products from product-related impurities by in silico screening of resin alternatives. Biotechnol. Bioeng. 2017;114: 1231-1240. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Predicting Participation in Dual Language Immersion Using Theory of Planned Behavior

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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…

Predictive Validity of the Columbia-Suicide Severity Rating Scale for Short-Term Suicidal Behavior: A Danish Study of Adolescents at a High Risk of Suicide.

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Conway, Paul Maurice; Erlangsen, Annette; Teasdale, Thomas William; Jakobsen, Ida Skytte; Larsen, Kim Juul

2017-07-03

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 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 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 at follow-up over and above suicidal behavior at baseline. Actual suicide attempts at baseline strongly predicted suicide attempts at follow-up. Baseline suicidal ideation severity and intensity did not significantly predict future actual attempts over and above baseline attempts. The suicidal ideation intensity items deterrents and duration were significant predictors of subsequent actual attempts after adjustment for baseline suicide attempts and suicidal behavior of any type, respectively. Suicidal ideation severity and intensity, and the intensity items frequency, duration and deterrents, all significantly predicted any type of suicidal behavior at follow-up, also after adjusting for baseline suicidal behavior. The present study points to an incremental predictive validity of the C-SSRS suicidal ideation scales for short-term suicidal behavior of any type among high-risk adolescents.

Short-Term Predictive Validity of Cluster Analytic and Dimensional Classification of Child Behavioral Adjustment in School

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Kim, Sangwon; Kamphaus, Randy W.; Baker, Jean A.

2006-01-01

A constructive debate over the classification of child psychopathology can be stimulated by investigating the validity of different classification approaches. We examined and compared the short-term predictive validity of cluster analytic and dimensional classifications of child behavioral adjustment in school using the Behavior Assessment System…

Predictive Effects of Good Self-Control and Poor Regulation on Alcohol-Related Outcomes: Do Protective Behavioral Strategies Mediate?

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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

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

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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.

Prediction and Cross-Situational Consistency of Daily Behavior across Cultures: Testing Trait and Cultural Psychology Perspectives

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Church, A. Timothy; Katigbak, Marcia S.; Reyes, Jose Alberto S.; Salanga, Maria Guadalupe C.; Miramontes, Lilia A.; Adams, Nerissa B.

2008-01-01

Trait and cultural psychology perspectives on the cross-situational consistency of behavior, and the predictive validity of traits, were tested in a daily process study in the United States (N = 68), an individualistic culture, and the Philippines (N = 80), a collectivistic culture. Participants completed the Revised NEO Personality Inventory (Costa & McCrae, 1992) and a measure of self-monitoring, then reported their daily behaviors and associated situational contexts for approximately 30 days. Consistent with trait perspectives, the Big Five traits predicted daily behaviors in both cultures, and relative (interindividual) consistency was observed across many, although not all, situational contexts. The frequency of various Big Five behaviors varied across relevant situational contexts in both cultures and, consistent with cultural psychology perspectives, there was a tendency for Filipinos to exhibit greater situational variability than Americans. Self-monitoring showed some ability to account for individual differences in situational variability in the American sample, but not the Filipino sample. PMID:22146866

Predicting dynamic behavior via anticipating synchronization in coupled pendulum-like systems

International Nuclear Information System (INIS)

Xu Shiyun; Yang Ying

2009-01-01

In this paper, the regime of anticipating synchronization (sometimes called predicted synchronization) in a class of nonlinear dynamical systems is investigated by testing the global asymptotical stability of time-delayed error dynamics. Sufficient conditions in terms of linear matrix inequalities are established for anticipating synchronization between such systems with and without state time delay. These results allow one to predict the dynamic behavior of the systems by using a copy of the same system that performs as a slave. Moreover, the cascaded anticipating synchronization is concerned such that several slave systems could anticipate the same master system with different delays. Concrete applications to phase-locked loops demonstrate the applicability and validity of the proposed results.

Gender-based violence and HIV sexual risk behavior: alcohol use and mental health problems as mediators among women in drinking venues, Cape Town.

Science.gov (United States)

Pitpitan, Eileen V; Kalichman, Seth C; Eaton, Lisa A; Sikkema, Kathleen J; Watt, Melissa H; Skinner, Donald

2012-10-01

Gender-based violence is a key determinant of HIV infection among women in South Africa as elsewhere. However, research has not examined potential mediating processes to explain the link between experiencing abuse and engaging in HIV sexual risk behavior. Previous studies suggest that alcohol use and mental health problems may explain how gender-based violence predicts sexual risk. In a prospective study, we examined whether lifetime history of gender-based violence indirectly affects future sexual risk behavior through alcohol use, depression and post-traumatic stress disorder (PTSD) in a high-risk socio-environmental context. We recruited a cohort of 560 women from alcohol drinking venues in a Cape Town, South African township. Participants completed computerized interviews at baseline and 4 months later. We tested prospective mediating associations between gender-based violence, alcohol use, depression, PTSD, and sexual risk behavior. There was a significant indirect effect of gender-based violence on sexual risk behavior through alcohol use, but not mental health problems. Women who were physically and sexually abused drank more, which in turn predicted more unprotected sex. We did not find a mediated relationship between alcohol use and sexual risk behavior through the experience of recent abuse or mental health problems. Alcohol use explains the link between gender-based violence and sexual risk behavior among women attending drinking venues in Cape Town, South Africa. Efforts to reduce HIV risk in South Africa by addressing gender-based violence must also address alcohol use. Copyright © 2012 Elsevier Ltd. All rights reserved.

Coupled thermomechanical behavior of graphene using the spring-based finite element approach

Energy Technology Data Exchange (ETDEWEB)

Georgantzinos, S. K., E-mail: sgeor@mech.upatras.gr; Anifantis, N. K., E-mail: nanif@mech.upatras.gr [Machine Design Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, Rio, 26500 Patras (Greece); Giannopoulos, G. I., E-mail: ggiannopoulos@teiwest.gr [Materials Science Laboratory, Department of Mechanical Engineering, Technological Educational Institute of Western Greece, 1 Megalou Alexandrou Street, 26334 Patras (Greece)

2016-07-07

The prediction of the thermomechanical behavior of graphene using a new coupled thermomechanical spring-based finite element approach is the aim of this work. Graphene sheets are modeled in nanoscale according to their atomistic structure. Based on molecular theory, the potential energy is defined as a function of temperature, describing the interatomic interactions in different temperature environments. The force field is approached by suitable straight spring finite elements. Springs simulate the interatomic interactions and interconnect nodes located at the atomic positions. Their stiffness matrix is expressed as a function of temperature. By using appropriate boundary conditions, various different graphene configurations are analyzed and their thermo-mechanical response is approached using conventional finite element procedures. A complete parametric study with respect to the geometric characteristics of graphene is performed, and the temperature dependency of the elastic material properties is finally predicted. Comparisons with available published works found in the literature demonstrate the accuracy of the proposed method.

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.

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.

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

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.

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.

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

Prediction of Physical Activity Level Using Processes of Change From the Transtheoretical Model: Experiential, Behavioral, or an Interaction Effect?

Science.gov (United States)

Romain, Ahmed Jérôme; Horwath, Caroline; Bernard, Paquito

2018-01-01

The purpose of the present study was to compare prediction of physical activity (PA) by experiential or behavioral processes of change (POCs) or an interaction between both types of processes. A cross-sectional study. This study was conducted using an online questionnaire. A total of 394 participants (244 women, 150 men), with a mean age of 35.12 ± 12.04 years and a mean body mass index of 22.97 ± 4.25 kg/m 2 were included. Participants completed the Processes of Change, Stages of Change questionnaires, and the International Physical Activity Questionnaire to evaluate self-reported PA level (total, vigorous, and moderate PA). Hierarchical multiple regression models were used to test the prediction of PA level. For both total PA (β = .261; P behavioral POCs were a significant predictor. Regarding moderate PA, only the interaction between experiential and behavioral POCs was a significant predictor (β = .123; P = .017). Our results provide confirmation that behavioral processes are most prominent in PA behavior. Nevertheless, it is of interest to note that the interaction between experiential and behavioral POCs was the only element predicting moderate PA level. Experiential processes were not associated with PA level.

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...

Predicting Social Support for Grieving Persons: A Theory of Planned Behavior Perspective

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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…

Predictability of Mobile Phone Associations

DEFF Research Database (Denmark)

Jensen, Bjørn Sand; Larsen, Jan; Hansen, Lars Kai

2010-01-01

Prediction and understanding of human behavior is of high importance in many modern applications and research areas ranging from context-aware services, wireless resource allocation to social sciences. In this study we collect a novel dataset using standard mobile phones and analyze how the predi...... representation, and general behavior. This is of vital interest in the development of context-aware services which rely on forecasting based on mobile phone sensors.......Prediction and understanding of human behavior is of high importance in many modern applications and research areas ranging from context-aware services, wireless resource allocation to social sciences. In this study we collect a novel dataset using standard mobile phones and analyze how...... the predictability of mobile sensors, acting as proxies for humans, change with time scale and sensor type such as GSM and WLAN. Applying recent information theoretic methods, it is demonstrated that an upper bound on predictability is relatively high for all sensors given the complete history (typically above 90...

Prediction of Happy-Sad Mood from Daily Behaviors and Previous Sleep History

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Sano, Akane; Yu, Amy; McHill, Andrew W.; Phillips, Andrew J. K.; Taylor, Sara; Jaques, Natasha; Klerman, Elizabeth B.; Picard, Rosalind W.

2016-01-01

We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants, for 30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other factors in college students. We analyzed daily and monthly behavior and physiology and identified factors that affect mood, including how accurately sleep duration and sleep regularity for the past 1-5 days classified the participants into high/low mood using support vector machines. We found statistically significant associations among sad mood and poor health-related factors. Behavioral factors such as the percentage of neutral social interactions and the total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity was a more important discriminator of mood than sleep duration for most participants, although both variables predicted happy/sad mood with from 70-82% accuracy. The number of nights giving the best prediction of happy/sad mood varied for different groups of individuals. PMID:26737854

Driving Green: Toward the Prediction and Influence of Efficient Driving Behavior

Science.gov (United States)

Newsome, William D.

Sub-optimal efficiency in activities involving the consumption of fossil fuels, such as driving, contribute to a miscellany of negative environmental, political, economic and social externalities. Demonstrations of the effectiveness of feedback interventions can be found in countless organizational settings, as can demonstrations of individual differences in sensitivity to feedback interventions. Mechanisms providing feedback to drivers about fuel economy are becoming standard equipment in most new vehicles, but vary considerably in their constitution. A keystone of Radical Behaviorism is the acknowledgement that verbal behavior appears to play a role in mediating apparent susceptibility to influence by contingencies of varying delay. In the current study, samples of verbal behavior (rules) were collected in the context of a feedback intervention to improve driving efficiency. In an analysis of differences in individual responsiveness to the feedback intervention, the rate of novel rules per week generated by drivers is revealed to account for a substantial proportion of the variability in relative efficiency gains across participants. The predictive utility of conceptual tools, such as the basic distinction among contingency-shaped and rule governed behavior, the elaboration of direct-acting and indirect-acting contingencies, and the psychological flexibility model, is bolstered by these findings.

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

Science.gov (United States)

Trendel, Olivier; Werle, Carolina O C

2016-09-01

Eating behaviors largely result from automatic processes. Yet, in existing research, automatic or implicit attitudes toward food often fail to predict eating behaviors. Applying findings in cognitive neuroscience research, we propose and find that a central reason why implicit attitudes toward food are not good predictors of eating behaviors is that implicit attitudes are driven by two distinct constructs that often have diverging evaluative consequences: the automatic affective reactions to food (e.g., tastiness; the affective basis of implicit attitudes) and the automatic cognitive reactions to food (e.g., healthiness; the cognitive basis of implicit attitudes). More importantly, we find that the affective and cognitive bases of implicit attitudes directly and uniquely influence actual food choices under different conditions. While the affective basis of implicit attitude is the main driver of food choices, it is the only driver when cognitive resources during choice are limited. The cognitive basis of implicit attitudes uniquely influences food choices when cognitive resources during choice are plentiful but only for participants low in impulsivity. Researchers interested in automatic processes in eating behaviors could thus benefit by distinguishing between the affective and cognitive bases of implicit attitudes. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

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.

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

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.

Contrasting losses and gains increases the predictability of behavior by frontal EEG asymmetry

Science.gov (United States)

Telpaz, Ariel; Yechiam, Eldad

2014-01-01

Frontal asymmetry measured at rest using EEG is considered a stable marker of approach-avoidance behaviors and risk taking. We examined whether without salient cues of attention in the form of losses, predictability is reduced. Fifty-seven participants performed an experiential decision task in a gain-only, loss-only, and mixed (gains and losses) condition. Increased risk taking on the part of individuals with relatively high left frontal activation, as denoted by the Alpha band, was only observed in the task involving both gains and losses. Event-related potential analysis sheds light on the processes leading to this pattern. Left-frontal dominant individuals had increased fronto-central P300 activation following risky compared to safe outcomes, while right-frontal dominant individuals did not show a P300 difference following safe and risky outcomes. This interaction also only emerged when losses were contrasted with gains. The findings highlight the sensitivity of behavioral predictability to cues of valence. PMID:24817845

The family environment predicts long-term academic achievement and classroom behavior following traumatic brain injury in early childhood.

Science.gov (United States)

Durber, Chelsea M; Yeates, Keith Owen; Taylor, H Gerry; Walz, Nicolay Chertkoff; Stancin, Terry; Wade, Shari L

2017-07-01

This study examined how the family environment predicts long-term academic and behavioral functioning in school following traumatic brain injury (TBI) in early childhood. Using a concurrent cohort, prospective design, 15 children with severe TBI, 39 with moderate TBI, and 70 with orthopedic injury (OI) who were injured when they were 3-7 years of age were compared on tests of academic achievement and parent and teacher ratings of school performance and behavior on average 6.83 years postinjury. Soon after injury and at the longer term follow-up, families completed measures of parental psychological distress, family functioning, and quality of the home environment. Hierarchical linear regression analyses examined group differences in academic outcomes and their associations with measures of the early and later family environment. The severe TBI group, but not the moderate TBI group, performed worse than did the OI group on all achievement tests, parent ratings of academic performance, and teacher ratings of internalizing problems. Higher quality early and late home environments predicted stronger academic skills and better classroom behavior for children with both TBI and OI. The early family environment more consistently predicted academic achievement, whereas the later family environment more consistently predicted classroom functioning. The quality of the home environment predicted academic outcomes more strongly than did parental psychological distress or family functioning. TBI in early childhood has long-term consequences for academic achievement and school performance and behavior. Higher quality early and later home environments predict better school outcomes for both children with TBI and children with OI. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Using the Theory of Planned Behavior to Predict College Students' Intention to Intervene With a Suicidal Individual.

Science.gov (United States)

Aldrich, Rosalie S

2015-01-01

Suicide among college students is an issue of serious concern. College peers may effectively intervene with at-risk persons due to their regular contact and close personal relationships with others in this population of significantly enhanced risk. The current study was designed to investigate whether the theory of planned behavior constructs predicted intention to intervene when a college peer is suicidal. Undergraduate students (n = 367) completed an on-line questionnaire; they answered questions about their attitudes, subjective norms, perceived behavioral control regarding suicide and suicide intervention, as well as their intention to intervene when someone is suicidal. The data were analyzed using multiple regression. The statistical significance of this cross-sectional study indicates that the theory of planned behavior constructs predicts self-reported intention to intervene with a suicidal individual. Theory of planned behavior is an effective framework for understanding peers' intention to intervene with a suicidal individual.

Predicting compliance with an information-based residential outdoor water conservation program

Science.gov (United States)

Landon, Adam C.; Kyle, Gerard T.; Kaiser, Ronald A.

2016-05-01

Residential water conservation initiatives often involve some form of education or persuasion intended to change the attitudes and behaviors of residential consumers. However, the ability of these instruments to change attitudes toward conservation and their efficacy in affecting water use remains poorly understood. In this investigation the authors examine consumer attitudes toward complying with a persuasive water conservation program, the extent to which those attitudes predict compliance, and the influence of environmental contextual factors on outdoor water use. Results indicate that the persuasive program was successful in developing positive attitudes toward compliance, and that those attitudes predict water use. However, attitudinal variables explain a relatively small proportion of the variance in objectively measured water use behavior. Recommendations for policy are made stressing the importance of understanding both the effects of attitudes and environmental contextual factors in behavior change initiatives in the municipal water sector.

Use of thermodynamic calculation to predict the effect of Si on the ageing behavior of Al-Mg-Si-Cu alloys

International Nuclear Information System (INIS)

Ji, Yanli; Zhong, Hao; Hu, Ping; Guo, Fuan

2011-01-01

Research highlights: → Thermodynamic calculation can predict the ageing behavior of 6xxx alloys. → The hardness level of the alloys depends on the Si content in as-quenched matrix. → The precipitation strengthening effect depends on the Mg 2 Si level of the alloys. -- Abstract: Thermodynamic calculation was employed to predict the influence of Si content on the ageing behavior of Al-Mg-Si-Cu alloys. In addition, experiments were carried out to verify the predictions. The results show that thermodynamic calculation can predict the effect of Si content on the ageing behavior of the studied alloys. This study further proposes that the hardness level of alloys during ageing is directly related to the Si content in the as-quenched supersaturated solution, while the precipitation strengthening effect is directly related to the Mg 2 Si level of the alloys.

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.

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.

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.

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.

An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior

Energy Technology Data Exchange (ETDEWEB)

Sabbaghi, Mostafa, E-mail: mostafas@buffalo.edu [Industrial and Systems Engineering Department, State University of New York, University at Buffalo, 437 Bell Hall, Buffalo, NY (United States); Esmaeilian, Behzad, E-mail: b.esmaeilian@neu.edu [Healthcare Systems Engineering Institute, Northeastern University, Boston, MA 02115 (United States); Raihanian Mashhadi, Ardeshir, E-mail: ardeshir@buffalo.edu [Mechanical and Aerospace Engineering, State University of New York, University at Buffalo, 437 Bell Hall, Buffalo, NY (United States); Behdad, Sara, E-mail: sarabehd@buffalo.edu [Industrial and Systems Engineering Department, State University of New York, University at Buffalo, 437 Bell Hall, Buffalo, NY (United States); Mechanical and Aerospace Engineering, State University of New York, University at Buffalo, 437 Bell Hall, Buffalo, NY (United States); Cade, Willie, E-mail: willie@pcrr.com [PC Rebuilder and Recyclers, 4734 W Chicago Ave, Chicago, IL 60651-3322 (United States)

2015-02-15

Highlights: • We analyzed a data set of HDDs returned back to an e-waste collection site. • We studied factors that affect the storage behavior. • Consumer type, brand and size are among factors which affect the storage behavior. • Commercial consumers have stored computers more than household consumers. • Machine learning models were used to predict the storage behavior. - Abstract: Consumers often have a tendency to store their used, old or un-functional electronics for a period of time before they discard them and return them back to the waste stream. This behavior increases the obsolescence rate of used still-functional products leading to lower profitability that could be resulted out of End-of-Use (EOU) treatments such as reuse, upgrade, and refurbishment. These types of behaviors are influenced by several product and consumer-related factors such as consumers’ traits and lifestyles, technology evolution, product design features, product market value, and pro-environmental stimuli. Better understanding of different groups of consumers, their utilization and storage behavior and the connection of these behaviors with product design features helps Original Equipment Manufacturers (OEMs) and recycling and recovery industry to better overcome the challenges resulting from the undesirable storage of used products. This paper aims at providing insightful statistical analysis of Electronic Waste (e-waste) dynamic nature by studying the effects of design characteristics, brand and consumer type on the electronics usage time and end of use time-in-storage. A database consisting of 10,063 Hard Disk Drives (HDD) of used personal computers returned back to a remanufacturing facility located in Chicago, IL, USA during 2011–2013 has been selected as the base for this study. The results show that commercial consumers have stored computers more than household consumers regardless of brand and capacity factors. Moreover, a heterogeneous storage behavior is

An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior

International Nuclear Information System (INIS)

Sabbaghi, Mostafa; Esmaeilian, Behzad; Raihanian Mashhadi, Ardeshir; Behdad, Sara; Cade, Willie

2015-01-01

Highlights: • We analyzed a data set of HDDs returned back to an e-waste collection site. • We studied factors that affect the storage behavior. • Consumer type, brand and size are among factors which affect the storage behavior. • Commercial consumers have stored computers more than household consumers. • Machine learning models were used to predict the storage behavior. - Abstract: Consumers often have a tendency to store their used, old or un-functional electronics for a period of time before they discard them and return them back to the waste stream. This behavior increases the obsolescence rate of used still-functional products leading to lower profitability that could be resulted out of End-of-Use (EOU) treatments such as reuse, upgrade, and refurbishment. These types of behaviors are influenced by several product and consumer-related factors such as consumers’ traits and lifestyles, technology evolution, product design features, product market value, and pro-environmental stimuli. Better understanding of different groups of consumers, their utilization and storage behavior and the connection of these behaviors with product design features helps Original Equipment Manufacturers (OEMs) and recycling and recovery industry to better overcome the challenges resulting from the undesirable storage of used products. This paper aims at providing insightful statistical analysis of Electronic Waste (e-waste) dynamic nature by studying the effects of design characteristics, brand and consumer type on the electronics usage time and end of use time-in-storage. A database consisting of 10,063 Hard Disk Drives (HDD) of used personal computers returned back to a remanufacturing facility located in Chicago, IL, USA during 2011–2013 has been selected as the base for this study. The results show that commercial consumers have stored computers more than household consumers regardless of brand and capacity factors. Moreover, a heterogeneous storage behavior is

Development of a brief substance use sensation seeking scale: validation and prediction of injection-related behaviors.

Science.gov (United States)

Werb, Dan; Richardson, Chris; Buxton, Jane; Shoveller, Jeannie; Wood, Evan; Kerr, Thomas

2015-02-01

Sensation seeking, a personality trait, has been shown to predict engagement in high-risk behaviors. However, little is known regarding the impact of sensation seeking on substance use among street youth. We therefore sought to modify a sensation seeking scale (SSS) for use among this population. Street youth from the Vancouver-based At-Risk Youth Study (n = 226) completed the modified SSS. Exploratory and confirmatory factor analysis (EFA/CFA) were undertaken to establish the scale's dimensionality and internal validity. The association between SSS score and injection-related behaviors was tested using generalized estimating equation analysis. EFA results indicated scale unidimensionality. The comparative fit index (CFI) suggested acceptable fit (CFI = 0.914). In multivariate analysis, sensation seeking was independently associated with injection drug use, crystal methamphetamine use, polysubstance use, and binge drug use (all p < 0.05). Our findings provide preliminary support for the use of the modified SSS among street youth.

Ductility prediction of substrate-supported metal layers based on rate-independent crystal plasticity theory

Directory of Open Access Journals (Sweden)

Akpama Holanyo K.

2016-01-01

Full Text Available In this paper, both the bifurcation theory and the initial imperfection approach are used to predict localized necking in substrate-supported metal layers. The self-consistent scale-transition scheme is used to derive the mechanical behavior of a representative volume element of the metal layer from the behavior of its microscopic constituents (the single crystals. The mechanical behavior of the elastomer substrate follows the neo-Hookean hyperelastic model. The adherence between the two layers is assumed to be perfect. Through numerical results, it is shown that the limit strains predicted by the initial imperfection approach tend towards the bifurcation predictions when the size of the geometric imperfection in the metal layer vanishes. Also, it is shown that the addition of an elastomer layer to a metal layer enhances ductility.

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.

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.

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.

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

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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

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.

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)

Examining and Predicting College Students' Reading Intentions and Behaviors: An Application of the Theory of Reasoned Action

Science.gov (United States)

Burak, Lydia

2004-01-01

This study examined the recreational reading attitudes, intentions, and behaviors of college students. The theory of reasoned action provided the framework for the investigation and prediction of the students' intentions and behaviors. Two hundred and one students completed questionnaires developed according to the guidelines for the construction…

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.

Enjoyment and Behavioral Intention Predict Organized Youth Sport Participation and Dropout.

Science.gov (United States)

Gardner, Lauren A; Magee, Christopher A; Vella, Stewart A

2017-11-01

Dropout from organized youth sport has significant adverse health implications. Enjoyment and behavioral intentions have consistently been linked with participation and dropout; however, few studies have investigated these links using a prospective design. This study explored whether enjoyment and intentions to continue predicted dropout behavior at 1-year follow-up. Questionnaires were completed by 327 regular sport participants (mean age = 13.01 y at baseline). After 1 year, 247 individuals (75.5%) continued participating in their main sport and 26 individuals (8%) dropped out. A hierarchical logistic regression model estimated the probability of dropout. In step 1, the following covariates were included: age, sex, competition level, perceived competence, parental support, coach-athlete relationship, friendship quality, and peer acceptance. In step 2, enjoyment and intentions to continue were included. Step 1 indicated that age, parental support, coach-athlete relationship quality, and peer acceptance were significantly associated with dropout. Step 2 explained further variance in dropout, with both enjoyment and intentions inversely associated with dropout. Peer acceptance was the only covariate to remain significantly associated with dropout in step 2. Findings support the use of enjoyment and behavioral intentions as indicators of sport participation/dropout behavior and may aid the development of interventions aimed at preventing future dropout.

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.

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.

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