The Relationship of Hope and Strength's Self-Efficacy to the Social Change Model of Leadership

ERIC Educational Resources Information Center

Lane, Forrest C.; Chapman, Natasha H.

2011-01-01

The social change model of leadership (SCM) is a widely used leadership model in higher education. StrengthsQuest is conceptually similar to the individual values of the SCM in its aim to identify and grow individual talents. This model is based on the idea that individuals perform at higher levels when they build upon their identified talents…

Individual Markers of Resilience in Train Traffic Control: The Role of Operators' Goals and Strategic Mental Models and Implications for Variation, Expertise, and Performance.

PubMed

Lo, Julia C; Pluyter, Kari R; Meijer, Sebastiaan A

2016-02-01

The aim of this study was to examine individual markers of resilience and obtain quantitative insights into the understanding and the implications of variation and expertise levels in train traffic operators' goals and strategic mental models and their impact on performance. The Dutch railways are one of the world's most heavy utilized railway networks and have been identified to be weak in system and organizational resilience. Twenty-two train traffic controllers enacted two scenarios in a human-in-the-loop simulator. Their experience, goals, strategic mental models, and performance were assessed through questionnaires and simulator logs. Goals were operationalized through performance indicators and strategic mental models through train completion strategies. A variation was found between operators for both self-reported primary performance indicators and completion strategies. Further, the primary goal of only 14% of the operators reflected the primary organizational goal (i.e., arrival punctuality). An incongruence was also found between train traffic controllers' self-reported performance indicators and objective performance in a more disrupted condition. The level of experience tends to affect performance differently. There is a gap between primary organizational goals and preferred individual goals. Further, the relative strong diversity in primary operator goals and strategic mental models indicates weak resilience at the individual level. With recent and upcoming large-scale changes throughout the sociotechnical space of the railway infrastructure organization, the findings are useful to facilitate future railway traffic control and the development of a resilient system. © 2015, Human Factors and Ergonomics Society.

Estimating thermal performance curves from repeated field observations

USGS Publications Warehouse

Childress, Evan; Letcher, Benjamin H.

2017-01-01

Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.

Speeded Old-New Recognition of Multidimensional Perceptual Stimuli: Modeling Performance at the Individual-Participant and Individual-Item Levels

ERIC Educational Resources Information Center

Nosofsky, Robert M.; Stanton, Roger D.

2006-01-01

Observers made speeded old-new recognition judgments of color stimuli embedded in a multidimensional similarity space. The paradigm used multiple lists but with the underlying similarity structures repeated across lists, to allow for quantitative modeling of the data at the individual-participant and individual-item levels. Correct rejection…

A hierarchical model of the evolution of cooperation in cultural systems.

PubMed

Savatsky, K; Reynolds, R G

1989-01-01

In this paper the following problem is addressed: "Under what conditions can a collection of individual organisms learn to cooperate when cooperation appears to outwardly degrade individual performance at the outset. In order to attempt a theoretical solution to this problem, data from a real world problem in anthropology is used. A distributed simulation model of this system was developed to assess its long term behavior using using an approach suggested by Zeigler (Zeigler, B.P., 1984, Multifaceted Modelling and Discrete Event Simulation (Academic Press, London)). The results of the simulation are used to show that although cooperation degrades the performance potential of each individual, it enhances the persistence of the individual's partial solution to the problem in certain situations."

An individual reproduction model sensitive to milk yield and body condition in Holstein dairy cows.

PubMed

Brun-Lafleur, L; Cutullic, E; Faverdin, P; Delaby, L; Disenhaus, C

2013-08-01

To simulate the consequences of management in dairy herds, the use of individual-based herd models is very useful and has become common. Reproduction is a key driver of milk production and herd dynamics, whose influence has been magnified by the decrease in reproductive performance over the last decades. Moreover, feeding management influences milk yield (MY) and body reserves, which in turn influence reproductive performance. Therefore, our objective was to build an up-to-date animal reproduction model sensitive to both MY and body condition score (BCS). A dynamic and stochastic individual reproduction model was built mainly from data of a single recent long-term experiment. This model covers the whole reproductive process and is composed of a succession of discrete stochastic events, mainly calving, ovulations, conception and embryonic loss. Each reproductive step is sensitive to MY or BCS levels or changes. The model takes into account recent evolutions of reproductive performance, particularly concerning calving-to-first ovulation interval, cyclicity (normal cycle length, prevalence of prolonged luteal phase), oestrus expression and pregnancy (conception, early and late embryonic loss). A sensitivity analysis of the model to MY and BCS at calving was performed. The simulated performance was compared with observed data from the database used to build the model and from the bibliography to validate the model. Despite comprising a whole series of reproductive steps, the model made it possible to simulate realistic global reproduction outputs. It was able to well simulate the overall reproductive performance observed in farms in terms of both success rate (recalving rate) and reproduction delays (calving interval). This model has the purpose to be integrated in herd simulation models to usefully test the impact of management strategies on herd reproductive performance, and thus on calving patterns and culling rates.

A computational model of prefrontal control in free recall: strategic memory use in the California Verbal Learning Task.

PubMed

Becker, Suzanna; Lim, Jean

2003-08-15

Several decades of research into the function of the frontal lobes in brain-damaged patients, and more recently in intact individuals using function brain imaging, has delineated the complex executive functions of the frontal cortex. And yet, the mechanisms by which the brain achieves these functions remain poorly understood. Here, we present a computational model of the role of the prefrontal cortex (PFC) in controlled memory use that may help to shed light on the mechanisms underlying one aspect of frontal control: the development and deployment of recall strategies. The model accounts for interactions between the PFC and medial temporal lobe in strategic memory use. The PFC self-organizes its own mnemonic codes using internally derived performance measures. These mnemonic codes serve as retrieval cues by biasing retrieval in the medial temporal lobe memory system. We present data from three simulation experiments that demonstrate strategic encoding and retrieval in the free recall of categorized lists of words. Experiment 1 compares the performance of the model with two control networks to evaluate the contribution of various components of the model. Experiment 2 compares the performance of normal and frontally lesioned models to data from several studies using frontally intact and frontally lesioned individuals, as well as normal, healthy individuals under conditions of divided attention. Experiment 3 compares the model's performance on the recall of blocked and unblocked categorized lists of words to data from Stuss et al. (1994) for individuals with control and frontal lobe lesions. Overall, our model captures a number of aspects of human performance on free recall tasks: an increase in total words recalled and in semantic clustering scores across trials, superiority on blocked lists of related items compared to unblocked lists of related items, and similar patterns of performance across trials in the normal and frontally lesioned models, with poorer overall performance of the lesioned models on all measures. The model also has a number of shortcomings, in light of which we suggest extensions to the model that would enable more sophisticated forms of strategic control.

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model

PubMed Central

Guan, Li; Hao, Bibo; Cheng, Qijin; Yip, Paul SF

2015-01-01

Background Traditional offline assessment of suicide probability is time consuming and difficult in convincing at-risk individuals to participate. Identifying individuals with high suicide probability through online social media has an advantage in its efficiency and potential to reach out to hidden individuals, yet little research has been focused on this specific field. Objective The objective of this study was to apply two classification models, Simple Logistic Regression (SLR) and Random Forest (RF), to examine the feasibility and effectiveness of identifying high suicide possibility microblog users in China through profile and linguistic features extracted from Internet-based data. Methods There were nine hundred and nine Chinese microblog users that completed an Internet survey, and those scoring one SD above the mean of the total Suicide Probability Scale (SPS) score, as well as one SD above the mean in each of the four subscale scores in the participant sample were labeled as high-risk individuals, respectively. Profile and linguistic features were fed into two machine learning algorithms (SLR and RF) to train the model that aims to identify high-risk individuals in general suicide probability and in its four dimensions. Models were trained and then tested by 5-fold cross validation; in which both training set and test set were generated under the stratified random sampling rule from the whole sample. There were three classic performance metrics (Precision, Recall, F1 measure) and a specifically defined metric “Screening Efficiency” that were adopted to evaluate model effectiveness. Results Classification performance was generally matched between SLR and RF. Given the best performance of the classification models, we were able to retrieve over 70% of the labeled high-risk individuals in overall suicide probability as well as in the four dimensions. Screening Efficiency of most models varied from 1/4 to 1/2. Precision of the models was generally below 30%. Conclusions Individuals in China with high suicide probability are recognizable by profile and text-based information from microblogs. Although there is still much space to improve the performance of classification models in the future, this study may shed light on preliminary screening of risky individuals via machine learning algorithms, which can work side-by-side with expert scrutiny to increase efficiency in large-scale-surveillance of suicide probability from online social media. PMID:26543921

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model.

PubMed

Guan, Li; Hao, Bibo; Cheng, Qijin; Yip, Paul Sf; Zhu, Tingshao

2015-01-01

Traditional offline assessment of suicide probability is time consuming and difficult in convincing at-risk individuals to participate. Identifying individuals with high suicide probability through online social media has an advantage in its efficiency and potential to reach out to hidden individuals, yet little research has been focused on this specific field. The objective of this study was to apply two classification models, Simple Logistic Regression (SLR) and Random Forest (RF), to examine the feasibility and effectiveness of identifying high suicide possibility microblog users in China through profile and linguistic features extracted from Internet-based data. There were nine hundred and nine Chinese microblog users that completed an Internet survey, and those scoring one SD above the mean of the total Suicide Probability Scale (SPS) score, as well as one SD above the mean in each of the four subscale scores in the participant sample were labeled as high-risk individuals, respectively. Profile and linguistic features were fed into two machine learning algorithms (SLR and RF) to train the model that aims to identify high-risk individuals in general suicide probability and in its four dimensions. Models were trained and then tested by 5-fold cross validation; in which both training set and test set were generated under the stratified random sampling rule from the whole sample. There were three classic performance metrics (Precision, Recall, F1 measure) and a specifically defined metric "Screening Efficiency" that were adopted to evaluate model effectiveness. Classification performance was generally matched between SLR and RF. Given the best performance of the classification models, we were able to retrieve over 70% of the labeled high-risk individuals in overall suicide probability as well as in the four dimensions. Screening Efficiency of most models varied from 1/4 to 1/2. Precision of the models was generally below 30%. Individuals in China with high suicide probability are recognizable by profile and text-based information from microblogs. Although there is still much space to improve the performance of classification models in the future, this study may shed light on preliminary screening of risky individuals via machine learning algorithms, which can work side-by-side with expert scrutiny to increase efficiency in large-scale-surveillance of suicide probability from online social media.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

A side-by-side comparison of CPV module and system performance

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

Muller, Matthew; Marion, Bill; Kurtz, Sarah

A side-by-side comparison is made between concentrator photovoltaic module and system direct current aperture efficiency data with a focus on quantifying system performance losses. The individual losses measured/calculated, when combined, are in good agreement with the total loss seen between the module and the system. Results indicate that for the given test period, the largest individual loss of 3.7% relative is due to the baseline performance difference between the individual module and the average for the 200 modules in the system. A basic empirical model is derived based on module spectral performance data and the tabulated losses between the modulemore » and the system. The model predicts instantaneous system direct current aperture efficiency with a root mean square error of 2.3% relative.« less

Visual Predictive Check in Models with Time-Varying Input Function.

PubMed

Largajolli, Anna; Bertoldo, Alessandra; Campioni, Marco; Cobelli, Claudio

2015-11-01

The nonlinear mixed effects models are commonly used modeling techniques in the pharmaceutical research as they enable the characterization of the individual profiles together with the population to which the individuals belong. To ensure a correct use of them is fundamental to provide powerful diagnostic tools that are able to evaluate the predictive performance of the models. The visual predictive check (VPC) is a commonly used tool that helps the user to check by visual inspection if the model is able to reproduce the variability and the main trend of the observed data. However, the simulation from the model is not always trivial, for example, when using models with time-varying input function (IF). In this class of models, there is a potential mismatch between each set of simulated parameters and the associated individual IF which can cause an incorrect profile simulation. We introduce a refinement of the VPC by taking in consideration a correlation term (the Mahalanobis or normalized Euclidean distance) that helps the association of the correct IF with the individual set of simulated parameters. We investigate and compare its performance with the standard VPC in models of the glucose and insulin system applied on real and simulated data and in a simulated pharmacokinetic/pharmacodynamic (PK/PD) example. The newly proposed VPC performance appears to be better with respect to the standard VPC especially for the models with big variability in the IF where the probability of simulating incorrect profiles is higher.

Teams as innovative systems: multilevel motivational antecedents of innovation in R&D teams.

PubMed

Chen, Gilad; Farh, Jiing-Lih; Campbell-Bush, Elizabeth M; Wu, Zhiming; Wu, Xin

2013-11-01

Integrating theories of proactive motivation, team innovation climate, and motivation in teams, we developed and tested a multilevel model of motivators of innovative performance in teams. Analyses of multisource data from 428 members of 95 research and development (R&D) teams across 33 Chinese firms indicated that team-level support for innovation climate captured motivational mechanisms that mediated between transformational leadership and team innovative performance, whereas members' motivational states (role-breadth self-efficacy and intrinsic motivation) mediated between proactive personality and individual innovative performance. Furthermore, individual motivational states and team support for innovation climate uniquely promoted individual innovative performance, and, in turn, individual innovative performance linked team support for innovation climate to team innovative performance. (c) 2013 APA, all rights reserved.

Updating the Behavior Engineering Model.

ERIC Educational Resources Information Center

Chevalier, Roger

2003-01-01

Considers Thomas Gilbert's Behavior Engineering Model as a tool for systematically identifying barriers to individual and organizational performance. Includes a detailed case study and a performance aid that incorporates gap analysis, cause analysis, and force field analysis to update the original model. (Author/LRW)

Investigating systematic individual differences in sleep-deprived performance on a high-fidelity flight simulator.

PubMed

Van Dongen, Hans P A; Caldwell, John A; Caldwell, J Lynn

2006-05-01

Laboratory research has revealed considerable systematic variability in the degree to which individuals' alertness and performance are affected by sleep deprivation. However, little is known about whether or not different populations exhibit similar levels of individual variability. In the present study, we examined individual variability in performance impairment due to sleep loss in a highly select population of militaryjet pilots. Ten active-duty F-117 pilots were deprived of sleep for 38 h and studied repeatedly in a high-fidelity flight simulator. Data were analyzed with a mixed-model ANOVA to quantify individual variability. Statistically significant, systematic individual differences in the effects of sleep deprivation were observed, even when baseline differences were accounted for. The findings suggest that highly select populations may exhibit individual differences in vulnerability to performance impairment from sleep loss just as the general population does. Thus, the scientific and operational communities' reliance on group data as opposed to individual data may entail substantial misestimation of the impact of job-related stressors on safety and performance.

How social information can improve estimation accuracy in human groups.

PubMed

Jayles, Bertrand; Kim, Hye-Rin; Escobedo, Ramón; Cezera, Stéphane; Blanchet, Adrien; Kameda, Tatsuya; Sire, Clément; Theraulaz, Guy

2017-11-21

In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the questions of how individuals use social information and how it affects their decisions. We report experiments performed in France and Japan, in which subjects could update their estimates after having received information from other subjects. We measure and model the impact of this social information at individual and collective scales. We observe and justify that, when individuals have little prior knowledge about a quantity, the distribution of the logarithm of their estimates is close to a Cauchy distribution. We find that social influence helps the group improve its properly defined collective accuracy. We quantify the improvement of the group estimation when additional controlled and reliable information is provided, unbeknownst to the subjects. We show that subjects' sensitivity to social influence permits us to define five robust behavioral traits and increases with the difference between personal and group estimates. We then use our data to build and calibrate a model of collective estimation to analyze the impact on the group performance of the quantity and quality of information received by individuals. The model quantitatively reproduces the distributions of estimates and the improvement of collective performance and accuracy observed in our experiments. Finally, our model predicts that providing a moderate amount of incorrect information to individuals can counterbalance the human cognitive bias to systematically underestimate quantities and thereby improve collective performance. Copyright © 2017 the Author(s). Published by PNAS.

How social information can improve estimation accuracy in human groups

PubMed Central

Jayles, Bertrand; Kim, Hye-rin; Cezera, Stéphane; Blanchet, Adrien; Kameda, Tatsuya; Sire, Clément; Theraulaz, Guy

2017-01-01

In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the questions of how individuals use social information and how it affects their decisions. We report experiments performed in France and Japan, in which subjects could update their estimates after having received information from other subjects. We measure and model the impact of this social information at individual and collective scales. We observe and justify that, when individuals have little prior knowledge about a quantity, the distribution of the logarithm of their estimates is close to a Cauchy distribution. We find that social influence helps the group improve its properly defined collective accuracy. We quantify the improvement of the group estimation when additional controlled and reliable information is provided, unbeknownst to the subjects. We show that subjects’ sensitivity to social influence permits us to define five robust behavioral traits and increases with the difference between personal and group estimates. We then use our data to build and calibrate a model of collective estimation to analyze the impact on the group performance of the quantity and quality of information received by individuals. The model quantitatively reproduces the distributions of estimates and the improvement of collective performance and accuracy observed in our experiments. Finally, our model predicts that providing a moderate amount of incorrect information to individuals can counterbalance the human cognitive bias to systematically underestimate quantities and thereby improve collective performance. PMID:29118142

Modeling the Direct and Indirect Determinants of Different Types of Individual Job Performance

DTIC Science & Technology

2008-06-01

cognitions , and self-regulation). A different model was found to describe the process depending on whether the performance dimension was an element of...performing the behaviors they indicated they intended to perform, and assembled a battery of existing instruments to measure cognitive ability, personality...model came from the task performance dimension. For this dimension, knowledge, skill, cognitive choice aspects of motivation, and self-regulation

Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model

PubMed Central

Johnson, Robin R.; Popovic, Djordje P.; Olmstead, Richard E.; Stikic, Maja; Levendowski, Daniel J.; Berka, Chris

2011-01-01

A great deal of research over the last century has focused on drowsiness/alertness detection, as fatigue-related physical and cognitive impairments pose a serious risk to public health and safety. Available drowsiness/alertness detection solutions are unsatisfactory for a number of reasons: 1) lack of generalizability, 2) failure to address individual variability in generalized models, and/or 3) they lack a portable, un-tethered application. The current study aimed to address these issues, and determine if an individualized electroencephalography (EEG) based algorithm could be defined to track performance decrements associated with sleep loss, as this is the first step in developing a field deployable drowsiness/alertness detection system. The results indicated that an EEG-based algorithm, individualized using a series of brief "identification" tasks, was able to effectively track performance decrements associated with sleep deprivation. Future development will address the need for the algorithm to predict performance decrements due to sleep loss, and provide field applicability. PMID:21419826

Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model.

PubMed

Johnson, Robin R; Popovic, Djordje P; Olmstead, Richard E; Stikic, Maja; Levendowski, Daniel J; Berka, Chris

2011-05-01

A great deal of research over the last century has focused on drowsiness/alertness detection, as fatigue-related physical and cognitive impairments pose a serious risk to public health and safety. Available drowsiness/alertness detection solutions are unsatisfactory for a number of reasons: (1) lack of generalizability, (2) failure to address individual variability in generalized models, and/or (3) lack of a portable, un-tethered application. The current study aimed to address these issues, and determine if an individualized electroencephalography (EEG) based algorithm could be defined to track performance decrements associated with sleep loss, as this is the first step in developing a field deployable drowsiness/alertness detection system. The results indicated that an EEG-based algorithm, individualized using a series of brief "identification" tasks, was able to effectively track performance decrements associated with sleep deprivation. Future development will address the need for the algorithm to predict performance decrements due to sleep loss, and provide field applicability. Copyright © 2011 Elsevier B.V. All rights reserved.

CFD-RANS prediction of individual exposure from continuous release of hazardous airborne materials in complex urban environments

NASA Astrophysics Data System (ADS)

Efthimiou, G. C.; Andronopoulos, S.; Bartzis, J. G.; Berbekar, E.; Harms, F.; Leitl, B.

2017-02-01

One of the key issues of recent research on the dispersion inside complex urban environments is the ability to predict individual exposure (maximum dosages) of an airborne material which is released continuously from a point source. The present work addresses the question whether the computational fluid dynamics (CFD)-Reynolds-averaged Navier-Stokes (RANS) methodology can be used to predict individual exposure for various exposure times. This is feasible by providing the two RANS concentration moments (mean and variance) and a turbulent time scale to a deterministic model. The whole effort is focused on the prediction of individual exposure inside a complex real urban area. The capabilities of the proposed methodology are validated against wind-tunnel data (CUTE experiment). The present simulations were performed 'blindly', i.e. the modeller had limited information for the inlet boundary conditions and the results were kept unknown until the end of the COST Action ES1006. Thus, a high uncertainty of the results was expected. The general performance of the methodology due to this 'blind' strategy is good. The validation metrics fulfil the acceptance criteria. The effect of the grid and the turbulence model on the model performance is examined.

The contribution of attentional lapses to individual differences in visual working memory capacity.

PubMed

Adam, Kirsten C S; Mance, Irida; Fukuda, Keisuke; Vogel, Edward K

2015-08-01

Attentional control and working memory capacity are important cognitive abilities that substantially vary between individuals. Although much is known about how attentional control and working memory capacity relate to each other and to constructs like fluid intelligence, little is known about how trial-by-trial fluctuations in attentional engagement impact trial-by-trial working memory performance. Here, we employ a novel whole-report memory task that allowed us to distinguish between varying levels of attentional engagement in humans performing a working memory task. By characterizing low-performance trials, we can distinguish between models in which working memory performance failures are caused by either (1) complete lapses of attention or (2) variations in attentional control. We found that performance failures increase with set-size and strongly predict working memory capacity. Performance variability was best modeled by an attentional control model of attention, not a lapse model. We examined neural signatures of performance failures by measuring EEG activity while participants performed the whole-report task. The number of items correctly recalled in the memory task was predicted by frontal theta power, with decreased frontal theta power associated with poor performance on the task. In addition, we found that poor performance was not explained by failures of sensory encoding; the P1/N1 response and ocular artifact rates were equivalent for high- and low-performance trials. In all, we propose that attentional lapses alone cannot explain individual differences in working memory performance. Instead, we find that graded fluctuations in attentional control better explain the trial-by-trial differences in working memory that we observe.

The Contribution of Attentional Lapses to Individual Differences in Visual Working Memory Capacity

PubMed Central

Adam, Kirsten C. S.; Mance, Irida; Fukuda, Keisuke; Vogel, Edward K.

2015-01-01

Attentional control and working memory capacity are important cognitive abilities that substantially vary between individuals. Although much is known about how attentional control and working memory capacity relate to each other and to constructs like fluid intelligence, little is known about how trial-by-trial fluctuations in attentional engagement impact trial-by-trial working memory performance. Here, we employ a novel whole-report memory task that allowed us to distinguish between varying levels of attentional engagement in humans performing a working memory task. By characterizing low-performance trials, we can distinguish between models in which working memory performance failures are caused by either (1) complete lapses of attention or (2) variations in attentional control. We found that performance failures increase with set-size and strongly predict working memory capacity. Performance variability was best modeled by an attentional control model of attention, not a lapse model. We examined neural signatures of performance failures by measuring EEG activity while participants performed the whole-report task. The number of items correctly recalled in the memory task was predicted by frontal theta power, with decreased frontal theta power associated with poor performance on the task. In addition, we found that poor performance was not explained by failures of sensory encoding; the P1/N1 response and ocular artifact rates were equivalent for high- and low-performance trials. In all, we propose that attentional lapses alone cannot explain individual differences in working memory performance. Instead, we find that graded fluctuations in attentional control better explain the trial-by-trial differences in working memory that we observe. PMID:25811710

The New York Head-A precise standardized volume conductor model for EEG source localization and tES targeting.

PubMed

Huang, Yu; Parra, Lucas C; Haufe, Stefan

2016-10-15

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semi-automated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI), and thus head modeling is often based on the anatomy of an 'arbitrary' individual (e.g. Colin27). Additionally, existing reference models for the human head often do not include the cerebro-spinal fluid (CSF), and their field of view excludes portions of the head and neck-two factors that demonstrably affect current-flow patterns. Here we present a highly detailed FEM, which we call ICBM-NY, or "New York Head". It is based on the ICBM152 anatomical template (a non-linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5mm(3) resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the 'ground truth') is compared to its BEM counterpart, the ICBM-NY, a BEM of the ICBM anatomy, an 'individualized' BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM-NY outperforms FEMs of mismatched individual anatomies as well as the BEM of the ICBM anatomy according to both criteria. We therefore propose the New York Head as a new standard head model to be used in future EEG and tES studies whenever an individual MRI is not available. We release all model data online at neuralengr.com/nyhead/ to facilitate broad adoption. Published by Elsevier Inc.

Visual Cortical Entrainment to Motion and Categorical Speech Features during Silent Lipreading

PubMed Central

O’Sullivan, Aisling E.; Crosse, Michael J.; Di Liberto, Giovanni M.; Lalor, Edmund C.

2017-01-01

Speech is a multisensory percept, comprising an auditory and visual component. While the content and processing pathways of audio speech have been well characterized, the visual component is less well understood. In this work, we expand current methodologies using system identification to introduce a framework that facilitates the study of visual speech in its natural, continuous form. Specifically, we use models based on the unheard acoustic envelope (E), the motion signal (M) and categorical visual speech features (V) to predict EEG activity during silent lipreading. Our results show that each of these models performs similarly at predicting EEG in visual regions and that respective combinations of the individual models (EV, MV, EM and EMV) provide an improved prediction of the neural activity over their constituent models. In comparing these different combinations, we find that the model incorporating all three types of features (EMV) outperforms the individual models, as well as both the EV and MV models, while it performs similarly to the EM model. Importantly, EM does not outperform EV and MV, which, considering the higher dimensionality of the V model, suggests that more data is needed to clarify this finding. Nevertheless, the performance of EMV, and comparisons of the subject performances for the three individual models, provides further evidence to suggest that visual regions are involved in both low-level processing of stimulus dynamics and categorical speech perception. This framework may prove useful for investigating modality-specific processing of visual speech under naturalistic conditions. PMID:28123363

Brain regions engaged by part- and whole-task performance in a video game: a model-based test of the decomposition hypothesis.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Anderson, Abraham R; Poole, Ben; Qin, Yulin

2011-12-01

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model's predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits.

Human performance across decision making, selective attention, and working memory tasks: Experimental data and computer simulations.

PubMed

Stocco, Andrea; Yamasaki, Brianna L; Prat, Chantel S

2018-04-01

This article describes the data analyzed in the paper "Individual differences in the Simon effect are underpinned by differences in the competitive dynamics in the basal ganglia: An experimental verification and a computational model" (Stocco et al., 2017) [1]. The data includes behavioral results from participants performing three cognitive tasks (Probabilistic Stimulus Selection (Frank et al., 2004) [2], Simon task (Craft and Simon, 1970) [3], and Automated Operation Span (Unsworth et al., 2005) [4]), as well as simulationed traces generated by a computational neurocognitive model that accounts for individual variations in human performance across the tasks. The experimental data encompasses individual data files (in both preprocessed and native output format) as well as group-level summary files. The simulation data includes the entire model code, the results of a full-grid search of the model's parameter space, and the code used to partition the model space and parallelize the simulations. Finally, the repository includes the R scripts used to carry out the statistical analyses reported in the original paper.

Exploring Pre-Service Training and School Counselor Interns Use of the ASCA Model Tasks

ERIC Educational Resources Information Center

Oberman, Aaron; Studer, Jeannine

2016-01-01

Activities performed by school counselor interns perform that are related to the American School Counselor Association (ASCA) National Model and Performance Standards were explored in this study. Interns were more likely to perform tasks that included individual and small group counseling, monitoring student progress, and conducting individual…

Mediators, Moderators, and Tests for Mediation.

DTIC Science & Technology

1983-12-09

relation between level of poor performance and degree of intended persistence for high self - esteem individuals, and (b) ability attributions mediate...the relation between level of poor performance and degree of intended persistence for low self - esteem individuals. The proposed causal models are shown...in Figure Ia. Individuals are first given a self - esteem ouestionnaire and then blocked (subgrouped) into high self - esteems or lcw self - esteems , the

Are Some Negotiators Better Than Others? Individual Differences in Bargaining Outcomes

PubMed Central

Elfenbein, Hillary Anger; Curhan, Jared R.; Eisenkraft, Noah; Shirako, Aiwa; Baccaro, Lucio

2008-01-01

The authors address the long-standing mystery of stable individual differences in negotiation performance, on which intuition and conventional wisdom have clashed with inconsistent empirical findings. The present study used the Social Relations Model to examine individual differences directly via consistency in performance across multiple negotiations and to disentangle the roles of both parties within these inherently dyadic interactions. Individual differences explained a substantial 46% of objective performance and 19% of subjective performance in a mixed-motive bargaining exercise. Previous work may have understated the influence of individual differences because conventional research designs require specific traits to be identified and measured. Exploratory analyses of a battery of traits revealed few reliable associations with consistent individual differences in objective performance—except for positive beliefs about negotiation, positive affect, and concern for one's outcome, each of which predicted better performance. Findings suggest that the field has large untapped potential to explain substantial individual differences. Limitations, areas for future research, and practical implications are discussed. PMID:21720453

Study on individual stochastic model of GNSS observations for precise kinematic applications

NASA Astrophysics Data System (ADS)

Próchniewicz, Dominik; Szpunar, Ryszard

2015-04-01

The proper definition of mathematical positioning model, which is defined by functional and stochastic models, is a prerequisite to obtain the optimal estimation of unknown parameters. Especially important in this definition is realistic modelling of stochastic properties of observations, which are more receiver-dependent and time-varying than deterministic relationships. This is particularly true with respect to precise kinematic applications which are characterized by weakening model strength. In this case, incorrect or simplified definition of stochastic model causes that the performance of ambiguity resolution and accuracy of position estimation can be limited. In this study we investigate the methods of describing the measurement noise of GNSS observations and its impact to derive precise kinematic positioning model. In particular stochastic modelling of individual components of the variance-covariance matrix of observation noise performed using observations from a very short baseline and laboratory GNSS signal generator, is analyzed. Experimental test results indicate that the utilizing the individual stochastic model of observations including elevation dependency and cross-correlation instead of assumption that raw measurements are independent with the same variance improves the performance of ambiguity resolution as well as rover positioning accuracy. This shows that the proposed stochastic assessment method could be a important part in complex calibration procedure of GNSS equipment.

High variability of individual longitudinal motor performance over five years in very preterm infants.

PubMed

Janssen, Anjo J W M; Oostendorp, Rob A B; Akkermans, Reinier P; Steiner, Katerina; Kollée, Louis A A; Nijhuis-van der Sanden, Maria W G

2016-12-01

To determine longitudinal motor performance in very preterm (VPT) infants from 6 months to 5 years of age for the entire cohort of infants, according to gender and gestational age and at the individual level. Single-center, prospective longitudinal study of 201 VPT infants (106 boys) without severe impairments. Motor performance was assessed with the Bayley Scales of Infant Development (BSID-II-MS: 6, 12, 24 months) and the Movement Assessment Battery for Children (MABC-2-NL: 5 years). At 6, 12, and 24 months and then at 5 years, 77%, 80%, 48%, and 22% of the infants, respectively, showed delayed motor performance (<-1SD). At 5 years, girls performed significantly better than boys in manual dexterity and balance. MIXED MODEL ANALYSES: that examined interactions between time and gender and time and gestational age, revealed no significant interactions. The variance at child level was 29%. Linear mixed model analysis revealed that mean z-scores of -1.46 at 6 months of age declined significantly to -0.52 at 5 years. Individual longitudinal motor performance showed high variability. Longitudinal motor performance improved almost 1 SD over five years. However, the variability of individual longitudinal motor performance hampers evaluation in clinical care and research. Copyright © 2016 Elsevier Ltd. All rights reserved.

Of Uberfleas and Krakens: Detecting Trade-offs Using Mixed Models.

PubMed

Careau, Vincent; Wilson, Robbie S

2017-08-01

All animals experience performance trade-offs as they complete tasks such as capturing prey, defending territories, acquiring mates, and escaping predators. Why then, is it so hard to detect performance trade-offs at the whole-organismal level? Why do we sometimes even obtain positive correlations between two performance traits that are predicted to be negatively associated? Here we explore two plausible explanations. First, most analyses are based on individual maximal values (i.e., personal best), which could introduce a bias in the correlation estimates. Second, phenotypic correlations alone may be poor indicators of a trade-off when contrasting processes occur at the among- versus within-individual levels. One such scenario is the "big houses big cars" model developed in life-history theory to explain the existence of "uberfleas" that are superior in all regards (because they acquire more resources than others). We highlight that the exact opposite scenario might occur for performance trade-offs, where among-individual trade-offs may be masked by within-individual changes in physical condition. One of the best ways to test among these alternative scenarios is to collect repeated pairs of performance traits and analyze them using multivariate mixed models (MMMs). MMMs allow straightforward and simultaneous examination of trait correlations at the among- and within-individual levels. We use a simple simulation tool (SQuID package in R) to create a population of Krakens, a mythical giant squid-like sea creature whose morphology generates a performance trade-off between swimming speed and strength or ability to sink ships. The simulations showed that using individual maximum values introduces a bias that is particularly severe when individuals differ in the number of repeated samples (ntrial). Finally, we show how MMMs can help detect performance (or any other type of) trade-offs and offer additional insights (e.g., help detect plasticity integration). We hope researchers will adopt MMMs when exploring trade-offs in whole-animal performances. © The Author 2017. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

Investigating Individual Differences in Toddler Search with Mixture Models

ERIC Educational Resources Information Center

Berthier, Neil E.; Boucher, Kelsea; Weisner, Nina

2015-01-01

Children's performance on cognitive tasks is often described in categorical terms in that a child is described as either passing or failing a test, or knowing or not knowing some concept. We used binomial mixture models to determine whether individual children could be classified as passing or failing two search tasks, the DeLoache model room…

Effects of video modeling on treatment integrity of behavioral interventions.

PubMed

Digennaro-Reed, Florence D; Codding, Robin; Catania, Cynthia N; Maguire, Helena

2010-01-01

We examined the effects of individualized video modeling on the accurate implementation of behavioral interventions using a multiple baseline design across 3 teachers. During video modeling, treatment integrity improved above baseline levels; however, teacher performance remained variable. The addition of verbal performance feedback increased treatment integrity to 100% for all participants, and performance was maintained 1 week later. Teachers found video modeling to be more socially acceptable with performance feedback than alone, but rated both positively.

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability.

PubMed

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-01

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsets driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models. Copyright © 2015 Elsevier Inc. All rights reserved.

Two ways related to performance in elite sport: the path of self-confidence and competitive anxiety and the path of group cohesion and group goal-clarity.

PubMed

Kjørmo, Odd; Halvari, Hallgeir

2002-06-01

A model tested among 136 Norwegian Olympic-level athletes yielded two paths related to performance. The first path indicated that self-confidence, modeled as an antecedent of competitive anxiety, is negatively correlated with anxiety. Competitive anxiety in turn is negatively correlated with performance. The second path indicated that group cohesion is positively correlated with group goal-clarity, which in turn is positively correlated with performance. Competitive anxiety mediates the relation between self-confidence and performance, whereas group goal-clarity mediates the relation between group cohesion and performance. Results from multiple regression analyses supported the model in the total sample and among individual sport athletes organized in training groups (n = 100). Among team sport athletes (n = 36), personality and group measures are more strongly intercorrelated than among individual sport athletes, and the relation with performance is more complex for the former group. The interaction of self-confidence and competitive anxiety is related to performance among team sport athletes.

Effects of Video Modeling on Treatment Integrity of Behavioral Interventions

ERIC Educational Resources Information Center

DiGennaro-Reed, Florence D.; Codding, Robin; Catania, Cynthia N.; Maguire, Helena

2010-01-01

We examined the effects of individualized video modeling on the accurate implementation of behavioral interventions using a multiple baseline design across 3 teachers. During video modeling, treatment integrity improved above baseline levels; however, teacher performance remained variable. The addition of verbal performance feedback increased…

The Impact of Individual Differences, Types of Model and Social Settings on Block Building Performance among Chinese Preschoolers.

PubMed

Tian, Mi; Deng, Zhu; Meng, Zhaokun; Li, Rui; Zhang, Zhiyi; Qi, Wenhui; Wang, Rui; Yin, Tingting; Ji, Menghui

2018-01-01

Children's block building performances are used as indicators of other abilities in multiple domains. In the current study, we examined individual differences, types of model and social settings as influences on children's block building performance. Chinese preschoolers ( N = 180) participated in a block building activity in a natural setting, and performance was assessed with multiple measures in order to identify a range of specific skills. Using scores generated across these measures, three dependent variables were analyzed: block building skills, structural balance and structural features. An overall MANOVA showed that there were significant main effects of gender and grade level across most measures. Types of model showed no significant effect in children's block building. There was a significant main effect of social settings on structural features, with the best performance in the 5-member group, followed by individual and then the 10-member block building. These findings suggest that boys performed better than girls in block building activity. Block building performance increased significantly from 1st to 2nd year of preschool, but not from second to third. The preschoolers created more representational constructions when presented with a model made of wooden rather than with a picture. There was partial evidence that children performed better when working with peers in a small group than when working alone or working in a large group. It is suggested that future study should examine other modalities rather than the visual one, diversify the samples and adopt a longitudinal investigation.

Development and Enhancement of a Model of Performance and Decision Making Under Stress in a Real Life Setting

DTIC Science & Technology

1993-02-12

admission tnat occurred during airway manipulation, distracted the attending anesthesiologist managing patient one from detecting why the mechanical... articular interest in settings where skilled individuals function as a tear are the relationships between team performance and individual decision-making

An ICAI architecture for troubleshooting in complex, dynamic systems

NASA Technical Reports Server (NTRS)

Fath, Janet L.; Mitchell, Christine M.; Govindaraj, T.

1990-01-01

Ahab, an intelligent computer-aided instruction (ICAI) program, illustrates an architecture for simulator-based ICAI programs to teach troubleshooting in complex, dynamic environments. The architecture posits three elements of a computerized instructor: the task model, the student model, and the instructional module. The task model is a prescriptive model of expert performance that uses symptomatic and topographic search strategies to provide students with directed problem-solving aids. The student model is a descriptive model of student performance in the context of the task model. This student model compares the student and task models, critiques student performance, and provides interactive performance feedback. The instructional module coordinates information presented by the instructional media, the task model, and the student model so that each student receives individualized instruction. Concept and metaconcept knowledge that supports these elements is contained in frames and production rules, respectively. The results of an experimental evaluation are discussed. They support the hypothesis that training with an adaptive online system built using the Ahab architecture produces better performance than training using simulator practice alone, at least with unfamiliar problems. It is not sufficient to develop an expert strategy and present it to students using offline materials. The training is most effective if it adapts to individual student needs.

Autism-specific covariation in perceptual performances: "g" or "p" factor?

PubMed

Meilleur, Andrée-Anne S; Berthiaume, Claude; Bertone, Armando; Mottron, Laurent

2014-01-01

Autistic perception is characterized by atypical and sometimes exceptional performance in several low- (e.g., discrimination) and mid-level (e.g., pattern matching) tasks in both visual and auditory domains. A factor that specifically affects perceptive abilities in autistic individuals should manifest as an autism-specific association between perceptual tasks. The first purpose of this study was to explore how perceptual performances are associated within or across processing levels and/or modalities. The second purpose was to determine if general intelligence, the major factor that accounts for covariation in task performances in non-autistic individuals, equally controls perceptual abilities in autistic individuals. We asked 46 autistic individuals and 46 typically developing controls to perform four tasks measuring low- or mid-level visual or auditory processing. Intelligence was measured with the Wechsler's Intelligence Scale (FSIQ) and Raven Progressive Matrices (RPM). We conducted linear regression models to compare task performances between groups and patterns of covariation between tasks. The addition of either Wechsler's FSIQ or RPM in the regression models controlled for the effects of intelligence. In typically developing individuals, most perceptual tasks were associated with intelligence measured either by RPM or Wechsler FSIQ. The residual covariation between unimodal tasks, i.e. covariation not explained by intelligence, could be explained by a modality-specific factor. In the autistic group, residual covariation revealed the presence of a plurimodal factor specific to autism. Autistic individuals show exceptional performance in some perceptual tasks. Here, we demonstrate the existence of specific, plurimodal covariation that does not dependent on general intelligence (or "g" factor). Instead, this residual covariation is accounted for by a common perceptual process (or "p" factor), which may drive perceptual abilities differently in autistic and non-autistic individuals.

Autism-Specific Covariation in Perceptual Performances: “g” or “p” Factor?

PubMed Central

Meilleur, Andrée-Anne S.; Berthiaume, Claude; Bertone, Armando; Mottron, Laurent

2014-01-01

Background Autistic perception is characterized by atypical and sometimes exceptional performance in several low- (e.g., discrimination) and mid-level (e.g., pattern matching) tasks in both visual and auditory domains. A factor that specifically affects perceptive abilities in autistic individuals should manifest as an autism-specific association between perceptual tasks. The first purpose of this study was to explore how perceptual performances are associated within or across processing levels and/or modalities. The second purpose was to determine if general intelligence, the major factor that accounts for covariation in task performances in non-autistic individuals, equally controls perceptual abilities in autistic individuals. Methods We asked 46 autistic individuals and 46 typically developing controls to perform four tasks measuring low- or mid-level visual or auditory processing. Intelligence was measured with the Wechsler's Intelligence Scale (FSIQ) and Raven Progressive Matrices (RPM). We conducted linear regression models to compare task performances between groups and patterns of covariation between tasks. The addition of either Wechsler's FSIQ or RPM in the regression models controlled for the effects of intelligence. Results In typically developing individuals, most perceptual tasks were associated with intelligence measured either by RPM or Wechsler FSIQ. The residual covariation between unimodal tasks, i.e. covariation not explained by intelligence, could be explained by a modality-specific factor. In the autistic group, residual covariation revealed the presence of a plurimodal factor specific to autism. Conclusions Autistic individuals show exceptional performance in some perceptual tasks. Here, we demonstrate the existence of specific, plurimodal covariation that does not dependent on general intelligence (or “g” factor). Instead, this residual covariation is accounted for by a common perceptual process (or “p” factor), which may drive perceptual abilities differently in autistic and non-autistic individuals. PMID:25117450

The Motivational Determinants of Task Performance in a Non-Industrial Milieu: A Modification and Extension of Vroom's Model.

ERIC Educational Resources Information Center

Mendel, Raymond M.; Dickinson, Terry L.

Vroom's cognitive model, which proposes to both explain and predict an individual's level of work productivity by drawing on the construct motivation, is discussed and three hypotheses generated: (1) that Vroom's model does predict performance in a non-industrial setting; (2) that it predicts self-perceived performance better than measures…

Individualized estimation of human core body temperature using noninvasive measurements.

PubMed

Laxminarayan, Srinivas; Rakesh, Vineet; Oyama, Tatsuya; Kazman, Josh B; Yanovich, Ran; Ketko, Itay; Epstein, Yoram; Morrison, Shawnda; Reifman, Jaques

2018-06-01

A rising core body temperature (T c ) during strenuous physical activity is a leading indicator of heat-injury risk. Hence, a system that can estimate T c in real time and provide early warning of an impending temperature rise may enable proactive interventions to reduce the risk of heat injuries. However, real-time field assessment of T c requires impractical invasive technologies. To address this problem, we developed a mathematical model that describes the relationships between T c and noninvasive measurements of an individual's physical activity, heart rate, and skin temperature, and two environmental variables (ambient temperature and relative humidity). A Kalman filter adapts the model parameters to each individual and provides real-time personalized T c estimates. Using data from three distinct studies, comprising 166 subjects who performed treadmill and cycle ergometer tasks under different experimental conditions, we assessed model performance via the root mean squared error (RMSE). The individualized model yielded an overall average RMSE of 0.33 (SD = 0.18)°C, allowing us to reach the same conclusions in each study as those obtained using the T c measurements. Furthermore, for 22 unique subjects whose T c exceeded 38.5°C, a potential lower T c limit of clinical relevance, the average RMSE decreased to 0.25 (SD = 0.20)°C. Importantly, these results remained robust in the presence of simulated real-world operational conditions, yielding no more than 16% worse RMSEs when measurements were missing (40%) or laden with added noise. Hence, the individualized model provides a practical means to develop an early warning system for reducing heat-injury risk. NEW & NOTEWORTHY A model that uses an individual's noninvasive measurements and environmental variables can continually "learn" the individual's heat-stress response by automatically adapting the model parameters on the fly to provide real-time individualized core body temperature estimates. This individualized model can replace impractical invasive sensors, serving as a practical and effective surrogate for core temperature monitoring.

The New York Head—A precise standardized volume conductor model for EEG source localization and tES targeting

PubMed Central

Huang, Yu; Parra, Lucas C.; Haufe, Stefan

2018-01-01

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semiautomated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI), and thus head modeling is often based on the anatomy of an ‘arbitrary’ individual (e.g. Colin27). Additionally, existing reference models for the human head often do not include the cerebrospinal fluid (CSF), and their field of view excludes portions of the head and neck—two factors that demonstrably affect current-flow patterns. Here we present a highly detailed FEM, which we call ICBM-NY, or “New York Head”. It is based on the ICBM152 anatomical template (a non-linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5 mm 3 resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the ‘ground truth’) is compared to its BEM counterpart, the ICBM-NY, a BEM of the ICBM anatomy, an ‘individualized’ BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM-NY outperforms FEMs of mismatched individual anatomies as well as the BEM of the ICBM anatomy according to both criteria. We therefore propose the New York Head as a new standard head model to be used in future EEG and tES studies whenever an individual MRI is not available. We release all model data online at neuralengr.com/nyhead/ to facilitate broad adoption. PMID:26706450

[Investigation of team processes that enhance team performance in business organization].

PubMed

Nawata, Kengo; Yamaguchi, Hiroyuki; Hatano, Toru; Aoshima, Mika

2015-02-01

Many researchers have suggested team processes that enhance team performance. However, past team process models were based on crew team, whose all team members perform an indivisible temporary task. These models may be inapplicable business teams, whose individual members perform middle- and long-term tasks assigned to individual members. This study modified the teamwork model of Dickinson and McIntyre (1997) and aimed to demonstrate a whole team process that enhances the performance of business teams. We surveyed five companies (member N = 1,400, team N = 161) and investigated team-level-processes. Results showed that there were two sides of team processes: "communication" and "collaboration to achieve a goal." Team processes in which communication enhanced collaboration improved team performance with regard to all aspects of the quantitative objective index (e.g., current income and number of sales), supervisor rating, and self-rating measurements. On the basis of these results, we discuss the entire process by which teamwork enhances team performance in business organizations.

Network Model of Decreased Context Utilization in Autism Spectrum Disorder

ERIC Educational Resources Information Center

Beversdorf, David Q.; Narayanan, Ananth; Hillier, Ashleigh; Hughes, John D.

2007-01-01

Individuals with autism spectrum disorders (ASD) demonstrate impaired utilization of context, which allows for superior performance on the "false memory" task. We report the application of a simplified parallel distributed processing model of context utilization to the false memory task. For individuals without ASD, experiments support a model…

A holistic approach to movement education in sport and fitness: a systems based model.

PubMed

Polsgrove, Myles Jay

2012-01-01

The typical model used by movement professionals to enhance performance relies on the notion that a linear increase in load results in steady and progressive gains, whereby, the greater the effort, the greater the gains in performance. Traditional approaches to movement progression typically rely on the proper sequencing of extrinsically based activities to facilitate the individual in reaching performance objectives. However, physical rehabilitation or physical performance rarely progresses in such a linear fashion; instead they tend to evolve non-linearly and rather unpredictably. A dynamic system can be described as an entity that self-organizes into increasingly complex forms. Applying this view to the human body, practitioners could facilitate non-linear performance gains through a systems based programming approach. Utilizing a dynamic systems view, the Holistic Approach to Movement Education (HADME) is a model designed to optimize performance by accounting for non-linear and self-organizing traits associated with human movement. In this model, gains in performance occur through advancing individual perspectives and through optimizing sub-system performance. This inward shift of the focus of performance creates a sharper self-awareness and may lead to more optimal movements. Copyright © 2011 Elsevier Ltd. All rights reserved.

Individualized Educational Programming for the Mentally Retarded.

ERIC Educational Resources Information Center

Singh, Nirbhay N.; Ahrens, Michael G.

1980-01-01

The minimal components of a model which utilizes a computer for summarizing individual performance records for teaching educational skills to the mentally retarded are described. The most important components are assessment, individual and group programing, continuous data collection, and program evaluation. (Author)

Individualized Next-Generation Biomathematical Modeling of Fatigue and Performance

DTIC Science & Technology

2006-07-10

the following expression: - lo (Yo;K,?o,p,Vo,y,n0o,1,(p,F) p[Xo;O,k] p[vo;0,r] p[, lo ;0,c] / Lo (yo;K,k,p,r,7,c,,p,a). A numerical algorithm to minimize...Individualized Next-Generation Biomathematical Modeling of Fatigue and Performance Transitions Pulsar Inc. (Daniel Mollicone) Transitioned the Bayesian...forecasting framework developed as part of this grant (Specific Aim 1), so that Pulsar Inc. could initiate the development of a state/trait optimization

Dynamical networks of influence in small group discussions.

PubMed

Moussaïd, Mehdi; Noriega Campero, Alejandro; Almaatouq, Abdullah

2018-01-01

In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network-a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences.

Dynamical networks of influence in small group discussions

PubMed Central

Noriega Campero, Alejandro; Almaatouq, Abdullah

2018-01-01

In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network—a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences. PMID:29338013

Moving beyond qualitative evaluations of Bayesian models of cognition.

PubMed

Hemmer, Pernille; Tauber, Sean; Steyvers, Mark

2015-06-01

Bayesian models of cognition provide a powerful way to understand the behavior and goals of individuals from a computational point of view. Much of the focus in the Bayesian cognitive modeling approach has been on qualitative model evaluations, where predictions from the models are compared to data that is often averaged over individuals. In many cognitive tasks, however, there are pervasive individual differences. We introduce an approach to directly infer individual differences related to subjective mental representations within the framework of Bayesian models of cognition. In this approach, Bayesian data analysis methods are used to estimate cognitive parameters and motivate the inference process within a Bayesian cognitive model. We illustrate this integrative Bayesian approach on a model of memory. We apply the model to behavioral data from a memory experiment involving the recall of heights of people. A cross-validation analysis shows that the Bayesian memory model with inferred subjective priors predicts withheld data better than a Bayesian model where the priors are based on environmental statistics. In addition, the model with inferred priors at the individual subject level led to the best overall generalization performance, suggesting that individual differences are important to consider in Bayesian models of cognition.

Perceived Effects of Emotion Intensity on Athletic Performance: A Contingency-Based Individualized Approach

ERIC Educational Resources Information Center

Robazza, Claudio; Bortoli, Laura; Hanin, Yuri

2006-01-01

This study, based on the Individual Zones of Optimal Functioning model, examined the perceived effect of idiosyncratic emotions and bodily symptoms on athletic performance along the entire emotion-intensity range. The participants were 35 elite Italian athletes, 16 women and 19 men, competing in either figure skating or gymnastics. Idiosyncratic…

Employee Uncertainty as a Factor in Occupational Stress.

ERIC Educational Resources Information Center

Beehr, Terry A.; O'Driscoll, Michael P.

Job stress is an area of research in which the relationships among job stressors (characteristics of the workplace) and individual strains (responses of the individual worker) are explored. The uncertainty model of occupational stress proposes that the two uncertainties (of effort-to-performance or E-->P and performance-to-outcome or P--0>)…

Individual Differences in Visual Word Recognition: Insights from the English Lexicon Project

ERIC Educational Resources Information Center

Yap, Melvin J.; Balota, David A.; Sibley, Daragh E.; Ratcliff, Roger

2012-01-01

Empirical work and models of visual word recognition have traditionally focused on group-level performance. Despite the emphasis on the prototypical reader, there is clear evidence that variation in reading skill modulates word recognition performance. In the present study, we examined differences among individuals who contributed to the English…

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes.

PubMed

Yates, Katherine L; Mellin, Camille; Caley, M Julian; Radford, Ben T; Meeuwig, Jessica J

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes

PubMed Central

Yates, Katherine L.; Mellin, Camille; Caley, M. Julian; Radford, Ben T.; Meeuwig, Jessica J.

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability. PMID:27333202

Individual Differences in Visual Word Recognition: Insights from the English Lexicon Project

PubMed Central

Yap, Melvin J.; Balota, David A.; Sibley, Daragh E.; Ratcliff, Roger

2011-01-01

Empirical work and models of visual word recognition have traditionally focused on group-level performance. Despite the emphasis on the prototypical reader, there is clear evidence that variation in reading skill modulates word recognition performance. In the present study, we examined differences between individuals who contributed to the English Lexicon Project (http://elexicon.wustl.edu), an online behavioral database containing nearly four million word recognition (speeded pronunciation and lexical decision) trials from over 1,200 participants. We observed considerable within- and between-session reliability across distinct sets of items, in terms of overall mean response time (RT), RT distributional characteristics, diffusion model parameters (Ratcliff, Gomez, & McKoon, 2004), and sensitivity to underlying lexical dimensions. This indicates reliably detectable individual differences in word recognition performance. In addition, higher vocabulary knowledge was associated with faster, more accurate word recognition performance, attenuated sensitivity to stimuli characteristics, and more efficient accumulation of information. Finally, in contrast to suggestions in the literature, we did not find evidence that individuals were trading-off in their utilization of lexical and nonlexical information. PMID:21728459

Testing the Self-Efficacy-Performance Linkage of Social-Cognitive Theory.

ERIC Educational Resources Information Center

Harrison, Allison W.; Rainer, R. Kelly, Jr.; Hochwarter, Wayne A.; Thompson, Kenneth R.

1997-01-01

Briefly reviews Albert Bandura's Self-Efficacy Performance Model (ability to perform a task is influenced by an individual's belief in their capability). Tests this model with a sample of 776 university employees and computer-related knowledge and skills. Results supported Bandura's thesis. Includes statistical tables and a discussion of related…

Athlete Characteristics and Team Competitive Performance as Moderators for the Relationship Between Coach Transformational Leadership and Athlete Performance.

PubMed

Bormann, Kai C; Schulte-Coerne, Paul; Diebig, Mathias; Rowold, Jens

2016-06-01

The goal of this study is to examine the effects of coaches' transformational leadership on player performance. To advance existing research, we examine (a) effects on individual and team performance and (b) consider joint moderating effects of players' win orientation and teams' competitive performance on the leadership- individual performance link. In a three-source sample from German handball teams, we collected data on 336 players and 30 coaches and teams. Results showed positive main effects of transformational leadership's facet of articulating a vision (AV) on team and individual performance and negative main effects of providing an appropriate model (PAM) on team performance. With regard to moderating effects, AV increased and PAM decreased individual performance when both moderators were low, and intellectual stimulation had a positive effect when both were high. This study expands insights into the potential and limitation of transformational leadership with a strong focus on the role of situational contingencies.

A connectionist model of category learning by individuals with high-functioning autism spectrum disorder.

PubMed

Dovgopoly, Alexander; Mercado, Eduardo

2013-06-01

Individuals with autism spectrum disorder (ASD) show atypical patterns of learning and generalization. We explored the possible impacts of autism-related neural abnormalities on perceptual category learning using a neural network model of visual cortical processing. When applied to experiments in which children or adults were trained to classify complex two-dimensional images, the model can account for atypical patterns of perceptual generalization. This is only possible, however, when individual differences in learning are taken into account. In particular, analyses performed with a self-organizing map suggested that individuals with high-functioning ASD show two distinct generalization patterns: one that is comparable to typical patterns, and a second in which there is almost no generalization. The model leads to novel predictions about how individuals will generalize when trained with simplified input sets and can explain why some researchers have failed to detect learning or generalization deficits in prior studies of category learning by individuals with autism. On the basis of these simulations, we propose that deficits in basic neural plasticity mechanisms may be sufficient to account for the atypical patterns of perceptual category learning and generalization associated with autism, but they do not account for why only a subset of individuals with autism would show such deficits. If variations in performance across subgroups reflect heterogeneous neural abnormalities, then future behavioral and neuroimaging studies of individuals with ASD will need to account for such disparities.

The Impact of Individual Differences, Types of Model and Social Settings on Block Building Performance among Chinese Preschoolers

PubMed Central

Tian, Mi; Deng, Zhu; Meng, Zhaokun; Li, Rui; Zhang, Zhiyi; Qi, Wenhui; Wang, Rui; Yin, Tingting; Ji, Menghui

2018-01-01

Children’s block building performances are used as indicators of other abilities in multiple domains. In the current study, we examined individual differences, types of model and social settings as influences on children’s block building performance. Chinese preschoolers (N = 180) participated in a block building activity in a natural setting, and performance was assessed with multiple measures in order to identify a range of specific skills. Using scores generated across these measures, three dependent variables were analyzed: block building skills, structural balance and structural features. An overall MANOVA showed that there were significant main effects of gender and grade level across most measures. Types of model showed no significant effect in children’s block building. There was a significant main effect of social settings on structural features, with the best performance in the 5-member group, followed by individual and then the 10-member block building. These findings suggest that boys performed better than girls in block building activity. Block building performance increased significantly from 1st to 2nd year of preschool, but not from second to third. The preschoolers created more representational constructions when presented with a model made of wooden rather than with a picture. There was partial evidence that children performed better when working with peers in a small group than when working alone or working in a large group. It is suggested that future study should examine other modalities rather than the visual one, diversify the samples and adopt a longitudinal investigation. PMID:29441031

A frontier analysis approach for benchmarking hospital performance in the treatment of acute myocardial infarction.

PubMed

Stanford, Robert E

2004-05-01

This paper uses a non-parametric frontier model and adaptations of the concepts of cross-efficiency and peer-appraisal to develop a formal methodology for benchmarking provider performance in the treatment of Acute Myocardial Infarction (AMI). Parameters used in the benchmarking process are the rates of proper recognition of indications of six standard treatment processes for AMI; the decision making units (DMUs) to be compared are the Medicare eligible hospitals of a particular state; the analysis produces an ordinal ranking of individual hospital performance scores. The cross-efficiency/peer-appraisal calculation process is constructed to accommodate DMUs that experience no patients in some of the treatment categories. While continuing to rate highly the performances of DMUs which are efficient in the Pareto-optimal sense, our model produces individual DMU performance scores that correlate significantly with good overall performance, as determined by a comparison of the sums of the individual DMU recognition rates for the six standard treatment processes. The methodology is applied to data collected from 107 state Medicare hospitals.

Use of genetic data to infer population-specific ecological and phenotypic traits from mixed aggregations

USGS Publications Warehouse

Moran, Paul; Bromaghin, Jeffrey F.; Masuda, Michele

2014-01-01

Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions.

Use of Genetic Data to Infer Population-Specific Ecological and Phenotypic Traits from Mixed Aggregations

PubMed Central

Moran, Paul; Bromaghin, Jeffrey F.; Masuda, Michele

2014-01-01

Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions. PMID:24905464

Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models.

PubMed

Campbell, William; Ganna, Andrea; Ingelsson, Erik; Janssens, A Cecile J W

2016-01-01

We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance. Copyright © 2016 Elsevier Inc. All rights reserved.

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability

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

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-15

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsetsmore » driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models. - Highlights: • Consensus method to predict ready biodegradability by prioritizing multiple QSARs. • Consensus reduced the amount of unpredictable chemicals to less than 2%. • Performance increased with the number of QSAR models considered. • The absence of 2D atom pairs contributed significantly to the consensus model.« less

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model.

PubMed

Winslow, Brent D; Nguyen, Nam; Venta, Kimberly E

2017-01-01

Sleep impairment significantly alters human brain structure and cognitive function, but available evidence suggests that adults in developed nations are sleeping less. A growing body of research has sought to use sleep to forecast cognitive performance by modeling the relationship between the two, but has generally focused on vigilance rather than other cognitive constructs affected by sleep, such as reaction time, executive function, and working memory. Previous modeling efforts have also utilized subjective, self-reported sleep durations and were restricted to laboratory environments. In the current effort, we addressed these limitations by employing wearable systems and mobile applications to gather objective sleep information, assess multi-construct cognitive performance, and model/predict changes to mental acuity. Thirty participants were recruited for participation in the study, which lasted 1 week. Using the Fitbit Charge HR and a mobile version of the automated neuropsychological assessment metric called CogGauge, we gathered a series of features and utilized the unified model of performance to predict mental acuity based on sleep records. Our results suggest that individuals poorly rate their sleep duration, supporting the need for objective sleep metrics to model circadian changes to mental acuity. Participant compliance in using the wearable throughout the week and responding to the CogGauge assessments was 80%. Specific biases were identified in temporal metrics across mobile devices and operating systems and were excluded from the mental acuity metric development. Individualized prediction of mental acuity consistently outperformed group modeling. This effort indicates the feasibility of creating an individualized, mobile assessment and prediction of mental acuity, compatible with the majority of current mobile devices.

Using GAMM to examine inter-individual heterogeneity in thermal performance curves for Natrix natrix indicates bet hedging strategy by mothers.

PubMed

Vickers, Mathew J; Aubret, Fabien; Coulon, Aurélie

2017-01-01

The thermal performance curve (TPC) illustrates the dependence on body- and therefore environmental- temperature of many fitness-related aspects of ectotherm ecology and biology including foraging, growth, predator avoidance, and reproduction. The typical thermal performance curve model is linear in its parameters despite the well-known, strong, non-linearity of the response of performance to temperature. In addition, it is usual to consider a single model based on few individuals as descriptive of a species-level response to temperature. To overcome these issues, we used generalized additive mixed modeling (GAMM) to estimate thermal performance curves for 73 individual hatchling Natrix natrix grass snakes from seven clutches, taking advantage of the structure of GAMM to demonstrate that almost 16% of the deviance in thermal performance curves is attributed to inter-individual variation, while only 1.3% is attributable to variation amongst clutches. GAMM allows precise estimation of curve characteristics, which we used to test hypotheses on tradeoffs thought to constrain the thermal performance curve: hotter is better, the specialist-generalist trade off, and resource allocation/acquisition. We observed a negative relationship between maximum performance and performance breadth, indicating a specialist-generalist tradeoff, and a positive relationship between thermal optimum and maximum performance, suggesting "hotter is better". There was a significant difference among matrilines in the relationship between Area Under the Curve and maximum performance - relationship that is an indicator of evenness in acquisition or allocation of resources. As we used unfed hatchlings, the observed matriline effect indicates divergent breeding strategies among mothers, with some mothers provisioning eggs unequally resulting in some offspring being better than others, while other mothers provisioned the eggs more evenly, resulting in even performance throughout the clutch. This observation is reminiscent of bet-hedging strategies, and implies the possibility for intra-clutch variability in the TPCs to buffer N. natrix against unpredictable environmental variability. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting the size of individual and group differences on speeded cognitive tasks.

PubMed

Chen, Jing; Hale, Sandra; Myerson, Joel

2007-06-01

An a priori test of the difference engine model (Myerson, Hale, Zheng, Jenkins, & Widaman, 2003) was conducted using a large, diverse sample of individuals who performed three speeded verbal tasks and three speeded visuospatial tasks. Results demonstrated that, as predicted by the model, the group standard deviation (SD) on any task was proportional to the amount of processing required by that task. Both individual performances as well as those of fast and slow subgroups could be accurately predicted by the model using no free parameters, just an individual or subgroup's mean z-score and the values of theoretical constructs estimated from fits to the group SDs. Taken together, these results are consistent with post hoc analyses reported by Myerson et al. and provide even stronger supporting evidence. In particular, the ability to make quantitative predictions without using any free parameters provides the clearest demonstration to date of the power of an analytic approach on the basis of the difference engine.

Predictive Monitoring for Improved Management of Glucose Levels

PubMed Central

Reifman, Jaques; Rajaraman, Srinivasan; Gribok, Andrei; Ward, W. Kenneth

2007-01-01

Background Recent developments and expected near-future improvements in continuous glucose monitoring (CGM) devices provide opportunities to couple them with mathematical forecasting models to produce predictive monitoring systems for early, proactive glycemia management of diabetes mellitus patients before glucose levels drift to undesirable levels. This article assesses the feasibility of data-driven models to serve as the forecasting engine of predictive monitoring systems. Methods We investigated the capabilities of data-driven autoregressive (AR) models to (1) capture the correlations in glucose time-series data, (2) make accurate predictions as a function of prediction horizon, and (3) be made portable from individual to individual without any need for model tuning. The investigation is performed by employing CGM data from nine type 1 diabetic subjects collected over a continuous 5-day period. Results With CGM data serving as the gold standard, AR model-based predictions of glucose levels assessed over nine subjects with Clarke error grid analysis indicated that, for a 30-minute prediction horizon, individually tuned models yield 97.6 to 100.0% of data in the clinically acceptable zones A and B, whereas cross-subject, portable models yield 95.8 to 99.7% of data in zones A and B. Conclusions This study shows that, for a 30-minute prediction horizon, data-driven AR models provide sufficiently-accurate and clinically-acceptable estimates of glucose levels for timely, proactive therapy and should be considered as the modeling engine for predictive monitoring of patients with type 1 diabetes mellitus. It also suggests that AR models can be made portable from individual to individual with minor performance penalties, while greatly reducing the burden associated with model tuning and data collection for model development. PMID:19885110

Identification of the Predictive Power of Five Factor Personality Traits for Individual Instrument Performance Anxiety

ERIC Educational Resources Information Center

Özdemir, Gökhan; Dalkiran, Esra

2017-01-01

This study, with the aim of identifying the predictive power of the five-factor personality traits of music teacher candidates on individual instrument performance anxiety, was designed according to the relational screening model. The study population was students attending the Music Education branch of Fine Arts Education Departments in…

A computational cognitive model of self-efficacy and daily adherence in mHealth.

PubMed

Pirolli, Peter

2016-12-01

Mobile health (mHealth) applications provide an excellent opportunity for collecting rich, fine-grained data necessary for understanding and predicting day-to-day health behavior change dynamics. A computational predictive model (ACT-R-DStress) is presented and fit to individual daily adherence in 28-day mHealth exercise programs. The ACT-R-DStress model refines the psychological construct of self-efficacy. To explain and predict the dynamics of self-efficacy and predict individual performance of targeted behaviors, the self-efficacy construct is implemented as a theory-based neurocognitive simulation of the interaction of behavioral goals, memories of past experiences, and behavioral performance.

Ancestral haplotype-based association mapping with generalized linear mixed models accounting for stratification.

PubMed

Zhang, Z; Guillaume, F; Sartelet, A; Charlier, C; Georges, M; Farnir, F; Druet, T

2012-10-01

In many situations, genome-wide association studies are performed in populations presenting stratification. Mixed models including a kinship matrix accounting for genetic relatedness among individuals have been shown to correct for population and/or family structure. Here we extend this methodology to generalized linear mixed models which properly model data under various distributions. In addition we perform association with ancestral haplotypes inferred using a hidden Markov model. The method was shown to properly account for stratification under various simulated scenari presenting population and/or family structure. Use of ancestral haplotypes resulted in higher power than SNPs on simulated datasets. Application to real data demonstrates the usefulness of the developed model. Full analysis of a dataset with 4600 individuals and 500 000 SNPs was performed in 2 h 36 min and required 2.28 Gb of RAM. The software GLASCOW can be freely downloaded from www.giga.ulg.ac.be/jcms/prod_381171/software. francois.guillaume@jouy.inra.fr Supplementary data are available at Bioinformatics online.

The model for Fundamentals of Endovascular Surgery (FEVS) successfully defines the competent endovascular surgeon.

PubMed

Duran, Cassidy; Estrada, Sean; O'Malley, Marcia; Sheahan, Malachi G; Shames, Murray L; Lee, Jason T; Bismuth, Jean

2015-12-01

Fundamental skills testing is now required for certification in general surgery. No model for assessing fundamental endovascular skills exists. Our objective was to develop a model that tests the fundamental endovascular skills and differentiates competent from noncompetent performance. The Fundamentals of Endovascular Surgery model was developed in silicon and virtual-reality versions. Twenty individuals (with a range of experience) performed four tasks on each model in three separate sessions. Tasks on the silicon model were performed under fluoroscopic guidance, and electromagnetic tracking captured motion metrics for catheter tip position. Image processing captured tool tip position and motion on the virtual model. Performance was evaluated using a global rating scale, blinded video assessment of error metrics, and catheter tip movement and position. Motion analysis was based on derivations of speed and position that define proficiency of movement (spectral arc length, duration of submovement, and number of submovements). Performance was significantly different between competent and noncompetent interventionalists for the three performance measures of motion metrics, error metrics, and global rating scale. The mean error metric score was 6.83 for noncompetent individuals and 2.51 for the competent group (P < .0001). Median global rating scores were 2.25 for the noncompetent group and 4.75 for the competent users (P < .0001). The Fundamentals of Endovascular Surgery model successfully differentiates competent and noncompetent performance of fundamental endovascular skills based on a series of objective performance measures. This model could serve as a platform for skills testing for all trainees. Copyright © 2015 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Building communities through performance: emerging approaches to interculturality.

PubMed

Parent, Roger

2009-08-01

Changing definitions of culture are modifying approaches to intercultural education and training. This paper outlines the principal features of these emerging models for innovation and capacity building in communities. Semiotics provides a theoretical frame for the interdisciplinary analysis of research on cultural competency, especially regarding recent studies on "cultural intelligence", performance and creativity. Interdisciplinary research on cultural literacy is shifting from cultural knowledge to intercultural know-how. This know-how translates into the individual's capacity to innovate and illustrates the influence of culture on individual and group performance. Research on cultural intelligence, performance and creativity provides promising new models for capacity building in communities. These approaches constitute a synthesis of previous research on cultural competency and provide new avenues for innovative social action through intercultural exchange.

A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 1. Trajectories of life function priorities and genetic scaling.

PubMed

Martin, O; Sauvant, D

2010-12-01

The prediction of the control of nutrient partitioning, particularly energy, is a major issue in modelling dairy cattle performance. The proportions of energy channelled to physiological functions (growth, maintenance, gestation and lactation) change as the animal ages and reproduces, and according to its genotype and nutritional environment. This is the first of two papers describing a teleonomic model of individual performance during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. The conceptual framework is based on the coupling of a regulating sub-model providing teleonomic drives to govern the work of an operating sub-model scaled with genetic parameters. The regulating sub-model describes the dynamic partitioning of a mammal female's priority between life functions targeted to growth (G), ageing (A), balance of body reserves (R) and nutrient supply of the unborn (U), newborn (N) and suckling (S) calf. The so-called GARUNS dynamic pattern defines a trajectory of relative priorities, goal directed towards the survival of the individual for the continuation of the specie. The operating sub-model describes changes in body weight (BW) and composition, foetal growth, milk yield and composition and food intake in dairy cows throughout their lifespan, that is, during growth, over successive reproductive cycles and through ageing. This dynamic pattern of performance defines a reference trajectory of a cow under normal husbandry conditions and feed regimen. Genetic parameters are incorporated in the model to scale individual performance and simulate differences within and between breeds. The model was calibrated for dairy cows with literature data. The model was evaluated by comparison with simulations of previously published empirical equations of BW, body condition score, milk yield and composition and feed intake. This evaluation showed that the model adequately simulates these production variables throughout the lifespan, and across a range of dairy cattle genotypes.

A Spatial Statistical Model for Landscape Genetics

PubMed Central

Guillot, Gilles; Estoup, Arnaud; Mortier, Frédéric; Cosson, Jean François

2005-01-01

Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., FST < 0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites. PMID:15520263

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.

PubMed

Aerts, Hannelore; Schirner, Michael; Jeurissen, Ben; Van Roost, Dirk; Achten, Eric; Ritter, Petra; Marinazzo, Daniele

2018-01-01

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

Model-based prognostics for batteries which estimates useful life and uses a probability density function

NASA Technical Reports Server (NTRS)

Saha, Bhaskar (Inventor); Goebel, Kai F. (Inventor)

2012-01-01

This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.

Simulation Models for Developing an Individualized, Performance Criterion Learning Situation. Technical Monograph No. 21.

ERIC Educational Resources Information Center

Anderson, G. Ernest, Jr.

The mission of the simulation team of the Model Elementary Teacher Education Project, 1968-71, was to develop simulation tools and conduct appropriate studies of the anticipated operation of that project. The team focused on the experiences of individual students and on the resources necessary for these experiences to be reasonable. This report…

Individual Skills Based Volunteerism and Life Satisfaction among Healthcare Volunteers in Malaysia: Role of Employer Encouragement, Self-Esteem and Job Performance, A Cross-Sectional Study

PubMed Central

Veerasamy, Chanthiran; Sambasivan, Murali; Kumar, Naresh

2013-01-01

The purpose of this paper is to analyze two important outcomes of individual skills-based volunteerism (ISB-V) among healthcare volunteers in Malaysia. The outcomes are: job performance and life satisfaction. This study has empirically tested the impact of individual dimensions of ISB-V along with their inter-relationships in explaining the life satisfaction and job performance. Besides, the effects of employer encouragement to the volunteers, demographic characteristics of volunteers, and self-esteem of volunteers on job performance and life satisfaction have been studied. The data were collected through a questionnaire distributed to 1000 volunteers of St. John Ambulance in Malaysia. Three hundred and sixty six volunteers responded by giving their feedback. The model was tested using Structural Equation Modeling (SEM). The main results of this study are: (1) Volunteer duration and nature of contact affects life satisfaction, (2) volunteer frequency has impact on volunteer duration, (3) self-esteem of volunteers has significant relationships with volunteer frequency, job performance and life satisfaction, (4) job performance of volunteers affect their life satisfaction and (5) current employment level has significant relationships with duration of volunteering, self esteem, employer encouragement and job performance of volunteers. The model in this study has been able to explain 39% of the variance in life satisfaction and 45% of the variance in job performance. The current study adds significantly to the body of knowledge on healthcare volunteerism. PMID:24194894

Individual skills based volunteerism and life satisfaction among healthcare volunteers in Malaysia: role of employer encouragement, self-esteem and job performance, a cross-sectional study.

PubMed

Veerasamy, Chanthiran; Sambasivan, Murali; Kumar, Naresh

2013-01-01

The purpose of this paper is to analyze two important outcomes of individual skills-based volunteerism (ISB-V) among healthcare volunteers in Malaysia. The outcomes are: job performance and life satisfaction. This study has empirically tested the impact of individual dimensions of ISB-V along with their inter-relationships in explaining the life satisfaction and job performance. Besides, the effects of employer encouragement to the volunteers, demographic characteristics of volunteers, and self-esteem of volunteers on job performance and life satisfaction have been studied. The data were collected through a questionnaire distributed to 1000 volunteers of St. John Ambulance in Malaysia. Three hundred and sixty six volunteers responded by giving their feedback. The model was tested using Structural Equation Modeling (SEM). The main results of this study are: (1) Volunteer duration and nature of contact affects life satisfaction, (2) volunteer frequency has impact on volunteer duration, (3) self-esteem of volunteers has significant relationships with volunteer frequency, job performance and life satisfaction, (4) job performance of volunteers affect their life satisfaction and (5) current employment level has significant relationships with duration of volunteering, self esteem, employer encouragement and job performance of volunteers. The model in this study has been able to explain 39% of the variance in life satisfaction and 45% of the variance in job performance. The current study adds significantly to the body of knowledge on healthcare volunteerism.

Stage effects on stalling and recovery of a high-speed 10-stage axial-flow compressor

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

Copenhaver, W.W.

1988-01-01

Results of a high-speed 10-stage axial-flow compressor test involving overall compressor and individual stage performance while stalling and operating in quasi-steady rotating stall are described. Test procedures and data-acquisition methods used to obtain the dynamic stalling and quasi-steady in-stall data are explained. Unstalled and in-stall time-averaged data obtained from the compressor operating at five different shaft speeds and one off-schedule variable vane condition are presented. Effects of compressor speed and variable geometry on overall compressor in-stall pressure rise and hysteresis extent are illustrated through the use of quasi-steady-stage temperature rise and pressure-rise characteristics. Results indicate that individual stage performance duringmore » overall compressor rotating stall operation varies considerably throughout the length of the compressor. The measured high-speed 10-stage test compressor individual stage pressure and temperature characteristics were input into a stage-by-stage dynamic compressor performance model. Comparison of the model results and measured pressures provided the additional validation necessary to demonstrate the model's ability to predict high-speed multistage compressor stalling and in-stall performance.« less

Efficacy of Multimedia Instruction and an Introduction to Digital Multimedia Technology

DTIC Science & Technology

1992-07-01

performed by Bandura , Ro3s and Ross (1961). They found that children exposed to an adult displaying aggression toward a Bobo doll later also performed...and enjoy successful task performance. 7 Modeling Bandura (1969) describes modeling as the ability of individuals to learn a behavior or attitude... Bandura argued that all learning involving direct reinforcement could also result from observation. A classic study of modeling is an experiment

Effects of Data Anonymization by Cell Suppression on Descriptive Statistics and Predictive Modeling Performance

PubMed Central

Ohno-Machado, Lucila; Vinterbo, Staal; Dreiseitl, Stephan

2002-01-01

Protecting individual data in disclosed databases is essential. Data anonymization strategies can produce table ambiguation by suppression of selected cells. Using table ambiguation, different degrees of anonymization can be achieved, depending on the number of individuals that a particular case must become indistinguishable from. This number defines the level of anonymization. Anonymization by cell suppression does not necessarily prevent inferences from being made from the disclosed data. Preventing inferences may be important to preserve confidentiality. We show that anonymized data sets can preserve descriptive characteristics of the data, but might also be used for making inferences on particular individuals, which is a feature that may not be desirable. The degradation of predictive performance is directly proportional to the degree of anonymity. As an example, we report the effect of anonymization on the predictive performance of a model constructed to estimate the probability of disease given clinical findings.

Effects of data anonymization by cell suppression on descriptive statistics and predictive modeling performance.

PubMed Central

Ohno-Machado, L.; Vinterbo, S. A.; Dreiseitl, S.

2001-01-01

Protecting individual data in disclosed databases is essential. Data anonymization strategies can produce table ambiguation by suppression of selected cells. Using table ambiguation, different degrees of anonymization can be achieved, depending on the number of individuals that a particular case must become indistinguishable from. This number defines the level of anonymization. Anonymization by cell suppression does not necessarily prevent inferences from being made from the disclosed data. Preventing inferences may be important to preserve confidentiality. We show that anonymized data sets can preserve descriptive characteristics of the data, but might also be used for making inferences on particular individuals, which is a feature that may not be desirable. The degradation of predictive performance is directly proportional to the degree of anonymity. As an example, we report the effect of anonymization on the predictive performance of a model constructed to estimate the probability of disease given clinical findings. PMID:11825239

Brain Regions Engaged by Part- and Whole-task Performance in a Video Game: A Model-based Test of the Decomposition Hypothesis

PubMed Central

Anderson, John R.; Bothell, Daniel; Fincham, Jon M.; Anderson, Abraham R.; Poole, Ben; Qin, Yulin

2013-01-01

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model’s predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits. PMID:21557648

A Generalized Thurstonian Paired Comparison Multicriteria Heuristic Model for Peer Evaluation of Individual Performance on IS Team Projects

ERIC Educational Resources Information Center

Scher, Julian M.

2010-01-01

Information Systems instructors are generally encouraged to introduce team projects into their pedagogy, with a consequential issue of objectively evaluating the performance of each individual team member. The concept of "freeloading" is well-known for team projects, and for this, and other reasons, a peer review process of team members,…

From neural oscillations to reasoning ability: Simulating the effect of the theta-to-gamma cycle length ratio on individual scores in a figural analogy test.

PubMed

Chuderski, Adam; Andrelczyk, Krzysztof

2015-02-01

Several existing computational models of working memory (WM) have predicted a positive relationship (later confirmed empirically) between WM capacity and the individual ratio of theta to gamma oscillatory band lengths. These models assume that each gamma cycle represents one WM object (e.g., a binding of its features), whereas the theta cycle integrates such objects into the maintained list. As WM capacity strongly predicts reasoning, it might be expected that this ratio also predicts performance in reasoning tasks. However, no computational model has yet explained how the differences in the theta-to-gamma ratio found among adult individuals might contribute to their scores on a reasoning test. Here, we propose a novel model of how WM capacity constraints figural analogical reasoning, aimed at explaining inter-individual differences in reasoning scores in terms of the characteristics of oscillatory patterns in the brain. In the model, the gamma cycle encodes the bindings between objects/features and the roles they play in the relations processed. Asynchrony between consecutive gamma cycles results from lateral inhibition between oscillating bindings. Computer simulations showed that achieving the highest WM capacity required reaching the optimal level of inhibition. When too strong, this inhibition eliminated some bindings from WM, whereas, when inhibition was too weak, the bindings became unstable and fell apart or became improperly grouped. The model aptly replicated several empirical effects and the distribution of individual scores, as well as the patterns of correlations found in the 100-people sample attempting the same reasoning task. Most importantly, the model's reasoning performance strongly depended on its theta-to-gamma ratio in same way as the performance of human participants depended on their WM capacity. The data suggest that proper regulation of oscillations in the theta and gamma bands may be crucial for both high WM capacity and effective complex cognition. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

A physiologically-based model for simulation of color vision deficiency.

PubMed

Machado, Gustavo M; Oliveira, Manuel M; Fernandes, Leandro A F

2009-01-01

Color vision deficiency (CVD) affects approximately 200 million people worldwide, compromising the ability of these individuals to effectively perform color and visualization-related tasks. This has a significant impact on their private and professional lives. We present a physiologically-based model for simulating color vision. Our model is based on the stage theory of human color vision and is derived from data reported in electrophysiological studies. It is the first model to consistently handle normal color vision, anomalous trichromacy, and dichromacy in a unified way. We have validated the proposed model through an experimental evaluation involving groups of color vision deficient individuals and normal color vision ones. Our model can provide insights and feedback on how to improve visualization experiences for individuals with CVD. It also provides a framework for testing hypotheses about some aspects of the retinal photoreceptors in color vision deficient individuals.

IASM: Individualized activity space modeler

NASA Astrophysics Data System (ADS)

Hasanzadeh, Kamyar

2018-01-01

Researchers from various disciplines have long been interested in analyzing and describing human mobility patterns. Activity space (AS), defined as an area encapsulating daily human mobility and activities, has been at the center of this interest. However, given the applied nature of research in this field and the complexity that advanced geographical modeling can pose to its users, the proposed models remain simplistic and inaccurate in many cases. Individualized Activity Space Modeler (IASM) is a geographic information system (GIS) toolbox, written in Python programming language using ESRI's Arcpy module, comprising four tools aiming to facilitate the use of advanced activity space models in empirical research. IASM provides individual-based and context-sensitive tools to estimate home range distances, delineate activity spaces, and model place exposures using individualized geographical data. In this paper, we describe the design and functionality of IASM, and provide an example of how it performs on a spatial dataset collected through an online map-based survey.

Are well functioning civil registration and vital statistics systems associated with better health outcomes?

PubMed

Phillips, David E; AbouZahr, Carla; Lopez, Alan D; Mikkelsen, Lene; de Savigny, Don; Lozano, Rafael; Wilmoth, John; Setel, Philip W

2015-10-03

In this Series paper, we examine whether well functioning civil registration and vital statistics (CRVS) systems are associated with improved population health outcomes. We present a conceptual model connecting CRVS to wellbeing, and describe an ecological association between CRVS and health outcomes. The conceptual model posits that the legal identity that civil registration provides to individuals is key to access entitlements and services. Vital statistics produced by CRVS systems provide essential information for public health policy and prevention. These outcomes benefit individuals and societies, including improved health. We use marginal linear models and lag-lead analysis to measure ecological associations between a composite metric of CRVS performance and three health outcomes. Results are consistent with the conceptual model: improved CRVS performance coincides with improved health outcomes worldwide in a temporally consistent manner. Investment to strengthen CRVS systems is not only an important goal for individuals and societies, but also a development imperative that is good for health. Copyright © 2015 Elsevier Ltd. All rights reserved.

Inverse sampling regression for pooled data.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Eskridge, Kent; Crossa, José

2017-06-01

Because pools are tested instead of individuals in group testing, this technique is helpful for estimating prevalence in a population or for classifying a large number of individuals into two groups at a low cost. For this reason, group testing is a well-known means of saving costs and producing precise estimates. In this paper, we developed a mixed-effect group testing regression that is useful when the data-collecting process is performed using inverse sampling. This model allows including covariate information at the individual level to incorporate heterogeneity among individuals and identify which covariates are associated with positive individuals. We present an approach to fit this model using maximum likelihood and we performed a simulation study to evaluate the quality of the estimates. Based on the simulation study, we found that the proposed regression method for inverse sampling with group testing produces parameter estimates with low bias when the pre-specified number of positive pools (r) to stop the sampling process is at least 10 and the number of clusters in the sample is also at least 10. We performed an application with real data and we provide an NLMIXED code that researchers can use to implement this method.

A Pirate's Life: A Model and a Metaphor for Learning.

ERIC Educational Resources Information Center

Solomon, David L.

2002-01-01

Discusses various ways in which context may be interpreted to enhance learning and performance; illustrates domains of learning using a hockey team as an example; and suggests implications for learning, performance, and instructional design. Highlights include an ecological systems model; and examples of individual development, team learning, and…

Modelling Short-Term Maximum Individual Exposure from Airborne Hazardous Releases in Urban Environments. Part ΙI: Validation of a Deterministic Model with Wind Tunnel Experimental Data.

PubMed

Efthimiou, George C; Bartzis, John G; Berbekar, Eva; Hertwig, Denise; Harms, Frank; Leitl, Bernd

2015-06-26

The capability to predict short-term maximum individual exposure is very important for several applications including, for example, deliberate/accidental release of hazardous substances, odour fluctuations or material flammability level exceedance. Recently, authors have proposed a simple approach relating maximum individual exposure to parameters such as the fluctuation intensity and the concentration integral time scale. In the first part of this study (Part I), the methodology was validated against field measurements, which are governed by the natural variability of atmospheric boundary conditions. In Part II of this study, an in-depth validation of the approach is performed using reference data recorded under truly stationary and well documented flow conditions. For this reason, a boundary-layer wind-tunnel experiment was used. The experimental dataset includes 196 time-resolved concentration measurements which detect the dispersion from a continuous point source within an urban model of semi-idealized complexity. The data analysis allowed the improvement of an important model parameter. The model performed very well in predicting the maximum individual exposure, presenting a factor of two of observations equal to 95%. For large time intervals, an exponential correction term has been introduced in the model based on the experimental observations. The new model is capable of predicting all time intervals giving an overall factor of two of observations equal to 100%.

The Relationship between Shared Mental Models and Task Performance in an Online Team- Based Learning Environment

ERIC Educational Resources Information Center

Johnson, Tristan E.; Lee, Youngmin

2008-01-01

In an effort to better understand learning teams, this study examines the effects of shared mental models on team and individual performance. The results indicate that each team's shared mental model changed significantly over the time that subjects participated in team-based learning activities. The results also showed that the shared mental…

Bridging the etiologic and prognostic outlooks in individualized assessment of absolute risk of an illness: application in lung cancer.

PubMed

Karp, Igor; Sylvestre, Marie-Pierre; Abrahamowicz, Michal; Leffondré, Karen; Siemiatycki, Jack

2016-11-01

Assessment of individual risk of illness is an important activity in preventive medicine. Development of risk-assessment models has heretofore relied predominantly on studies involving follow-up of cohort-type populations, while case-control studies have generally been considered unfit for this purpose. To present a method for individualized assessment of absolute risk of an illness (as illustrated by lung cancer) based on data from a 'non-nested' case-control study. We used data from a case-control study conducted in Montreal, Canada in 1996-2001. Individuals diagnosed with lung cancer (n = 920) and age- and sex-matched lung-cancer-free subjects (n = 1288) completed questionnaires documenting life-time cigarette-smoking history and occupational, medical, and family history. Unweighted and weighted logistic models were fitted. Model overfitting was assessed using bootstrap-based cross-validation and 'shrinkage.' The discriminating ability was assessed by the c-statistic, and the risk-stratifying performance was assessed by examination of the variability in risk estimates over hypothetical risk-profiles. In the logistic models, the logarithm of incidence-density of lung cancer was expressed as a function of age, sex, cigarette-smoking history, history of respiratory conditions and exposure to occupational carcinogens, and family history of lung cancer. The models entailed a minimal degree of overfitting ('shrinkage' factor: 0.97 for both unweighted and weighted models) and moderately high discriminating ability (c-statistic: 0.82 for the unweighted model and 0.66 for the weighted model). The method's risk-stratifying performance was quite high. The presented method allows for individualized assessment of risk of lung cancer and can be used for development of risk-assessment models for other illnesses.

Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: methodology and preliminary report.

PubMed

Fu, Jicheng; Jones, Maria; Jan, Yih-Kuen

2014-01-01

Wheelchair tilt and recline functions are two of the most desirable features for relieving seating pressure to decrease the risk of pressure ulcers. The effective guidance on wheelchair tilt and recline usage is therefore critical to pressure ulcer prevention. The aim of this study was to demonstrate the feasibility of using machine learning techniques to construct an intelligent model to provide personalized guidance to individuals with spinal cord injury (SCI). The motivation stems from the clinical evidence that the requirements of individuals vary greatly and that no universal guidance on tilt and recline usage could possibly satisfy all individuals with SCI. We explored all aspects involved in constructing the intelligent model and proposed approaches tailored to suit the characteristics of this preliminary study, such as the way of modeling research participants, using machine learning techniques to construct the intelligent model, and evaluating the performance of the intelligent model. We further improved the intelligent model's prediction accuracy by developing a two-phase feature selection algorithm to identify important attributes. Experimental results demonstrated that our approaches held the promise: they could effectively construct the intelligent model, evaluate its performance, and refine the participant model so that the intelligent model's prediction accuracy was significantly improved.

[Model oriented assessment of literacy performance in children with cochlear implants].

PubMed

Fiori, A; Reichmuth, K; Matulat, P; Schmidt, C M; Dinnesen, A G

2006-07-01

Although most hearing-impaired children lag behind normally hearing children in literacy acquisition, this aspect has hardly been addressed in the evaluation of language acquisition after cochlear implantation. The present study investigated written language abilities in 8 school-age children with cochlear implants. Neurolinguistic dual-route-models of written language processing indicate that literacy acquisition leads to the establishment of two distinct reading and writing strategies: a lexical one for the quick processing of known words and a sublexical one for decoding unfamiliar words or nonwords letter by letter. 8 school-aged children were investigated, a very heterogeneous group concerning age of onset of hearing impairment, educational placement, and competences in sign language. However, this range is typical of the group of CI-children. The aim was to investigate if children with cochlear implants are able to establish both strategies or if they need to find a differential and individual access to written language. Performance within the Salzburger Lese-Rechtschreib-Test was evaluated. Individual performance of each subject was analysed. Performance varied substantially ranging from only rudimentary spoken and written language abilities in two children to age-equivalent performance in three of them. Severe qualitative differences in written language processing were shown in the remaining three subjects. Suggestions for remediation were made and a re-test was carried out after 12 months. Their individual profiles of performance are described in detail. The present study stresses the importance of a thorough investigation of written language performance in the evaluation of language acquisition after cochlear implantation. The results draw a very heterogeneous picture of performance. Model-oriented testing and analysis of performance prove to be sensible in at least a subpopulation of children with cochlear implants. Based on a better understanding of their acquired word-processing strategies, remediation programs meeting the needs of each individual child can be derived.

Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases.

PubMed

Hwang, Eui Jin; Goo, Jin Mo; Kim, Jihye; Park, Sang Joon; Ahn, Soyeon; Park, Chang Min; Shin, Yeong-Gil

2017-08-01

To develop a prediction model for the variability range of lung nodule volumetry and validate the model in detecting nodule growth. For model development, 50 patients with metastatic nodules were prospectively included. Two consecutive CT scans were performed to assess volumetry for 1,586 nodules. Nodule volume, surface voxel proportion (SVP), attachment proportion (AP) and absolute percentage error (APE) were calculated for each nodule and quantile regression analyses were performed to model the 95% percentile of APE. For validation, 41 patients who underwent metastasectomy were included. After volumetry of resected nodules, sensitivity and specificity for diagnosis of metastatic nodules were compared between two different thresholds of nodule growth determination: uniform 25% volume change threshold and individualized threshold calculated from the model (estimated 95% percentile APE). SVP and AP were included in the final model: Estimated 95% percentile APE = 37.82 · SVP + 48.60 · AP-10.87. In the validation session, the individualized threshold showed significantly higher sensitivity for diagnosis of metastatic nodules than the uniform 25% threshold (75.0% vs. 66.0%, P = 0.004) CONCLUSION: Estimated 95% percentile APE as an individualized threshold of nodule growth showed greater sensitivity in diagnosing metastatic nodules than a global 25% threshold. • The 95 % percentile APE of a particular nodule can be predicted. • Estimated 95 % percentile APE can be utilized as an individualized threshold. • More sensitive diagnosis of metastasis can be made with an individualized threshold. • Tailored nodule management can be provided during nodule growth follow-up.

Adjustments of individual-tree survival and diameter-growth equations to match whole-stand attributes

Treesearch

Quang V. Cao

2010-01-01

Individual-tree models are flexible and can perform well in predicting tree survival and diameter growth for a certain growing period. However, the resulting stand-level outputs often suffer from accumulation of errors and subsequently cannot compete with predictions from whole-stand models, especially when the projection period lengthens. Evaluated in this study were...

Of Models and Mechanisms: Towards an Understanding of How Theatre-Making Works as an "Intervention" in Individual Health and Wellness

ERIC Educational Resources Information Center

Etheridge Woodson, Stephani; Szkupinski Quiroga, Seline; Underiner, Tamara; Farid Karimi, Robert

2017-01-01

Growing from a multi-year and multidisciplinary research and applied arts investigative team based in North America, this essay presents a model of how performative engagements contribute to individual behavioural change in wellness practices. To be even more specific, this essay analyses and theorises the mechanisms involved in the application of…

Does the mean adequately represent reading performance? Evidence from a cross-linguistic study

PubMed Central

Marinelli, Chiara V.; Horne, Joanna K.; McGeown, Sarah P.; Zoccolotti, Pierluigi; Martelli, Marialuisa

2014-01-01

Reading models are largely based on the interpretation of average data from normal or impaired readers, mainly drawn from English-speaking individuals. In the present study we evaluated the possible contribution of orthographic consistency in generating individual differences in reading behavior. We compared the reading performance of young adults speaking English (one of the most irregular orthographies) and Italian (a very regular orthography). In the 1st experiment we presented 22 English and 30 Italian readers with 5-letter words using the Rapid Serial Visual Presentation (RSVP) paradigm. In a 2nd experiment, we evaluated a new group of 26 English and 32 Italian proficient readers through the RSVP procedure and lists matched in the two languages for both number of phonemes and letters. The results of the two experiments indicate that English participants read at a similar rate but with much greater individual differences than the Italian participants. In a 3rd experiment, we extended these results to a vocal reaction time (vRT) task, examining the effect of word frequency. An ex-Gaussian distribution analysis revealed differences between languages in the size of the exponential parameter (tau) and in the variance (sigma), but not the mean, of the Gaussian component. Notably, English readers were more variable for both tau and sigma than Italian readers. The pattern of performance in English individuals runs counter to models of performance in timed tasks (Faust et al., 1999; Myerson et al., 2003) which envisage a general relationship between mean performance and variability; indeed, this relationship does not hold in the case of the English participants. The present data highlight the importance of developing reading models that not only capture mean level performance, but also variability across individuals, especially in order to account for cross-linguistic differences in reading behavior. PMID:25191289

Population pharmacokinetics of Rilpivirine in HIV-1-infected patients treated with the single-tablet regimen rilpivirine/tenofovir/emtricitabine.

PubMed

Néant, Nadège; Gattacceca, Florence; Lê, Minh Patrick; Yazdanpanah, Yazdan; Dhiver, Catherine; Bregigeon, Sylvie; Mokhtari, Saadia; Peytavin, Gilles; Tamalet, Catherine; Descamps, Diane; Lacarelle, Bruno; Solas, Caroline

2018-04-01

Rilpivirine, prescribed for the treatment of HIV infection, presents an important inter-individual pharmacokinetic variability. We aimed to determine population pharmacokinetic parameters of rilpivirine in adult HIV-infected patients and quantify their inter-individual variability. We conducted a multicenter, retrospective, and observational study in patients treated with the once-daily rilpivirine/tenofovir disoproxil fumarate/emtricitabine regimen. As part of routine therapeutic drug monitoring, rilpivirine concentrations were measured by UPLC-MS/MS. Population pharmacokinetic analysis was performed using NONMEM software. Once the compartmental and random effects models were selected, covariates were tested to explain the inter-individual variability in pharmacokinetic parameters. The final model qualification was performed by both statistical and graphical methods. We included 379 patients, resulting in the analysis of 779 rilpivirine plasma concentrations. Of the observed trough individual plasma concentrations, 24.4% were below the 50 ng/ml minimal effective concentration. A one-compartment model with first-order absorption best described the data. The estimated fixed effect for plasma apparent clearance and distribution volume were 9 L/h and 321 L, respectively, resulting in a half-life of 25.2 h. The common inter-individual variability for both parameters was 34.1% at both the first and the second occasions. The inter-individual variability of clearance was 30.3%. Our results showed a terminal half-life lower than reported and a high proportion of patients with suboptimal rilpivirine concentrations, which highlights the interest of using therapeutic drug monitoring in clinical practice. The population analysis performed with data from "real-life" conditions resulted in reliable post hoc estimates of pharmacokinetic parameters, suitable for individualization of dosing regimen.

Team Performance and Error Management in Chinese and American Simulated Flight Crews: The Role of Cultural and Individual Differences

NASA Technical Reports Server (NTRS)

Davis, Donald D.; Bryant, Janet L.; Tedrow, Lara; Liu, Ying; Selgrade, Katherine A.; Downey, Heather J.

2005-01-01

This report describes results of a study conducted for NASA-Langley Research Center. This study is part of a program of research conducted for NASA-LARC that has focused on identifying the influence of national culture on the performance of flight crews. We first reviewed the literature devoted to models of teamwork and team performance, crew resource management, error management, and cross-cultural psychology. Davis (1999) reported the results of this review and presented a model that depicted how national culture could influence teamwork and performance in flight crews. The second study in this research program examined accident investigations of foreign airlines in the United States conducted by the National Transportation Safety Board (NTSB). The ability of cross-cultural values to explain national differences in flight outcomes was examined. Cultural values were found to covary in a predicted way with national differences, but the absence of necessary data in the NTSB reports and limitations in the research method that was used prevented a clear understanding of the causal impact of cultural values. Moreover, individual differences such as personality traits were not examined in this study. Davis and Kuang (2001) report results of this second study. The research summarized in the current report extends this previous research by directly assessing cultural and individual differences among students from the United States and China who were trained to fly in a flight simulator using desktop computer workstations. The research design used in this study allowed delineation of the impact of national origin, cultural values, personality traits, cognitive style, shared mental model, and task workload on teamwork, error management and flight outcomes. We briefly review the literature that documents the importance of teamwork and error management and its impact on flight crew performance. We next examine teamwork and crew resource management training designed to improve teamwork. This is followed by discussion of the potential influence of national culture on teamwork and crew resource management. We then examine the influence of other individual and team differences, such as personality traits, cognitive style, shared mental model, and task workload. We provide a heuristic model that depicts the influence of national culture and individual differences on teamwork, error management and flight outcomes. The results demonstrate the usefulness of the model for future research.

On the transferability of RegCM4: Europe, Africa and Asia

NASA Astrophysics Data System (ADS)

Belda, Michal; Halenka, Tomas

2013-04-01

Simulations driven by ERA-interim reanalysis for CORDEX domains covering Europe, Africa and Asia have been performed using RegCM4 at 50 km resolution. The same settings are used in basic simulations and preliminary evaluation of model performance for individual regions will be presented. Several settings of different options is tested and sensitivity of selected ones will be shown in individual regions. Secant Mercator projection is introduced for Africa providing more efficient model geometry setting, the impact of proper emissivity inclusion is compared especially for Africa and Asia desserts. CRU data are used for the validation.

Many Districts Left Behind: An Individual Change Analysis of Inequity in the Kenyan Primary Educational Opportunities (2001-2007)

ERIC Educational Resources Information Center

Bagaka's, Joshua Gisemba

2010-01-01

The study examined variations in district performance in KCPE national examination in Kenya between 2001 and 2007. The individual change model revealed that district poverty rate was not a significant predictor of either the initial district performance (2001) or the rate of change over the seven-year period. The regional context of North Eastern…

Relationships Between Selected Teacher Behaviors and Pupil Academic Achievement: Preliminary Observations (Sample Project A). The Effect of Teacher Input on Student Performance (Sample Project B). Technical Report #35.

ERIC Educational Resources Information Center

Au, Kathryn H.

This Kamehameha Early Education Program (KEEP) report describes two studies on the effects of student-teacher interaction on student performance. Study I explored the relationship between three kinds of teacher behaviors (modeling, teacher attention to individual students, and praise-giving to individual students) and the pupil's academic…

General job performance of first-line supervisors: the role of conscientiousness in determining its effects on subordinate exhaustion.

PubMed

Perry, Sara Jansen; Rubino, Cristina; Witt, L A

2011-04-01

In an integrated test of the job demands-resources model and trait activation theory, we predicted that the general job performance of employees who also hold supervisory roles may act as a demand to subordinates, depending on levels of subordinate conscientiousness. In a sample of 313 customer service call centre employees, we found that high-conscientiousness individuals were more likely to experience emotional exhaustion, and low-conscientiousness individuals were less likely as the general job performance of their supervisor improved. The results were curvilinear, such that high-conscientiousness individuals' exhaustion levelled off with very high supervisor performance (two standard deviations above the mean), and low-conscientiousness individuals' exhaustion levelled off as supervisor performance improved from moderate to high. These findings suggest high-conscientiousness employees may efficiently handle demands presented by a low-performing coworker who is their boss, but when performance expectations are high (i.e. high-performing boss), these achievement-oriented employees may direct their resources (i.e. energy and time) towards performance-related efforts at the expense of their well-being. Conversely, low-conscientiousness employees suffer when paired with a low-performing boss, but benefit from a supervisor who demonstrates at least moderate job performance.

Video Self-Modeling to Improve Academic Performance: A Literature Review

ERIC Educational Resources Information Center

Prater, Mary Anne; Carter, Nari; Hitchcock, Caryl; Dowrick, Peter

2012-01-01

Video self-modeling (VSM) has been used for decades to effectively improve individuals' behaviors and skills. The purpose of this review is to locate and analyze published studies that used VSM for typical school-based academic skills to determine the effect of VSM interventions on students' academic performance. Only eight studies were located…

Emotions and Golf Performance

ERIC Educational Resources Information Center

Cohen, Alexander B.; Tenenbaum, Gershon; English, R. William

2006-01-01

A multiple case study investigation is reported in which emotions and performance were assessed within the probabilistic individual zone of optimal functioning (IZOF) model (Kamata, Tenenbaum, & Hanin, 2002) to develop idiosyncratic emotion-performance profiles. These profiles were incorporated into a psychological skills training (PST)…

Biomechanical, psychosocial and individual risk factors predicting low back functional impairment among furniture distribution employees

PubMed Central

Ferguson, Sue A.; Allread, W. Gary; Burr, Deborah L.; Heaney, Catherine; Marras, William S.

2013-01-01

Background Biomechanical, psychosocial and individual risk factors for low back disorder have been studied extensively however few researchers have examined all three risk factors. The objective of this was to develop a low back disorder risk model in furniture distribution workers using biomechanical, psychosocial and individual risk factors. Methods This was a prospective study with a six month follow-up time. There were 454 subjects at 9 furniture distribution facilities enrolled in the study. Biomechanical exposure was evaluated using the American Conference of Governmental Industrial Hygienists (2001) lifting threshold limit values for low back injury risk. Psychosocial and individual risk factors were evaluated via questionnaires. Low back health functional status was measured using the lumbar motion monitor. Low back disorder cases were defined as a loss of low back functional performance of −0.14 or more. Findings There were 92 cases of meaningful loss in low back functional performance and 185 non cases. A multivariate logistic regression model included baseline functional performance probability, facility, perceived workload, intermediated reach distance number of exertions above threshold limit values, job tenure manual material handling, and age combined to provide a model sensitivity of 68.5% and specificity of 71.9%. Interpretation: The results of this study indicate which biomechanical, individual and psychosocial risk factors are important as well as how much of each risk factor is too much resulting in increased risk of low back disorder among furniture distribution workers. PMID:21955915

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

PubMed

Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

2018-02-01

To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

Performance trade-offs and ageing in the 'world's greatest athletes'.

PubMed

Careau, Vincent; Wilson, Robbie S

2017-08-16

The mechanistic foundations of performance trade-offs are clear: because body size and shape constrains movement, and muscles vary in strength and fibre type, certain physical traits should act in opposition with others (e.g. sprint versus endurance). Yet performance trade-offs are rarely detected, and traits are often positively correlated. A potential resolution to this conundrum is that within -individual performance trade-offs can be masked by among -individual variation in 'quality'. Although there is a current debate on how to unambiguously define and account for quality, no previous studies have partitioned trait correlations at the within- and among-individual levels. Here, we evaluate performance trade-offs among and within 1369 elite athletes that performed in a total of 6418 combined-events competitions (decathlon and heptathlon). Controlling for age, experience and wind conditions, we detected strong trade-offs between groups of functionally similar events (throwing versus jumping versus running) occurring at the among-individual level. We further modelled individual (co)variation in age-related plasticity of performance and found previously unseen trade-offs in throwing versus running performance that manifest through ageing. Our results verify that human performance is limited by fundamental genetic, environmental and ageing constraints that preclude the simultaneous improvement of performance in multiple dimensions. Identifying these constraints is fundamental to understanding performance trade-offs and predicting the ageing of motor function. © 2017 The Author(s).

Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

NASA Astrophysics Data System (ADS)

Zaherpour, Jamal; Gosling, Simon N.; Mount, Nick; Müller Schmied, Hannes; Veldkamp, Ted I. E.; Dankers, Rutger; Eisner, Stephanie; Gerten, Dieter; Gudmundsson, Lukas; Haddeland, Ingjerd; Hanasaki, Naota; Kim, Hyungjun; Leng, Guoyong; Liu, Junguo; Masaki, Yoshimitsu; Oki, Taikan; Pokhrel, Yadu; Satoh, Yusuke; Schewe, Jacob; Wada, Yoshihide

2018-06-01

Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models’ ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model—a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.

Modeling structural, dyadic, and individual factors: the inclusion and exclusion model of HIV related behavior.

PubMed

Albarracin, Dolores; Tannenbaum, Melanie B; Glasman, Laura R; Rothman, Alexander J

2010-12-01

Changing HIV-related behaviors requires addressing the individual, dyadic, and structural influences that shape them. This supplement of AIDS & Behavior presents frameworks that integrate these three influences on behavior. Concepts from these frameworks were selected to model the processes by which structural factors affect individual HIV-related behavior. In the Inclusion/Exclusion Model, material and symbolic inclusions and exclusions (sharing versus denying resources) regulate individuals' ability and motivation to detect, prevent, and treat HIV. Structural interventions create inclusions that increase one's ability or motivation to perform these behaviors or exclusions that hinder one's ability or motivation to execute counterproductive behaviors. The need to expand research regarding multilevel influences on HIV-related behavior is also discussed, particularly concerning further understanding of sustained behavior change and effective dissemination of evidence-based intervention strategies.

Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality

Treesearch

Susan L. King

2003-01-01

The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...

Relationship of cognitive and perceptual abilities to functional independence in adults who have had a stroke.

PubMed

Brown, Ted; Mapleston, Jennifer; Nairn, Allison; Molloy, Andrew

2013-03-01

Most individuals who have had a stroke present with some degree of residual cognitive and/or perceptual impairment. Occupational therapists often utilize standardized cognitive and perceptual assessments with clients to establish a baseline of skill performance as well as to inform goal setting and intervention planning. Being able to predict the functional independence of individuals who have had a stroke based on cognitive and perceptual impairments would assist with appropriate discharge planning and follow-up resource allocation. The study objective was to investigate the ability of the Developmental Test of Visual Perception - Adolescents and Adults (DTVP-A) and the Neurobehavioural Cognitive Status Exam (Cognistat) to predict the functional performance as measured by the Barthel Index of individuals who have had a stroke. Data was collected using the DTVP-A, Cognistat and the Barthal Index from 32 adults recovering from stroke. Two standard multiple regression models were used to determine predictive variables of the functional independence dependent variable. Both the Cognistat and DTVP-A had a statistically significant ability to predict functional performance (as measured by the Barthel Index) accounting for 64.4% and 27.9% of each regression model, respectively. Two Cognistat subscales (Comprehension [beta = 0.48; p < 0.001)] and Repetition [beta = 0.45; p < 0.004]) and one DTVP-A subscale (Copying [beta = 0.46; p < 0.014]) made statistically significant contributions to the regression models as independent variables. On the basis of the regression model findings, it appears that DTVP-A's Copying and the Cognistat's Comprehension and Repetition subscales are useful in predicting the functional independence (as measured by the Barthel Index) in those individuals who have had a stroke. Given the fundamental importance that cognition and perception has for one's ability to function independently, further investigation is warranted to determine other predictors of functional performance of individuals with a stroke. Copyright © 2012 John Wiley & Sons, Ltd.

Modeling Individual Cyclic Variation in Human Behavior.

PubMed

Pierson, Emma; Althoff, Tim; Leskovec, Jure

2018-04-01

Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyH-MMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets-of human menstrual cycle symptoms and physical activity tracking data-yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.

Modeling Individual Cyclic Variation in Human Behavior

PubMed Central

Pierson, Emma; Althoff, Tim; Leskovec, Jure

2018-01-01

Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyH-MMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets—of human menstrual cycle symptoms and physical activity tracking data—yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model. PMID:29780976

Modeling the residual effects and threshold saturation of training: a case study of Olympic swimmers.

PubMed

Hellard, Philippe; Avalos, Marta; Millet, Gregoire; Lacoste, Lucien; Barale, Frederic; Chatard, Jean-Claude

2005-02-01

The aim of this study was to model the residual effects of training on the swimming performance and to compare a model that includes threshold saturation (MM) with the Banister model (BM). Seven Olympic swimmers were studied over a period of 4 +/- 2 years. For 3 training loads (low-intensity w(LIT), high-intensity w(HIT), and strength training w(ST)), 3 residual training effects were determined: short-term (STE) during the taper phase (i.e., 3 weeks before the performance [weeks 0, 1, and 2]), intermediate-term (ITE) during the intensity phase (weeks 3, 4, and 5), and long-term (LTE) during the volume phase (weeks 6, 7, and 8). ITE and LTE were positive for w(HIT) and w(LIT), respectively (p < 0.05). Low-intensity training load during taper was related to performances by a parabolic relationship (p < 0.05). Different quality measures indicated that MM compares favorably with BM. Identifying individual training thresholds may help individualize the distribution of training loads.

Unlocking the forest inventory data: relating individual tree performance to unmeasured environmental factors

Treesearch

Jeremy W. Lichstein; Jonathan Dushoff; Kiona Ogle; Anping Chen; Drew W. Purves; John P. Caspersen; Stephen W. Pacala

2010-01-01

Geographically extensive forest inventories, such as the USDA Forest Service's Forest Inventory and Analysis (FIA) program, contain millions of individual tree growth and mortality records that could be used to develop broad-scale models of forest dynamics. A limitation of inventory data, however, is that individual-level measurements of light (L) and other...

The Relationship of Learning and Performance Diagnosis at Different System Levels.

ERIC Educational Resources Information Center

Lubega, Khalid

2003-01-01

Examines learning and performance diagnosis, separately and in relation to each other, as they function in organization systems; explains the relationship between learning and performance diagnosis at the individual, process, and organizational levels using a three-level performance model; and discusses types of learning, including nonlearning,…

The Role of Multimodel Combination in Improving Streamflow Prediction

NASA Astrophysics Data System (ADS)

Arumugam, S.; Li, W.

2008-12-01

Model errors are the inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictability. In this study, we present a new approach to combine multiple hydrological models by evaluating their predictability contingent on the predictor state. We combine two hydrological models, 'abcd' model and Variable Infiltration Capacity (VIC) model, with each model's parameter being estimated by two different objective functions to develop multimodel streamflow predictions. The performance of multimodel predictions is compared with individual model predictions using correlation, root mean square error and Nash-Sutcliffe coefficient. To quantify precisely under what conditions the multimodel predictions result in improved predictions, we evaluate the proposed algorithm by testing it against streamflow generated from a known model ('abcd' model or VIC model) with errors being homoscedastic or heteroscedastic. Results from the study show that streamflow simulated from individual models performed better than multimodels under almost no model error. Under increased model error, the multimodel consistently performed better than the single model prediction in terms of all performance measures. The study also evaluates the proposed algorithm for streamflow predictions in two humid river basins from NC as well as in two arid basins from Arizona. Through detailed validation in these four sites, the study shows that multimodel approach better predicts the observed streamflow in comparison to the single model predictions.

Individual differences in social information gathering revealed through Bayesian hierarchical models

PubMed Central

Pearson, John M.; Watson, Karli K.; Klein, Jeffrey T.; Ebitz, R. Becket; Platt, Michael L.

2013-01-01

As studies of the neural circuits underlying choice expand to include more complicated behaviors, analysis of behaviors elicited in laboratory paradigms has grown increasingly difficult. Social behaviors present a particular challenge, since inter- and intra-individual variation are expected to play key roles. However, due to limitations on data collection, studies must often choose between pooling data across all subjects or using individual subjects' data in isolation. Hierarchical models mediate between these two extremes by modeling individual subjects as drawn from a population distribution, allowing the population at large to serve as prior information about individuals' behavior. Here, we apply this method to data collected across multiple experimental sessions from a set of rhesus macaques performing a social information valuation task. We show that, while the values of social images vary markedly between individuals and between experimental sessions for the same individual, individuals also differentially value particular categories of social images. Furthermore, we demonstrate covariance between values for image categories within individuals and find evidence suggesting that magnitudes of stimulus values tend to diminish over time. PMID:24062635

Developing and validating risk prediction models in an individual participant data meta-analysis

PubMed Central

2014-01-01

Background Risk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors). We review how researchers develop and validate risk prediction models within an individual participant data (IPD) meta-analysis, in order to assess the feasibility and conduct of the approach. Methods A qualitative review of the aims, methodology, and reporting in 15 articles that developed a risk prediction model using IPD from multiple studies. Results The IPD approach offers many opportunities but methodological challenges exist, including: unavailability of requested IPD, missing patient data and predictors, and between-study heterogeneity in methods of measurement, outcome definitions and predictor effects. Most articles develop their model using IPD from all available studies and perform only an internal validation (on the same set of data). Ten of the 15 articles did not allow for any study differences in baseline risk (intercepts), potentially limiting their model’s applicability and performance in some populations. Only two articles used external validation (on different data), including a novel method which develops the model on all but one of the IPD studies, tests performance in the excluded study, and repeats by rotating the omitted study. Conclusions An IPD meta-analysis offers unique opportunities for risk prediction research. Researchers can make more of this by allowing separate model intercept terms for each study (population) to improve generalisability, and by using ‘internal-external cross-validation’ to simultaneously develop and validate their model. Methodological challenges can be reduced by prospectively planned collaborations that share IPD for risk prediction. PMID:24397587

Critical research issues in development of biomathematical models of fatigue and performance.

PubMed

Dinges, David F

2004-03-01

This article reviews the scientific research needed to ensure the continued development, validation, and operational transition of biomathematical models of fatigue and performance. These models originated from the need to ascertain the formal underlying relationships among sleep and circadian dynamics in the control of alertness and neurobehavioral performance capability. Priority should be given to research that further establishes their basic validity, including the accuracy of the core mathematical formulae and parameters that instantiate the interactions of sleep/wake and circadian processes. Since individuals can differ markedly and reliably in their responses to sleep loss and to countermeasures for it, models must incorporate estimates of these inter-individual differences, and research should identify predictors of them. To ensure models accurately predict recovery of function with sleep of varying durations, dose-response curves for recovery of performance as a function of prior sleep homeostatic load and the number of days of recovery are needed. It is also necessary to establish whether the accuracy of models is affected by using work/rest schedules as surrogates for sleep/wake inputs to models. Given the importance of light as both a circadian entraining agent and an alerting agent, research should determine the extent to which light input could incrementally improve model predictions of performance, especially in persons exposed to night work, jet lag, and prolonged work. Models seek to estimate behavioral capability and/or the relative risk of adverse events in a fatigued state. Research is needed on how best to scale and interpret metrics of behavioral capability, and incorporate factors that amplify or diminish the relationship between model predictions of performance and risk outcomes.

[On the relation between encounter rate and population density: Are classical models of population dynamics justified?].

PubMed

Nedorezov, L V

2015-01-01

A stochastic model of migrations on a lattice and with discrete time is considered. It is assumed that space is homogenous with respect to its properties and during one time step every individual (independently of local population numbers) can migrate to nearest nodes of lattice with equal probabilities. It is also assumed that population size remains constant during certain time interval of computer experiments. The following variants of estimation of encounter rate between individuals are considered: when for the fixed time moments every individual in every node of lattice interacts with all other individuals in the node; when individuals can stay in nodes independently, or can be involved in groups in two, three or four individuals. For each variant of interactions between individuals, average value (with respect to space and time) is computed for various values of population size. The samples obtained were compared with respective functions of classic models of isolated population dynamics: Verhulst model, Gompertz model, Svirezhev model, and theta-logistic model. Parameters of functions were calculated with least square method. Analyses of deviations were performed using Kolmogorov-Smirnov test, Lilliefors test, Shapiro-Wilk test, and other statistical tests. It is shown that from traditional point of view there are no correspondence between the encounter rate and functions describing effects of self-regulatory mechanisms on population dynamics. Best fitting of samples was obtained with Verhulst and theta-logistic models when using the dataset resulted from the situation when every individual in the node interacts with all other individuals.

Untangling Performance from Success

NASA Astrophysics Data System (ADS)

Yucesoy, Burcu; Barabasi, Albert-Laszlo

Fame, popularity and celebrity status, frequently used tokens of success, are often loosely related to, or even divorced from professional performance. This dichotomy is partly rooted in the difficulty to distinguish performance, an individual measure that captures the actions of a performer, from success, a collective measure that captures a community's reactions to these actions. Yet, finding the relationship between the two measures is essential for all areas that aim to objectively reward excellence, from science to business. Here we quantify the relationship between performance and success by focusing on tennis, an individual sport where the two quantities can be independently measured. We show that a predictive model, relying only on a tennis player's performance in tournaments, can accurately predict an athlete's popularity, both during a player's active years and after retirement. Hence the model establishes a direct link between performance and momentary popularity. The agreement between the performance-driven and observed popularity suggests that in most areas of human achievement exceptional visibility may be rooted in detectable performance measures. This research was supported by Air Force Office of Scientific Research (AFOSR) under agreement FA9550-15-1-0077.

Improving Cover-Letter Writing Skills of Individuals with Intellectual Disabilities

ERIC Educational Resources Information Center

Pennington, Robert; Delano, Monica; Scott, Renee

2014-01-01

We evaluated a multicomponent intervention for improving the cover-letter writing skills of individuals with intellectual disabilities. An intervention that included modeling, self-monitoring, prompting, and feedback increased correct performance for all participants. In addition, the skill was demonstrated across audiences.

A Primer on the Statistical Modelling of Learning Curves in Health Professions Education

ERIC Educational Resources Information Center

Pusic, Martin V.; Boutis, Kathy; Pecaric, Martin R.; Savenkov, Oleksander; Beckstead, Jason W.; Jaber, Mohamad Y.

2017-01-01

Learning curves are a useful way of representing the rate of learning over time. Features include an index of baseline performance (y-intercept), the efficiency of learning over time (slope parameter) and the maximal theoretical performance achievable (upper asymptote). Each of these parameters can be statistically modelled on an individual and…

A Human Resource Development Performance Improvement Model for Workers with Mental Retardation in Supported Employment

ERIC Educational Resources Information Center

Fornes, Sandra; Rosenberg, Howard; Rocco, Tonette S.; Gallagher, Jo

2006-01-01

This literature review discusses the factors for successful job retention of adult workers with mental retardation (MR) including external factors related to work environments and internal issues of the individual worker. Through the synthesis of the literature, a performance improvement model for supported employment (SE) is discussed based on…

Quantum Computation Using Optically Coupled Quantum Dot Arrays

NASA Technical Reports Server (NTRS)

Pradhan, Prabhakar; Anantram, M. P.; Wang, K. L.; Roychowhury, V. P.; Saini, Subhash (Technical Monitor)

1998-01-01

A solid state model for quantum computation has potential advantages in terms of the ease of fabrication, characterization, and integration. The fundamental requirements for a quantum computer involve the realization of basic processing units (qubits), and a scheme for controlled switching and coupling among the qubits, which enables one to perform controlled operations on qubits. We propose a model for quantum computation based on optically coupled quantum dot arrays, which is computationally similar to the atomic model proposed by Cirac and Zoller. In this model, individual qubits are comprised of two coupled quantum dots, and an array of these basic units is placed in an optical cavity. Switching among the states of the individual units is done by controlled laser pulses via near field interaction using the NSOM technology. Controlled rotations involving two or more qubits are performed via common cavity mode photon. We have calculated critical times, including the spontaneous emission and switching times, and show that they are comparable to the best times projected for other proposed models of quantum computation. We have also shown the feasibility of accessing individual quantum dots using the NSOM technology by calculating the photon density at the tip, and estimating the power necessary to perform the basic controlled operations. We are currently in the process of estimating the decoherence times for this system; however, we have formulated initial arguments which seem to indicate that the decoherence times will be comparable, if not longer, than many other proposed models.

Development of a Web-Accessible Population Pharmacokinetic Service—Hemophilia (WAPPS-Hemo): Study Protocol

PubMed Central

Foster, Gary; Navarro-Ruan, Tamara; McEneny-King, Alanna; Edginton, Andrea N; Thabane, Lehana

2016-01-01

Background Individual pharmacokinetic assessment is a critical component of tailored prophylaxis for hemophilia patients. Population pharmacokinetics allows using individual sparse data, thus simplifying individual pharmacokinetic studies. Implementing population pharmacokinetics capacity for the hemophilia community is beyond individual reach and requires a system effort. Objective The Web-Accessible Population Pharmacokinetic Service—Hemophilia (WAPPS-Hemo) project aims to assemble a database of patient pharmacokinetic data for all existing factor concentrates, develop and validate population pharmacokinetics models, and integrate these models within a Web-based calculator for individualized pharmacokinetic estimation in patients at participating treatment centers. Methods Individual pharmacokinetic studies on factor VIII and IX concentrates will be sourced from pharmaceutical companies and independent investigators. All factor concentrate manufacturers, hemophilia treatment centers (HTCs), and independent investigators (identified via a systematic review of the literature) having on file pharmacokinetic data and willing to contribute full or sparse pharmacokinetic data will be eligible for participation. Multicompartmental modeling will be performed using a mixed-model approach for derivation and Bayesian forecasting for estimation of individual sparse data. NONMEM (ICON Development Solutions) will be used as modeling software. Results The WAPPS-Hemo research network has been launched and is currently joined by 30 HTCs from across the world. We have gathered dense individual pharmacokinetic data on 878 subjects, including several replicates, on 21 different molecules from 17 different sources. We have collected sparse individual pharmacokinetic data on 289 subjects from the participating centers through the testing phase of the WAPPS-Hemo Web interface. We have developed prototypal population pharmacokinetics models for 11 molecules. The WAPPS-Hemo website (available at www.wapps-hemo.org, version 2.4), with core functionalities allowing hemophilia treaters to obtain individual pharmacokinetic estimates on sparse data points after 1 or more infusions of a factor concentrate, was launched for use within the research network in July 2015. Conclusions The WAPPS-Hemo project and research network aims to make it easier to perform individual pharmacokinetic assessments on a reduced number of plasma samples by adoption of a population pharmacokinetics approach. The project will also gather data to substantially enhance the current knowledge about factor concentrate pharmacokinetics and sources of its variability in target populations. Trial Registration ClinicalTrials.gov NCT02061072; https://clinicaltrials.gov/ct2/show/NCT02061072 (Archived by WebCite at http://www.webcitation.org/6mRK9bKP6) PMID:27977390

Prediction of Biological Motion Perception Performance from Intrinsic Brain Network Regional Efficiency

PubMed Central

Wang, Zengjian; Zhang, Delong; Liang, Bishan; Chang, Song; Pan, Jinghua; Huang, Ruiwang; Liu, Ming

2016-01-01

Biological motion perception (BMP) refers to the ability to perceive the moving form of a human figure from a limited amount of stimuli, such as from a few point lights located on the joints of a moving body. BMP is commonplace and important, but there is great inter-individual variability in this ability. This study used multiple regression model analysis to explore the association between BMP performance and intrinsic brain activity, in order to investigate the neural substrates underlying inter-individual variability of BMP performance. The resting-state functional magnetic resonance imaging (rs-fMRI) and BMP performance data were collected from 24 healthy participants, for whom intrinsic brain networks were constructed, and a graph-based network efficiency metric was measured. Then, a multiple linear regression model was used to explore the association between network regional efficiency and BMP performance. We found that the local and global network efficiency of many regions was significantly correlated with BMP performance. Further analysis showed that the local efficiency rather than global efficiency could be used to explain most of the BMP inter-individual variability, and the regions involved were predominately located in the Default Mode Network (DMN). Additionally, discrimination analysis showed that the local efficiency of certain regions such as the thalamus could be used to classify BMP performance across participants. Notably, the association pattern between network nodal efficiency and BMP was different from the association pattern of static directional/gender information perception. Overall, these findings show that intrinsic brain network efficiency may be considered a neural factor that explains BMP inter-individual variability. PMID:27853427

Individual-based modelling of population growth and diffusion in discrete time.

PubMed

Tkachenko, Natalie; Weissmann, John D; Petersen, Wesley P; Lake, George; Zollikofer, Christoph P E; Callegari, Simone

2017-01-01

Individual-based models (IBMs) of human populations capture spatio-temporal dynamics using rules that govern the birth, behavior, and death of individuals. We explore a stochastic IBM of logistic growth-diffusion with constant time steps and independent, simultaneous actions of birth, death, and movement that approaches the Fisher-Kolmogorov model in the continuum limit. This model is well-suited to parallelization on high-performance computers. We explore its emergent properties with analytical approximations and numerical simulations in parameter ranges relevant to human population dynamics and ecology, and reproduce continuous-time results in the limit of small transition probabilities. Our model prediction indicates that the population density and dispersal speed are affected by fluctuations in the number of individuals. The discrete-time model displays novel properties owing to the binomial character of the fluctuations: in certain regimes of the growth model, a decrease in time step size drives the system away from the continuum limit. These effects are especially important at local population sizes of <50 individuals, which largely correspond to group sizes of hunter-gatherers. As an application scenario, we model the late Pleistocene dispersal of Homo sapiens into the Americas, and discuss the agreement of model-based estimates of first-arrival dates with archaeological dates in dependence of IBM model parameter settings.

How do leader-member exchange quality and differentiation affect performance in teams? An integrated multilevel dual process model.

PubMed

Li, Alex Ning; Liao, Hui

2014-09-01

Integrating leader-member exchange (LMX) research with role engagement theory (Kahn, 1990) and role system theory (Katz & Kahn, 1978), we propose a multilevel, dual process model to understand the mechanisms through which LMX quality at the individual level and LMX differentiation at the team level simultaneously affect individual and team performance. With regard to LMX differentiation, we introduce a new configural approach focusing on the pattern of LMX differentiation to complement the traditional approach focusing on the degree of LMX differentiation. Results based on multiphase, multisource data from 375 employees of 82 teams revealed that, at the individual level, LMX quality positively contributed to customer-rated employee performance through enhancing employee role engagement. At the team level, LMX differentiation exerted negative influence on teams' financial performance through disrupting team coordination. In particular, teams with the bimodal form of LMX configuration (i.e., teams that split into 2 LMX-based subgroups with comparable size) suffered most in team performance because they experienced greatest difficulty in coordinating members' activities. Furthermore, LMX differentiation strengthened the relationship between LMX quality and role engagement, and team coordination strengthened the relationship between role engagement and employee performance. Theoretical and practical implications of the findings are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Verbal and visual-spatial working memory and mathematical ability in different domains throughout primary school.

PubMed

Van de Weijer-Bergsma, Eva; Kroesbergen, Evelyn H; Van Luit, Johannes E H

2015-04-01

The relative importance of visual-spatial and verbal working memory for mathematics performance and learning seems to vary with age, the novelty of the material, and the specific math domain that is investigated. In this study, the relations between verbal and visual-spatial working memory and performance in four math domains (i.e., addition, subtraction, multiplication, and division) at different ages during primary school are investigated. Children (N = 4337) from grades 2 through 6 participated. Visual-spatial and verbal working memory were assessed using online computerized tasks. Math performance was assessed at the start, middle, and end of the school year using a speeded arithmetic test. Multilevel Multigroup Latent Growth Modeling was used to model individual differences in level and growth in math performance, and examine the predictive value of working memory per grade, while controlling for effects of classroom membership. The results showed that as grade level progressed, the predictive value of visual-spatial working memory for individual differences in level of mathematics performance waned, while the predictive value of verbal working memory increased. Working memory did not predict individual differences between children in their rate of performance growth throughout the school year. These findings are discussed in relation to three, not mutually exclusive, explanations for such age-related findings.

Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR

NASA Astrophysics Data System (ADS)

Kukunda, Collins B.; Duque-Lazo, Joaquín; González-Ferreiro, Eduardo; Thaden, Hauke; Kleinn, Christoph

2018-03-01

Distinguishing tree species is relevant in many contexts of remote sensing assisted forest inventory. Accurate tree species maps support management and conservation planning, pest and disease control and biomass estimation. This study evaluated the performance of applying ensemble techniques with the goal of automatically distinguishing Pinus sylvestris L. and Pinus uncinata Mill. Ex Mirb within a 1.3 km2 mountainous area in Barcelonnette (France). Three modelling schemes were examined, based on: (1) high-density LiDAR data (160 returns m-2), (2) Worldview-2 multispectral imagery, and (3) Worldview-2 and LiDAR in combination. Variables related to the crown structure and height of individual trees were extracted from the normalized LiDAR point cloud at individual-tree level, after performing individual tree crown (ITC) delineation. Vegetation indices and the Haralick texture indices were derived from Worldview-2 images and served as independent spectral variables. Selection of the best predictor subset was done after a comparison of three variable selection procedures: (1) Random Forests with cross validation (AUCRFcv), (2) Akaike Information Criterion (AIC) and (3) Bayesian Information Criterion (BIC). To classify the species, 9 regression techniques were combined using ensemble models. Predictions were evaluated using cross validation and an independent dataset. Integration of datasets and models improved individual tree species classification (True Skills Statistic, TSS; from 0.67 to 0.81) over individual techniques and maintained strong predictive power (Relative Operating Characteristic, ROC = 0.91). Assemblage of regression models and integration of the datasets provided more reliable species distribution maps and associated tree-scale mapping uncertainties. Our study highlights the potential of model and data assemblage at improving species classifications needed in present-day forest planning and management.

Common carotid intima-media thickness does not add to Framingham risk score in individuals with diabetes mellitus: the USE-IMT initiative.

PubMed

den Ruijter, H M; Peters, S A E; Groenewegen, K A; Anderson, T J; Britton, A R; Dekker, J M; Engström, G; Eijkemans, M J; Evans, G W; de Graaf, J; Grobbee, D E; Hedblad, B; Hofman, A; Holewijn, S; Ikeda, A; Kavousi, M; Kitagawa, K; Kitamura, A; Koffijberg, H; Ikram, M A; Lonn, E M; Lorenz, M W; Mathiesen, E B; Nijpels, G; Okazaki, S; O'Leary, D H; Polak, J F; Price, J F; Robertson, C; Rembold, C M; Rosvall, M; Rundek, T; Salonen, J T; Sitzer, M; Stehouwer, C D A; Witteman, J C; Moons, K G; Bots, M L

2013-07-01

The aim of this work was to investigate whether measurement of the mean common carotid intima-media thickness (CIMT) improves cardiovascular risk prediction in individuals with diabetes. We performed a subanalysis among 4,220 individuals with diabetes in a large ongoing individual participant data meta-analysis involving 56,194 subjects from 17 population-based cohorts worldwide. We first refitted the risk factors of the Framingham heart risk score on the individuals without previous cardiovascular disease (baseline model) and then expanded this model with the mean common CIMT (CIMT model). The absolute 10 year risk for developing a myocardial infarction or stroke was estimated from both models. In individuals with diabetes we compared discrimination and calibration of the two models. Reclassification of individuals with diabetes was based on allocation to another cardiovascular risk category when mean common CIMT was added. During a median follow-up of 8.7 years, 684 first-time cardiovascular events occurred among the population with diabetes. The C statistic was 0.67 for the Framingham model and 0.68 for the CIMT model. The absolute 10 year risk for developing a myocardial infarction or stroke was 16% in both models. There was no net reclassification improvement with the addition of mean common CIMT (1.7%; 95% CI -1.8, 3.8). There were no differences in the results between men and women. There is no improvement in risk prediction in individuals with diabetes when measurement of the mean common CIMT is added to the Framingham risk score. Therefore, this measurement is not recommended for improving individual cardiovascular risk stratification in individuals with diabetes.

Developing a theory of the strategic core of teams: a role composition model of team performance.

PubMed

Humphrey, Stephen E; Morgeson, Frederick P; Mannor, Michael J

2009-01-01

Although numerous models of team performance have been articulated over the past 20 years, these models have primarily focused on the individual attribute approach to team composition. The authors utilized a role composition approach, which investigates how the characteristics of a set of role holders impact team effectiveness, to develop a theory of the strategic core of teams. Their theory suggests that certain team roles are most important for team performance and that the characteristics of the role holders in the "core" of the team are more important for overall team performance. This theory was tested in 778 teams drawn from 29 years of major league baseball (1974'-2002). Results demonstrate that although high levels of experience and job-related skill are important predictors of team performance, the relationships between these constructs and team performance are significantly stronger when the characteristics are possessed by core role holders (as opposed to non-core role holders). Further, teams that invest more of their financial resources in these core roles are able to leverage such investments into significantly improved performance. These results have implications for team composition models, as they suggest a new method for considering individual contributions to a team's success that shifts the focus onto core roles. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

High pressures in room evacuation processes and a first approach to the dynamics around unconscious pedestrians

NASA Astrophysics Data System (ADS)

Cornes, F. E.; Frank, G. A.; Dorso, C. O.

2017-10-01

Clogging raises as the principal phenomenon during many evacuation processes of pedestrians in an emergency situation. As people push to escape from danger, compression forces may increase to harming levels. Many individuals might fall down, while others will try to dodge the fallen people, or, simply pass through them. We studied the dynamics of the crowd for these situations, in the context of the "social force model". We modeled the unconscious (fallen) pedestrians as inanimate bodies that can be dodged (or not) by the surrounding individuals. We found that new morphological structures appear along the evacuating crowd. Under specific conditions, these structures may enhance the evacuation performance. The pedestrian's willings for either dodging or passing through the unconscious individuals play a relevant role in the overall evacuation performance.

Ecological modelling and toxicity data coupled to assess population recovery of marine amphipod Gammarus locusta: Application to disturbance by chronic exposure to aniline.

PubMed

de los Santos, Carmen B; Neuparth, Teresa; Torres, Tiago; Martins, Irene; Cunha, Isabel; Sheahan, Dave; McGowan, Tom; Santos, Miguel M

2015-06-01

A population agent-based model of marine amphipod Gammarus locusta was designed and implemented as a basis for ecological risk assessment of chemical pollutants impairing life-history traits at the individual level. We further used the model to assess the toxic effects of aniline (a priority hazardous and noxious substance, HNS) on amphipod populations using empirically-built dose-response functions derived from a chronic bioassay that we previously performed with this species. We observed a significant toxicant-induced mortality and adverse effects in reproductive performance (reduction of newborn production) in G. locusta at the individual level. Coupling the population model with the toxicological data from the chronic bioassay allowed the projection of the ecological costs associated with exposure to aniline that might occur in wild populations. Model simulations with different scenarios indicated that even low level prolonged exposure to the HNS aniline can have significant long-term impacts on G. locusta population abundance, until the impacted population returns to undisturbed levels. This approach may be a useful complement in ecotoxicological studies of chemical pollution to transfer individual-collected data to ecological-relevant levels. Copyright © 2015 Elsevier B.V. All rights reserved.

Modeling individualized coefficient alpha to measure quality of test score data.

PubMed

Liu, Molei; Hu, Ming; Zhou, Xiao-Hua

2018-05-23

Individualized coefficient alpha is defined. It is item and subject specific and is used to measure the quality of test score data with heterogenicity among the subjects and items. A regression model is developed based on 3 sets of generalized estimating equations. The first set of generalized estimating equation models the expectation of the responses, the second set models the response's variance, and the third set is proposed to estimate the individualized coefficient alpha, defined and used to measure individualized internal consistency of the responses. We also use different techniques to extend our method to handle missing data. Asymptotic property of the estimators is discussed, based on which inference on the coefficient alpha is derived. Performance of our method is evaluated through simulation study and real data analysis. The real data application is from a health literacy study in Hunan province of China. Copyright © 2018 John Wiley & Sons, Ltd.

Linear mixed-effects models to describe individual tree crown width for China-fir in Fujian Province, southeast China.

PubMed

Hao, Xu; Yujun, Sun; Xinjie, Wang; Jin, Wang; Yao, Fu

2015-01-01

A multiple linear model was developed for individual tree crown width of Cunninghamia lanceolata (Lamb.) Hook in Fujian province, southeast China. Data were obtained from 55 sample plots of pure China-fir plantation stands. An Ordinary Linear Least Squares (OLS) regression was used to establish the crown width model. To adjust for correlations between observations from the same sample plots, we developed one level linear mixed-effects (LME) models based on the multiple linear model, which take into account the random effects of plots. The best random effects combinations for the LME models were determined by the Akaike's information criterion, the Bayesian information criterion and the -2logarithm likelihood. Heteroscedasticity was reduced by three residual variance functions: the power function, the exponential function and the constant plus power function. The spatial correlation was modeled by three correlation structures: the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)], and the compound symmetry structure (CS). Then, the LME model was compared to the multiple linear model using the absolute mean residual (AMR), the root mean square error (RMSE), and the adjusted coefficient of determination (adj-R2). For individual tree crown width models, the one level LME model showed the best performance. An independent dataset was used to test the performance of the models and to demonstrate the advantage of calibrating LME models.

Predicting Student Performance in a Collaborative Learning Environment

ERIC Educational Resources Information Center

Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol

2015-01-01

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

System Models and Aging: A Driving Example.

ERIC Educational Resources Information Center

Melichar, Joseph F.

Chronological age is a marker in time but it fails to measure accurately the performance or behavioral characteristics of individuals. This paper models the complexity of aging by using a system model and a human function paradigm. These models help facilitate representation of older adults, integrate research agendas, and enhance remediative…

Recruiting and Retention of Military Personnel: Influences of Quality of Life and Personnel Tempo

DTIC Science & Technology

2009-10-01

with Children Relations with Relatives Neighbourhood Economic Cycle External Labour Market Enlistment Propensity Anticipated QoL Attraction Personal...dispositions Actual QoL (meaning) Individual factors Retention Absenteeism Individual Performance Dowden (2000) QoL Model of Married Marines with

Predicting population dynamics from the properties of individuals: a cross-level test of dynamic energy budget theory.

PubMed

Martin, Benjamin T; Jager, Tjalling; Nisbet, Roger M; Preuss, Thomas G; Grimm, Volker

2013-04-01

Individual-based models (IBMs) are increasingly used to link the dynamics of individuals to higher levels of biological organization. Still, many IBMs are data hungry, species specific, and time-consuming to develop and analyze. Many of these issues would be resolved by using general theories of individual dynamics as the basis for IBMs. While such theories have frequently been examined at the individual level, few cross-level tests exist that also try to predict population dynamics. Here we performed a cross-level test of dynamic energy budget (DEB) theory by parameterizing an individual-based model using individual-level data of the water flea, Daphnia magna, and comparing the emerging population dynamics to independent data from population experiments. We found that DEB theory successfully predicted population growth rates and peak densities but failed to capture the decline phase. Further assumptions on food-dependent mortality of juveniles were needed to capture the population dynamics after the initial population peak. The resulting model then predicted, without further calibration, characteristic switches between small- and large-amplitude cycles, which have been observed for Daphnia. We conclude that cross-level tests help detect gaps in current individual-level theories and ultimately will lead to theory development and the establishment of a generic basis for individual-based models and ecology.

Evaluating carbon fluxes of global forest ecosystems by using an individual tree-based model FORCCHN.

PubMed

Ma, Jianyong; Shugart, Herman H; Yan, Xiaodong; Cao, Cougui; Wu, Shuang; Fang, Jing

2017-05-15

The carbon budget of forest ecosystems, an important component of the terrestrial carbon cycle, needs to be accurately quantified and predicted by ecological models. As a preamble to apply the model to estimate global carbon uptake by forest ecosystems, we used the CO 2 flux measurements from 37 forest eddy-covariance sites to examine the individual tree-based FORCCHN model's performance globally. In these initial tests, the FORCCHN model simulated gross primary production (GPP), ecosystem respiration (ER) and net ecosystem production (NEP) with correlations of 0.72, 0.70 and 0.53, respectively, across all forest biomes. The model underestimated GPP and slightly overestimated ER across most of the eddy-covariance sites. An underestimation of NEP arose primarily from the lower GPP estimates. Model performance was better in capturing both the temporal changes and magnitude of carbon fluxes in deciduous broadleaf forest than in evergreen broadleaf forest, and it performed less well for sites in Mediterranean climate. We then applied the model to estimate the carbon fluxes of forest ecosystems on global scale over 1982-2011. This application of FORCCHN gave a total GPP of 59.41±5.67 and an ER of 57.21±5.32PgCyr -1 for global forest ecosystems during 1982-2011. The forest ecosystems over this same period contributed a large carbon storage, with total NEP being 2.20±0.64PgCyr -1 . These values are comparable to and reinforce estimates reported in other studies. This analysis highlights individual tree-based model FORCCHN could be used to evaluate carbon fluxes of forest ecosystems on global scale. Copyright © 2017 Elsevier B.V. All rights reserved.

Which risk models perform best in selecting ever-smokers for lung cancer screening?

Cancer.gov

A new analysis by scientists at NCI evaluates nine different individualized lung cancer risk prediction models based on their selections of ever-smokers for computed tomography (CT) lung cancer screening.

Understanding protocol performance: impact of test performance.

PubMed

Turner, Robert G

2013-01-01

This is the second of two articles that examine the factors that determine protocol performance. The objective of these articles is to provide a general understanding of protocol performance that can be used to estimate performance, establish limits on performance, decide if a protocol is justified, and ultimately select a protocol. The first article was concerned with protocol criterion and test correlation. It demonstrated the advantages and disadvantages of different criterion when all tests had the same performance. It also examined the impact of increasing test correlation on protocol performance and the characteristics of the different criteria. To examine the impact on protocol performance when individual tests in a protocol have different performance. This is evaluated for different criteria and test correlations. The results of the two articles are combined and summarized. A mathematical model is used to calculate protocol performance for different protocol criteria and test correlations when there are small to large variations in the performance of individual tests in the protocol. The performance of the individual tests that make up a protocol has a significant impact on the performance of the protocol. As expected, the better the performance of the individual tests, the better the performance of the protocol. Many of the characteristics of the different criteria are relatively independent of the variation in the performance of the individual tests. However, increasing test variation degrades some criteria advantages and causes a new disadvantage to appear. This negative impact increases as test variation increases and as more tests are added to the protocol. Best protocol performance is obtained when individual tests are uncorrelated and have the same performance. In general, the greater the variation in the performance of tests in the protocol, the more detrimental this variation is to protocol performance. Since this negative impact is increased as more tests are added to the protocol, greater test variation indicates using fewer tests in the protocol. American Academy of Audiology.

Why Are Some More Peer Than Others? Evidence from a Longitudinal Study of Social Networks and Individual Academic Performance

PubMed Central

Lomi, Alessandro; Snijders, Tom A.B.; Steglich, Christian E.G.; Torlo, Vanina Jasmine

2014-01-01

Studies of peer effects in educational settings confront two main problems. The first is the presence of endogenous sorting which confounds the effects of social influence and social selection on individual attainment. The second is how to account for the local network dependencies through which peer effects influence individual behavior. We empirically address these problems using longitudinal data on academic performance, friendship, and advice seeking relations among students in a full-time graduate academic program. We specify stochastic agent-based models that permit estimation of the interdependent contribution of social selection and social influence to individual performance. We report evidence of peer effects. Students tend to assimilate the average performance of their friends and of their advisors. At the same time, students attaining similar levels of academic performance are more likely to develop friendship and advice ties. Together, these results imply that processes of social influence and social selection are sub-components of a more general a co-evolutionary process linking network structure and individual behavior. We discuss possible points of contact between our findings and current research in the economics and sociology of education. PMID:25641999

Why Are Some More Peer Than Others? Evidence from a Longitudinal Study of Social Networks and Individual Academic Performance.

PubMed

Lomi, Alessandro; Snijders, Tom A B; Steglich, Christian E G; Torlo, Vanina Jasmine

2011-11-01

Studies of peer effects in educational settings confront two main problems. The first is the presence of endogenous sorting which confounds the effects of social influence and social selection on individual attainment. The second is how to account for the local network dependencies through which peer effects influence individual behavior. We empirically address these problems using longitudinal data on academic performance, friendship, and advice seeking relations among students in a full-time graduate academic program. We specify stochastic agent-based models that permit estimation of the interdependent contribution of social selection and social influence to individual performance. We report evidence of peer effects. Students tend to assimilate the average performance of their friends and of their advisors. At the same time, students attaining similar levels of academic performance are more likely to develop friendship and advice ties. Together, these results imply that processes of social influence and social selection are sub-components of a more general a co-evolutionary process linking network structure and individual behavior. We discuss possible points of contact between our findings and current research in the economics and sociology of education.

Genetic determinants of freckle occurrence in the Spanish population: Towards ephelides prediction from human DNA samples.

PubMed

Hernando, Barbara; Ibañez, Maria Victoria; Deserio-Cuesta, Julio Alberto; Soria-Navarro, Raquel; Vilar-Sastre, Inca; Martinez-Cadenas, Conrado

2018-03-01

Prediction of human pigmentation traits, one of the most differentiable externally visible characteristics among individuals, from biological samples represents a useful tool in the field of forensic DNA phenotyping. In spite of freckling being a relatively common pigmentation characteristic in Europeans, little is known about the genetic basis of this largely genetically determined phenotype in southern European populations. In this work, we explored the predictive capacity of eight freckle and sunlight sensitivity-related genes in 458 individuals (266 non-freckled controls and 192 freckled cases) from Spain. Four loci were associated with freckling (MC1R, IRF4, ASIP and BNC2), and female sex was also found to be a predictive factor for having a freckling phenotype in our population. After identifying the most informative genetic variants responsible for human ephelides occurrence in our sample set, we developed a DNA-based freckle prediction model using a multivariate regression approach. Once developed, the capabilities of the prediction model were tested by a repeated 10-fold cross-validation approach. The proportion of correctly predicted individuals using the DNA-based freckle prediction model was 74.13%. The implementation of sex into the DNA-based freckle prediction model slightly improved the overall prediction accuracy by 2.19% (76.32%). Further evaluation of the newly-generated prediction model was performed by assessing the model's performance in a new cohort of 212 Spanish individuals, reaching a classification success rate of 74.61%. Validation of this prediction model may be carried out in larger populations, including samples from different European populations. Further research to validate and improve this newly-generated freckle prediction model will be needed before its forensic application. Together with DNA tests already validated for eye and hair colour prediction, this freckle prediction model may lead to a substantially more detailed physical description of unknown individuals from DNA found at the crime scene. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatial modeling of personalized exposure dynamics: the case of pesticide use in small-scale agricultural production landscapes of the developing world.

PubMed

Leyk, Stefan; Binder, Claudia R; Nuckols, John R

2009-03-30

Pesticide poisoning is a global health issue with the largest impacts in the developing countries where residential and small-scale agricultural areas are often integrated and pesticides sprayed manually. To reduce health risks from pesticide exposure approaches for personalized exposure assessment (PEA) are needed. We present a conceptual framework to develop a spatial individual-based model (IBM) prototype for assessing potential exposure of farm-workers conducting small-scale agricultural production, which accounts for a considerable portion of global food crop production. Our approach accounts for dynamics in the contaminant distributions in the environment, as well as patterns of movement and activities performed on an individual level under different safety scenarios. We demonstrate a first prototype using data from a study area in a rural part of Colombia, South America. Different safety scenarios of PEA were run by including weighting schemes for activities performed under different safety conditions. We examined the sensitivity of individual exposure estimates to varying patterns of pesticide application and varying individual patterns of movement. This resulted in a considerable variation in estimates of magnitude, frequency and duration of exposure over the model runs for each individual as well as between individuals. These findings indicate the influence of patterns of pesticide application, individual spatial patterns of movement as well as safety conditions on personalized exposure in the agricultural production landscape that is the focus of our research. This approach represents a conceptual framework for developing individual based models to carry out PEA in small-scale agricultural settings in the developing world based on individual patterns of movement, safety conditions, and dynamic contaminant distributions. The results of our analysis indicate our prototype model is sufficiently sensitive to differentiate and quantify the influence of individual patterns of movement and decision-based pesticide management activities on potential exposure. This approach represents a framework for further understanding the contribution of agricultural pesticide use to exposure in the small-scale agricultural production landscape of many developing countries, and could be useful to evaluate public health intervention strategies to reduce risks to farm-workers and their families. Further research is needed to fully develop an operational version of the model.

Learning about individuals' health from aggregate data.

PubMed

Colbaugh, Rich; Glass, Kristin

2017-07-01

There is growing awareness that user-generated social media content contains valuable health-related information and is more convenient to collect than typical health data. For example, Twitter has been employed to predict aggregate-level outcomes, such as regional rates of diabetes and child poverty, and to identify individual cases of depression and food poisoning. Models which make aggregate-level inferences can be induced from aggregate data, and consequently are straightforward to build. In contrast, learning models that produce individual-level (IL) predictions, which are more informative, usually requires a large number of difficult-to-acquire labeled IL examples. This paper presents a new machine learning method which achieves the best of both worlds, enabling IL models to be learned from aggregate labels. The algorithm makes predictions by combining unsupervised feature extraction, aggregate-based modeling, and optimal integration of aggregate-level and IL information. Two case studies illustrate how to learn health-relevant IL prediction models using only aggregate labels, and show that these models perform as well as state-of-the-art models trained on hundreds or thousands of labeled individuals.

Adjudicating between face-coding models with individual-face fMRI responses

PubMed Central

Kriegeskorte, Nikolaus

2017-01-01

The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli. In the fusiform face area, a face-space coding model with sigmoidal ramp tuning provided a better account of the data than one based on exemplar tuning. However, an image-processing model with weighted banks of Gabor filters performed similarly. Accounting for the data required the inclusion of a measurement-level population averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings. Our study demonstrates the importance of comparing multiple models and of modeling the measurement process in computational neuroimaging. PMID:28746335

Implications of Self-Deception for Self-Reported Intrinsic and Extrinsic Motivational Dispositions and Actual Learning Performance: A Higher Order Structural Model

ERIC Educational Resources Information Center

Hirschfeld, Robert R.; Thomas, Christopher H.; McNatt, D. Brian

2008-01-01

The authors explored implications of individuals' self-deception (a trait) for their self-reported intrinsic and extrinsic motivational dispositions and their actual learning performance. In doing so, a higher order structural model was developed and tested in which intrinsic and extrinsic motivational dispositions were underlying factors that…

Modeling the distribution of colonial species to improve estimation of plankton concentration in ballast water

NASA Astrophysics Data System (ADS)

Rajakaruna, Harshana; VandenByllaardt, Julie; Kydd, Jocelyn; Bailey, Sarah

2018-03-01

The International Maritime Organization (IMO) has set limits on allowable plankton concentrations in ballast water discharge to minimize aquatic invasions globally. Previous guidance on ballast water sampling and compliance decision thresholds was based on the assumption that probability distributions of plankton are Poisson when spatially homogenous, or negative binomial when heterogeneous. We propose a hierarchical probability model, which incorporates distributions at the level of particles (i.e., discrete individuals plus colonies per unit volume) and also within particles (i.e., individuals per particle) to estimate the average plankton concentration in ballast water. We examined the performance of the models using data for plankton in the size class ≥ 10 μm and < 50 μm, collected from five different depths of a ballast tank of a commercial ship in three independent surveys. We show that the data fit to the negative binomial and the hierarchical probability models equally well, with both models performing better than the Poisson model at the scale of our sampling. The hierarchical probability model, which accounts for both the individuals and the colonies in a sample, reduces the uncertainty associated with the concentration estimation, and improves the power of rejecting the decision on ship's compliance when a ship does not truly comply with the standard. We show examples of how to test ballast water compliance using the above models.

Probe colorimeter for quantitating enzyme-linked immunosorbent assays and other colorimetric assays performed with microplates.

PubMed Central

Ackerman, S B; Kelley, E A

1983-01-01

The performance of a fiberoptic probe colorimeter (model PC800; Brinkmann Instruments, Inc., Westbury, N.Y.) for quantitating enzymatic or colorimetric assays in 96-well microtiter plates was compared with the performances of a spectrophotometer (model 240; Gilford Instrument Laboratories, Inc., Oberlin, Ohio) and a commercially available enzyme immunoassay reader (model MR590; Dynatech Laboratories, Inc., Alexandria, Va.). Alkaline phosphatase-p-nitrophenyl phosphate in 3 M NaOH was used as the chromophore source. Six types of plates were evaluated for use with the probe colorimeter; they generated reproducibility values (100% coefficient of variation) ranging from 91 to 98% when one individual made 24 independent measurements on the same dilution of chromophore on each plate. Eleven individuals each performed 24 measurements with the colorimeter on either a visually light (absorbance of 0.10 at 420 nm) or a dark (absorbance of 0.80 at 420 nm) dilution of chromophore; reproducibilities averaged 87% for the light dilution and 97% for the dark dilution. When one individual measured the same chromophore sample at least 20 times in the colorimeter, in the spectrophotometer or in the enzyme immunoassay reader, reproducibility for each instrument was greater than 99%. Measurements of a dilution series of chromophore in a fixed volume indicated that the optical responses of each instrument were linear in a range of 0.05 to 1.10 absorbance units. Images PMID:6341399

Probe colorimeter for quantitating enzyme-linked immunosorbent assays and other colorimetric assays performed with microplates.

PubMed

Ackerman, S B; Kelley, E A

1983-03-01

The performance of a fiberoptic probe colorimeter (model PC800; Brinkmann Instruments, Inc., Westbury, N.Y.) for quantitating enzymatic or colorimetric assays in 96-well microtiter plates was compared with the performances of a spectrophotometer (model 240; Gilford Instrument Laboratories, Inc., Oberlin, Ohio) and a commercially available enzyme immunoassay reader (model MR590; Dynatech Laboratories, Inc., Alexandria, Va.). Alkaline phosphatase-p-nitrophenyl phosphate in 3 M NaOH was used as the chromophore source. Six types of plates were evaluated for use with the probe colorimeter; they generated reproducibility values (100% coefficient of variation) ranging from 91 to 98% when one individual made 24 independent measurements on the same dilution of chromophore on each plate. Eleven individuals each performed 24 measurements with the colorimeter on either a visually light (absorbance of 0.10 at 420 nm) or a dark (absorbance of 0.80 at 420 nm) dilution of chromophore; reproducibilities averaged 87% for the light dilution and 97% for the dark dilution. When one individual measured the same chromophore sample at least 20 times in the colorimeter, in the spectrophotometer or in the enzyme immunoassay reader, reproducibility for each instrument was greater than 99%. Measurements of a dilution series of chromophore in a fixed volume indicated that the optical responses of each instrument were linear in a range of 0.05 to 1.10 absorbance units.

New Metacognitive Model for Human Performance Technology

ERIC Educational Resources Information Center

Turner, John R.

2011-01-01

Addressing metacognitive functions has been shown to improve performance at the individual, team, group, and organizational levels. Metacognition is beginning to surface as an added cognate discipline for the field of human performance technology (HPT). Advances from research in the fields of cognition and metacognition offer a place for HPT to…

Simulation of Drought-induced Tree Mortality Using a New Individual and Hydraulic Trait-based Model (S-TEDy)

NASA Astrophysics Data System (ADS)

Sinha, T.; Gangodagamage, C.; Ale, S.; Frazier, A. G.; Giambelluca, T. W.; Kumagai, T.; Nakai, T.; Sato, H.

2017-12-01

Drought-related tree mortality at a regional scale causes drastic shifts in carbon and water cycling in Southeast Asian tropical rainforests, where severe droughts are projected to occur more frequently, especially under El Niño conditions. To provide a useful tool for projecting the tropical rainforest dynamics under climate change conditions, we developed the Spatially Explicit Individual-Based (SEIB) Dynamic Global Vegetation Model (DGVM) applicable to simulating mechanistic tree mortality induced by the climatic impacts via individual-tree-scale ecophysiology such as hydraulic failure and carbon starvation. In this study, we present the new model, SEIB-originated Terrestrial Ecosystem Dynamics (S-TEDy) model, and the computation results were compared with observations collected at a field site in a Bornean tropical rainforest. Furthermore, after validating the model's performance, numerical experiments addressing a future of the tropical rainforest were conducted using some global climate model (GCM) simulation outputs.

The horizontal and vertical attributes of individualism and collectivism in a Spanish population.

PubMed

Gouveia, Valdiney V; Clemente, Miguel; Espinosa, Pablo

2003-02-01

The authors examined the dimensionality and factorial structure of individualism and collectivism in Spanish participants (N = 526). A series of confirmatory factor analyses were performed on responses to the 32-item individualism-collectivism measure reported by T. M. Singelis, H. C. Triandis, D. S. Bhawuk, and M. Gelfand (1995). Consistent with earlier data, the best fitting model was multidimensional: a vertical versus a horizontal attribute crossed with individualism and collectivism dimensions. Whereas the overall fit of the data to a LISREL model was moderate, additional self-report data on respondents' interpersonal experiences supported the construct validity of the 4 factors. The authors suggest that the additional complexity is useful in explaining Spanish social behavior.

Movement ecology: size-specific behavioral response of an invasive snail to food availability.

PubMed

Snider, Sunny B; Gilliam, James F

2008-07-01

Immigration, emigration, migration, and redistribution describe processes that involve movement of individuals. These movements are an essential part of contemporary ecological models, and understanding how movement is affected by biotic and abiotic factors is important for effectively modeling ecological processes that depend on movement. We asked how phenotypic heterogeneity (body size) and environmental heterogeneity (food resource level) affect the movement behavior of an aquatic snail (Tarebia granifera), and whether including these phenotypic and environmental effects improves advection-diffusion models of movement. We postulated various elaborations of the basic advection diffusion model as a priori working hypotheses. To test our hypotheses we measured individual snail movements in experimental streams at high- and low-food resource treatments. Using these experimental movement data, we examined the dependency of model selection on resource level and body size using Akaike's Information Criterion (AIC). At low resources, large individuals moved faster than small individuals, producing a platykurtic movement distribution; including size dependency in the model improved model performance. In stark contrast, at high resources, individuals moved upstream together as a wave, and body size differences largely disappeared. The model selection exercise indicated that population heterogeneity is best described by the advection component of movement for this species, because the top-ranked model included size dependency in advection, but not diffusion. Also, all probable models included resource dependency. Thus population and environmental heterogeneities both influence individual movement behaviors and the population-level distribution kernels, and their interaction may drive variation in movement behaviors in terms of both advection rates and diffusion rates. A behaviorally informed modeling framework will integrate the sentient response of individuals in terms of movement and enhance our ability to accurately model ecological processes that depend on animal movement.

Individuated finger control in focal hand dystonia: an fMRI study

PubMed Central

Moore, Ryan D; Gallea, Cecile; Horovitz, Silvina G; Hallett, Mark

2012-01-01

Objectives To better understand deficient selective motor control in focal hand dystonia by determining changes in striatal activation and connectivity in patients performing individuated finger control. Methods Functional imaging with a 3-Tesla magnetic resonance scanner was performed on 18 patients and 17 controls during non-symptom producing tasks requiring right-handed individuated or coupled finger control. A global linear model and psychophysiologic interactions model compared individuated to coupled tasks for patients and controls separately, and the results were submitted to a group analysis. The sensorimotor (posterior) and associative (anterior) putamen were considered as seed regions for the connectivity analysis. Results Compared to controls, patients had significant differences in activations and connectivity during individuated compared to coupled tasks: (i) decreased activations in the bilateral postcentral gyri, right associative posterior parietal areas, right cerebellum and left posterior putamen, while activations in the left anterior putamen were not different; (ii) increased connectivity of the left posterior putamen with the left cerebellum and left sensorimotor cortex; (iii) increased connectivity of the left anterior putamen with bilateral supplementary motor areas, the left premotor cortex, and left cerebellum. Interpretation Decreased activations in the sensorimotor putamen and cerebellum controlling the affected hand might underlie low levels of surround inhibition during individuated tasks. For identical motor performance in both groups, increased connectivity of sensorimotor and associative striato-cortical circuits in FHD suggests that both affected and unaffected territories of the striatum participate in compensatory processes. PMID:22484405

Individuated finger control in focal hand dystonia: an fMRI study.

PubMed

Moore, Ryan D; Gallea, Cecile; Horovitz, Silvina G; Hallett, Mark

2012-07-16

To better understand deficient selective motor control in focal hand dystonia by determining changes in striatal activation and connectivity in patients performing individuated finger control. Functional imaging with a 3-Tesla magnetic resonance scanner was performed on 18 patients and 17 controls during non-symptom producing tasks requiring right-handed individuated or coupled finger control. A global linear model and psychophysiologic interaction model compared individuated to coupled tasks for patients and controls separately, and the results were submitted to a group analysis. The sensorimotor (posterior) and associative (anterior) parts of the putamen were considered as seed regions for the connectivity analysis. Compared to controls, patients had significant differences in activations and connectivity during individuated compared to coupled tasks: (i) decreased activations in the bilateral postcentral gyri, right associative posterior parietal areas, right cerebellum and left posterior putamen, while activations in the left anterior putamen were not different; (ii) increased connectivity of the left posterior putamen with the left cerebellum and left sensorimotor cortex; and (iii) increased connectivity of the left anterior putamen with bilateral supplementary motor areas, the left premotor cortex, and left cerebellum. Decreased activations in the sensorimotor putamen and cerebellum controlling the affected hand might underlie low levels of surround inhibition during individuated tasks. For identical motor performance in both groups, increased connectivity of sensorimotor and associative striato-cortical circuits in FHD suggests that both affected and unaffected territories of the striatum participate in compensatory processes. Published by Elsevier Inc.

Assessing the quality of activities in a smart environment.

PubMed

Cook, Diane J; Schmitter-Edgecombe, M

2009-01-01

Pervasive computing technology can provide valuable health monitoring and assistance technology to help individuals live independent lives in their own homes. As a critical part of this technology, our objective is to design software algorithms that recognize and assess the consistency of activities of daily living that individuals perform in their own homes. We have designed algorithms that automatically learn Markov models for each class of activity. These models are used to recognize activities that are performed in a smart home and to identify errors and inconsistencies in the performed activity. We validate our approach using data collected from 60 volunteers who performed a series of activities in our smart apartment testbed. The results indicate that the algorithms correctly label the activities and successfully assess the completeness and consistency of the performed task. Our results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology. These algorithms will be useful for automating remote health monitoring and interventions.

An Examination of Individual Performance Using Markov Models in the Hellenic Navy’s Officer-Performance Evaluation System

DTIC Science & Technology

2012-03-01

similar to primary needs, but now emotions have replaced transmitted signals. In the 1940s, Maslow developed the needs-hierarchy theory. 37...is the specific design to meet new challenges and realize our potential. McShane and Von Glinow state that …according to Maslow , we are...circumstances, individuals seek their constant personal development. In addition to Abraham Maslow’s needs-hierarchy theory, a recently developed

Individual self-reported health, social participation and neighbourhood: a multilevel analysis in Malmö, Sweden.

PubMed

Lindström, Martin; Moghaddassi, Mahnaz; Merlo, Juan

2004-07-01

The influence of neighbourhood and individual factors on self-reported health was investigated. The public health survey in Malmö 1994 is a cross-sectional study. A total of 3,602 individuals aged 20-80 living in 75 neighbourhoods answered a postal questionnaire. The participation rate was 71%. A multilevel logistic regression model, with individuals at the first level and neighbourhoods at the second, was performed. We analysed the effect (intra-area correlation, cross-level modification and odds ratios) of neighbourhood on self-reported health after adjustment for individual factors. The neighbourhoods accounted for 2.8% of the crude total variance in self-reported health status. This effect was significantly reduced when individual factors such as country of origin, education and social participation were included in the model. In fact, no significant variance in self-reported health remained after the introduction of the individual factors in the model. In Malmö, the neighbourhood variance in self-reported health is mainly affected by individual factors, especially country of origin, socioeconomic status measured as level of education and individual social participation. Copyright 2004 The Institute for Cancer Prevention and Elsevier Inc.

PVWatts Version 1 Technical Reference

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

Dobos, A. P.

2013-10-01

The NREL PVWatts(TM) calculator is a web application developed by the National Renewable Energy Laboratory (NREL) that estimates the electricity production of a grid-connected photovoltaic system based on a few simple inputs. PVWatts combines a number of sub-models to predict overall system performance, and makes several hidden assumptions about performance parameters. This technical reference details the individual sub-models, documents assumptions and hidden parameters, and explains the sequence of calculations that yield the final system performance estimation.

Team Resilience as a Second-Order Emergent State: A Theoretical Model and Research Directions

PubMed Central

Bowers, Clint; Kreutzer, Christine; Cannon-Bowers, Janis; Lamb, Jerry

2017-01-01

Resilience has been recognized as an important phenomenon for understanding how individuals overcome difficult situations. However, it is not only individuals who face difficulties; it is not uncommon for teams to experience adversity. When they do, they must be able to overcome these challenges without performance decrements.This manuscript represents a theoretical model that might be helpful in conceptualizing this important construct. Specifically, it describes team resilience as a second-order emergent state. We also include research propositions that follow from the model. PMID:28861013

On the use and the performance of software reliability growth models

NASA Technical Reports Server (NTRS)

Keiller, Peter A.; Miller, Douglas R.

1991-01-01

We address the problem of predicting future failures for a piece of software. The number of failures occurring during a finite future time interval is predicted from the number failures observed during an initial period of usage by using software reliability growth models. Two different methods for using the models are considered: straightforward use of individual models, and dynamic selection among models based on goodness-of-fit and quality-of-prediction criteria. Performance is judged by the relative error of the predicted number of failures over future finite time intervals relative to the number of failures eventually observed during the intervals. Six of the former models and eight of the latter are evaluated, based on their performance on twenty data sets. Many open questions remain regarding the use and the performance of software reliability growth models.

Competing risks regression for clustered data

PubMed Central

Zhou, Bingqing; Fine, Jason; Latouche, Aurelien; Labopin, Myriam

2012-01-01

A population average regression model is proposed to assess the marginal effects of covariates on the cumulative incidence function when there is dependence across individuals within a cluster in the competing risks setting. This method extends the Fine–Gray proportional hazards model for the subdistribution to situations, where individuals within a cluster may be correlated due to unobserved shared factors. Estimators of the regression parameters in the marginal model are developed under an independence working assumption where the correlation across individuals within a cluster is completely unspecified. The estimators are consistent and asymptotically normal, and variance estimation may be achieved without specifying the form of the dependence across individuals. A simulation study evidences that the inferential procedures perform well with realistic sample sizes. The practical utility of the methods is illustrated with data from the European Bone Marrow Transplant Registry. PMID:22045910

The Structure of Human Intelligence: It Is Verbal, Perceptual, and Image Rotation (VPR), Not Fluid and Crystallized

ERIC Educational Resources Information Center

Johnson, W.; Bouchard, T.J.

2005-01-01

In a heterogeneous sample of 436 adult individuals who completed 42 mental ability tests, we evaluated the relative statistical performance of three major psychometric models of human intelligence-the Cattell-Horn fluid-crystallized model, Vernon's verbal-perceptual model, and Carroll's three-strata model. The verbal-perceptual model fit…

A method to assess the influence of individual player performance distribution on match outcome in team sports.

PubMed

Robertson, Sam; Gupta, Ritu; McIntosh, Sam

2016-10-01

This study developed a method to determine whether the distribution of individual player performances can be modelled to explain match outcome in team sports, using Australian Rules football as an example. Player-recorded values (converted to a percentage of team total) in 11 commonly reported performance indicators were obtained for all regular season matches played during the 2014 Australian Football League season, with team totals also recorded. Multiple features relating to heuristically determined percentiles for each performance indicator were then extracted for each team and match, along with the outcome (win/loss). A generalised estimating equation model comprising eight key features was developed, explaining match outcome at a median accuracy of 63.9% under 10-fold cross-validation. Lower 75th, 90th and 95th percentile values for team goals and higher 25th and 50th percentile values for disposals were linked with winning. Lower 95th and higher 25th percentile values for Inside 50s and Marks, respectively, were also important contributors. These results provide evidence supporting team strategies which aim to obtain an even spread of goal scorers in Australian Rules football. The method developed in this investigation could be used to quantify the importance of individual contributions to overall team performance in team sports.

Post-event processing in social anxiety.

PubMed

Dannahy, Laura; Stopa, Lusia

2007-06-01

Clark and Wells' [1995. A cognitive model of social phobia. In: R. Heimberg, M. Liebowitz, D.A. Hope, & F.R. Schneier (Eds.) Social phobia: Diagnosis, assessment and treatment (pp. 69-93). New York: Guildford Press.] cognitive model of social phobia proposes that following a social event, individuals with social phobia will engage in post-event processing, during which they conduct a detailed review of the event. This study investigated the relationship between self-appraisals of performance and post-event processing in individuals high and low in social anxiety. Participants appraised their performance immediately after a conversation with an unknown individual and prior to an anticipated second conversation task 1 week later. The frequency and valence of post-event processing during the week following the conversation was also assessed. The study also explored differences in the metacognitive processes of high and low socially anxious participants. The high socially anxious group experienced more anxiety, predicted worse performance, underestimated their actual performance, and engaged in more post-event processing than low socially anxious participants. The degree of negative post-event processing was linked to the extent of social anxiety and negative appraisals of performance, both immediately after the conversation task and 1 week later. Differences were also observed in some metacognitive processes. The results are discussed in relation to current theory and previous research.

Modelling nutrition across organizational levels: from individuals to superorganisms.

PubMed

Lihoreau, Mathieu; Buhl, Jerome; Charleston, Michael A; Sword, Gregory A; Raubenheimer, David; Simpson, Stephen J

2014-10-01

The Geometric Framework for nutrition has been increasingly used to describe how individual animals regulate their intake of multiple nutrients to maintain target physiological states maximizing growth and reproduction. However, only a few studies have considered the potential influences of the social context in which these nutritional decisions are made. Social insects, for instance, have evolved extreme levels of nutritional interdependence in which food collection, processing, storage and disposal are performed by different individuals with different nutritional needs. These social interactions considerably complicate nutrition and raise the question of how nutrient regulation is achieved at multiple organizational levels, by individuals and groups. Here, we explore the connections between individual- and collective-level nutrition by developing a modelling framework integrating concepts of nutritional geometry into individual-based models. Using this approach, we investigate how simple nutritional interactions between individuals can mediate a range of emergent collective-level phenomena in social arthropods (insects and spiders) and provide examples of novel and empirically testable predictions. We discuss how our approach could be expanded to a wider range of species and social systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Cattell-Horn-Carroll Model of Cognition for Clinical Assessment

ERIC Educational Resources Information Center

Jewsbury, Paul A.; Bowden, Stephen C.; Duff, Kevin

2017-01-01

The Cattell-Horn-Carroll (CHC) model is a comprehensive model of the major dimensions of individual differences that underlie performance on cognitive tests. Studies evaluating the generality of the CHC model across test batteries, age, gender, and culture were reviewed and found to be overwhelmingly supportive. However, less research is available…

Political Alienation in Adolescence: Associations with Parental Role Models, Parenting Styles, and Classroom Climate

ERIC Educational Resources Information Center

Gniewosz, Burkhard; Noack, Peter; Buhl, Monika

2009-01-01

The present study examined how parental political attitudes, parenting styles, and classroom characteristics predict adolescents' political alienation, as feelings about the individual's ability to affect the political system's performance at the individual level. Participants were 463 families that included mothers, fathers, and their adolescent…

A Job Retention Model for Individuals with Mental Retardation

ERIC Educational Resources Information Center

Fornes, Sandra

2006-01-01

This structured literature review examines the literature and addresses issues of job retention for adult workers with moderate to mild mental retardation (MR), investigating the relationships between work-related social behaviors, self-determination, person-job congruency of individuals with MR, and their job performance and job satisfaction with…

Individual versus Group Argumentation: Student's Performance in a Malaysian Context

ERIC Educational Resources Information Center

Heng, Lee Ling; Surif, Johari; Seng, Cher Hau

2014-01-01

Scientific argumentation has been greatly emphasized in the National Science Standard due to its ability to enhance students' understanding of scientific concepts. This study investigated the mastery level of scientific argumentation, based on Toulmin's Argumentation Model (TAP), when students engage in individual and group argumentations. A total…

Association of Individual Characteristics with Teleoperation Performance.

PubMed

Pan, Dan; Zhang, Yijing; Li, Zhizhong; Tian, Zhiqiang

2016-09-01

A number of space activities (e.g., extravehicular astronaut rescue, cooperation in satellite services, space station supplies, and assembly) are implemented directly or assisted by remote robotic arms. Our study aimed to reveal those individual characteristics which could positively influence or even predict teleoperation performance of such a space robotic arm. There were 64 male volunteers without robot operation experience recruited for the study. Their individual characteristics were assessed, including spatial cognitive ability, cognitive style, and personality traits. The experimental tasks were three abstracted teleoperation tasks of a simulated space robotic arm: point aiming, line alignment, and obstacle avoidance. Teleoperation performance was measured from two aspects: task performance (completion time, extra distance moved, operation slips) and safety performance (collisions, joint limitations reached). The Pearson coefficients between individual characteristics and teleoperation performance were examined along with performance prediction models. It was found that the subjects with relatively high mental rotation ability or low neuroticism had both better task and safety performance (|r| = 0.212 ∼ 0.381). Subjects with relatively high perspective taking ability or high agreeableness had better task performance (r = -0.253; r = -0.249). Imagery subjects performed better than verbal subjects regarding both task and safety performance (|r| = 0.236 ∼ 0.290). Compared with analytic subjects, wholist subjects had better safety performance (r = 0.300). Additionally, extraverted subjects had better task performance (r = -0.259), but worse safety performance (r = 0.230). Those with high spatial cognitive ability, imagery and wholist cognitive style, low neuroticism, and high agreeableness were seen to have more advantages in working with the remote robotic arm. These results could be helpful to astronaut selection and training for space station missions. Pan D, Zhang Y, Li Z, Tian Z. Association of individual characteristics with teleoperation performance. Aerosp Med Hum Perform. 2016; 87(9):772-780.

Evaluation of an urban land surface scheme over a tropical suburban neighborhood

NASA Astrophysics Data System (ADS)

Harshan, Suraj; Roth, Matthias; Velasco, Erik; Demuzere, Matthias

2017-07-01

The present study evaluates the performance of the SURFEX (TEB/ISBA) urban land surface parametrization scheme in offline mode over a suburban area of Singapore. Model performance (diurnal and seasonal characteristics) is investigated using measurements of energy balance fluxes, surface temperatures of individual urban facets, and canyon air temperature collected during an 11-month period. Model performance is best for predicting net radiation and sensible heat fluxes (both are slightly overpredicted during daytime), but weaker for latent heat (underpredicted during daytime) and storage heat fluxes (significantly underpredicted daytime peaks and nighttime storage). Daytime surface temperatures are generally overpredicted, particularly those containing horizontal surfaces such as roofs and roads. This result, together with those for the storage heat flux, point to the need for a better characterization of the thermal and radiative characteristics of individual urban surface facets in the model. Significant variation exists in model behavior between dry and wet seasons, the latter generally being better predicted. The simple vegetation parametrization used is inadequate to represent seasonal moisture dynamics, sometimes producing unrealistically dry conditions.

Modeling the residual effects and threshold saturation of training: a case study of Olympic swimmers

PubMed Central

Hellard, Philippe; Avalos, Marta; Millet, Grégoire; Lacoste, Lucien; Barale, Frédéric; Chatard, Jean-Claude

2005-01-01

The aim of this study was to model the residual effects of training on the swimming performance and to compare a model including threshold saturation (MM) to the Banister model (BM). Seven Olympic swimmers were studied over a period of 4 ± 2 years. For three training loads (low-intensity wLIT, high-intensity wHIT and strength training wST), three residual training effects were determined: short-term (STE) during the taper phase, i.e. three weeks before the performance (weeks 0, −1, −2), intermediate-term (ITE) during the intensity phase (weeks −3, −4 and −5) and long-term (LTE) during the volume phase (weeks −6, −7, −8). ITE and LTE were positive for wHIT and wLIT, respectively (P < 0.05). wLIT during taper was related to performances by a parabolic relationship (P < 0.05). Different quality measures indicated that MM compares favorably with BM. Identifying individual training thresholds may help individualizing the distribution of training loads. PMID:15705048

A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients.

PubMed

Oberije, Cary; De Ruysscher, Dirk; Houben, Ruud; van de Heuvel, Michel; Uyterlinde, Wilma; Deasy, Joseph O; Belderbos, Jose; Dingemans, Anne-Marie C; Rimner, Andreas; Din, Shaun; Lambin, Philippe

2015-07-15

Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability (www.predictcancer.org). The data set can be downloaded at https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048. The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Social participation, social capital and daily tobacco smoking: a population-based multilevel analysis in Malmö, Sweden.

PubMed

Lindström, Martin; Moghaddassi, Mahnaz; Bolin, Kristian; Lindgren, Björn; Merlo, Juan

2003-01-01

The aim of this study was to investigate the influence of contextual and individual factors on daily tobacco smoking. The public-health survey in Malmö 1994 is a cross-sectional study. A total of 5600 individuals aged 20-80 years were invited to answer a postal questionnaire. The participation rate was 71%. A multilevel logistic regression model, with individuals at the first level and neighbourhoods at the second, was performed. We analysed the effect (intra-area correlation, cross-level modification and odds ratios) of individual and neighbourhood factors on smoking after adjustment for individual factors. Neighbourhood factors accounted for 2.5% of the crude total variance in daily tobacco smoking. This effect was significantly reduced when the individual factors such as education were included in the model. However, individual social capital, measured by social participation, only marginally affected the total neighbourhood variance in daily tobacco smoking. In fact, no significant variance in daily tobacco smoking remained after the introduction of the individual factors other than individual social capital in the model. In Malmö, the neighbourhood variance in daily tobacco smoking is mainly affected by individual factors other than individual social capital, especially socioeconomic status measured as level of education.

Optical laboratory solution and error model simulation of a linear time-varying finite element equation

NASA Technical Reports Server (NTRS)

Taylor, B. K.; Casasent, D. P.

1989-01-01

The use of simplified error models to accurately simulate and evaluate the performance of an optical linear-algebra processor is described. The optical architecture used to perform banded matrix-vector products is reviewed, along with a linear dynamic finite-element case study. The laboratory hardware and ac-modulation technique used are presented. The individual processor error-source models and their simulator implementation are detailed. Several significant simplifications are introduced to ease the computational requirements and complexity of the simulations. The error models are verified with a laboratory implementation of the processor, and are used to evaluate its potential performance.

Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

PubMed

Zhang, Jianhua; Li, Sunan; Wang, Rubin

2017-01-01

In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

Verbal learning changes in older adults across 18 months.

PubMed

Zimprich, Daniel; Rast, Philippe

2009-07-01

The major aim of this study was to investigate individual changes in verbal learning across a period of 18 months. Individual differences in verbal learning have largely been neglected in the last years and, even more so, individual differences in change in verbal learning. The sample for this study comes from the Zurich Longitudinal Study on Cognitive Aging (ZULU; Zimprich et al., 2008a) and comprised 336 older adults in the age range of 65-80 years at first measurement occasion. In order to address change in verbal learning we used a latent change model of structured latent growth curves to account for the non-linearity of the verbal learning data. The individual learning trajectories were captured by a hyperbolic function which yielded three psychologically distinct parameters: initial performance, learning rate, and asymptotic performance. We found that average performance increased with respect to initial performance, but not in learning rate or in asymptotic performance. Further, variances and covariances remained stable across both measurement occasions, indicating that the amount of individual differences in the three parameters remained stable, as did the relationships among them. Moreover, older adults differed reliably in their amount of change in initial performance and asymptotic performance. Eventually, changes in asymptotic performance and learning rate were strongly negatively correlated. It thus appears as if change in verbal learning in old age is a constrained process: an increase in total learning capacity implies that it takes longer to learn. Together, these results point to the significance of individual differences in change of verbal learning in the elderly.

Ensuring quality and safety.

PubMed

Reid, Jerry

2010-01-01

The certification model addresses quality and safety by directly targeting the qualifications of individuals. The practice accreditation model takes a more global approach to quality and safety and addresses the qualifications of individuals and standards for additional components of the quality chain. Although both certification and practice accreditation fundamentally are voluntary, the programs may become mandatory when enforcement mechanisms are linked to the programs via state or federal legislation or via private reimbursement policies, effectively resulting in mandatory standards. The CARE bill takes a certification approach to quality and safety by focusing on the qualifications of the individual. MIPPA takes an accreditation approach by focusing on the practice. MQSA is somewhat of a hybrid in that it takes an accreditation approach, but spells out standards for the individual that the accreditor must follow. If the practice accreditation standards require that all technologists employed in the practice be certified in the modalities performed, then the practice accreditation model and the certification model become functionally equivalent in terms of personnel qualifications. To the extent that practice accreditation models are less prescriptive regarding personnel standards, the certification model results in more stringent standards.

Using a whole farm model to determine the impacts of mating management on the profitability of pasture-based dairy farms.

PubMed

Beukes, P C; Burke, C R; Levy, G; Tiddy, R M

2010-08-01

An approach to assessing likely impacts of altering reproductive performance on productivity and profitability in pasture-based dairy farms is described. The basis is the development of a whole farm model (WFM) that simulates the entire farm system and holistically links multiple physical performance factors to profitability. The WFM consists of a framework that links a mechanistic cow model, a pasture model, a crop model, management policies and climate. It simulates individual cows and paddocks, and runs on a day time-step. The WFM was upgraded to include reproductive modeling capability using reference tables and empirical equations describing published relationships between cow factors, physiology and mating management. It predicts reproductive status at any time point for individual cows within a modeled herd. The performance of six commercial pasture-based dairy farms was simulated for the period of 12 months beginning 1 June 2005 (05/06 year) to evaluate the accuracy of the model by comparison with actual outcomes. The model predicted most key performance indicators within an acceptable range of error (residual<10% of observed). The evaluated WFM was then used for the six farms to estimate the profitability of changes in farm "set-up" (farm conditions at the start of the farming year on 1 June) and mating management from 05/06 to 06/07 year. Among the six farms simulated, the 4-week calving rate emerged as an important set-up factor influencing profitability, while reproductive performance during natural bull mating was identified as an area with the greatest opportunity for improvement. The WFM presents utility to explore alternative management strategies to predict likely outcomes to proposed changes to a pasture-based farm system. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Influence of erroneous patient records on population pharmacokinetic modeling and individual bayesian estimation.

PubMed

van der Meer, Aize Franciscus; Touw, Daniël J; Marcus, Marco A E; Neef, Cornelis; Proost, Johannes H

2012-10-01

Observational data sets can be used for population pharmacokinetic (PK) modeling. However, these data sets are generally less precisely recorded than experimental data sets. This article aims to investigate the influence of erroneous records on population PK modeling and individual maximum a posteriori Bayesian (MAPB) estimation. A total of 1123 patient records of neonates who were administered vancomycin were used for population PK modeling by iterative 2-stage Bayesian (ITSB) analysis. Cut-off values for weighted residuals were tested for exclusion of records from the analysis. A simulation study was performed to assess the influence of erroneous records on population modeling and individual MAPB estimation. Also the cut-off values for weighted residuals were tested in the simulation study. Errors in registration have limited the influence on outcomes of population PK modeling but can have detrimental effects on individual MAPB estimation. A population PK model created from a data set with many registration errors has little influence on subsequent MAPB estimates for precisely recorded data. A weighted residual value of 2 for concentration measurements has good discriminative power for identification of erroneous records. ITSB analysis and its individual estimates are hardly affected by most registration errors. Large registration errors can be detected by weighted residuals of concentration.

A New Metric for Quantifying Performance Impairment on the Psychomotor Vigilance Test

DTIC Science & Technology

2012-01-01

used the coefficient of determination (R2) and the P-values based on Bartelss test of randomness of the residual error to quantify the goodness - of - fit ...we used the goodness - of - fit between each metric and the corresponding individualized two-process model output (Rajaraman et al., 2008, 2009) to assess...individualized two-process model fits for each of the 12 subjects using the five metrics. The P-values are for Bartelss

SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients.

PubMed

Holden, Richard J; Carayon, Pascale; Gurses, Ayse P; Hoonakker, Peter; Hundt, Ann Schoofs; Ozok, A Ant; Rivera-Rodriguez, A Joy

2013-01-01

Healthcare practitioners, patient safety leaders, educators and researchers increasingly recognise the value of human factors/ergonomics and make use of the discipline's person-centred models of sociotechnical systems. This paper first reviews one of the most widely used healthcare human factors systems models, the Systems Engineering Initiative for Patient Safety (SEIPS) model, and then introduces an extended model, 'SEIPS 2.0'. SEIPS 2.0 incorporates three novel concepts into the original model: configuration, engagement and adaptation. The concept of configuration highlights the dynamic, hierarchical and interactive properties of sociotechnical systems, making it possible to depict how health-related performance is shaped at 'a moment in time'. Engagement conveys that various individuals and teams can perform health-related activities separately and collaboratively. Engaged individuals often include patients, family caregivers and other non-professionals. Adaptation is introduced as a feedback mechanism that explains how dynamic systems evolve in planned and unplanned ways. Key implications and future directions for human factors research in healthcare are discussed.

Prediction of risk of recurrence of venous thromboembolism following treatment for a first unprovoked venous thromboembolism: systematic review, prognostic model and clinical decision rule, and economic evaluation.

PubMed

Ensor, Joie; Riley, Richard D; Jowett, Sue; Monahan, Mark; Snell, Kym Ie; Bayliss, Susan; Moore, David; Fitzmaurice, David

2016-02-01

Unprovoked first venous thromboembolism (VTE) is defined as VTE in the absence of a temporary provoking factor such as surgery, immobility and other temporary factors. Recurrent VTE in unprovoked patients is highly prevalent, but easily preventable with oral anticoagulant (OAC) therapy. The unprovoked population is highly heterogeneous in terms of risk of recurrent VTE. The first aim of the project is to review existing prognostic models which stratify individuals by their recurrence risk, therefore potentially allowing tailored treatment strategies. The second aim is to enhance the existing research in this field, by developing and externally validating a new prognostic model for individual risk prediction, using a pooled database containing individual patient data (IPD) from several studies. The final aim is to assess the economic cost-effectiveness of the proposed prognostic model if it is used as a decision rule for resuming OAC therapy, compared with current standard treatment strategies. Standard systematic review methodology was used to identify relevant prognostic model development, validation and cost-effectiveness studies. Bibliographic databases (including MEDLINE, EMBASE and The Cochrane Library) were searched using terms relating to the clinical area and prognosis. Reviewing was undertaken by two reviewers independently using pre-defined criteria. Included full-text articles were data extracted and quality assessed. Critical appraisal of included full texts was undertaken and comparisons made of model performance. A prognostic model was developed using IPD from the pooled database of seven trials. A novel internal-external cross-validation (IECV) approach was used to develop and validate a prognostic model, with external validation undertaken in each of the trials iteratively. Given good performance in the IECV approach, a final model was developed using all trials data. A Markov patient-level simulation was used to consider the economic cost-effectiveness of using a decision rule (based on the prognostic model) to decide on resumption of OAC therapy (or not). Three full-text articles were identified by the systematic review. Critical appraisal identified methodological and applicability issues; in particular, all three existing models did not have external validation. To address this, new prognostic models were sought with external validation. Two potential models were considered: one for use at cessation of therapy (pre D-dimer), and one for use after cessation of therapy (post D-dimer). Model performance measured in the external validation trials showed strong calibration performance for both models. The post D-dimer model performed substantially better in terms of discrimination (c = 0.69), better separating high- and low-risk patients. The economic evaluation identified that a decision rule based on the final post D-dimer model may be cost-effective for patients with predicted risk of recurrence of over 8% annually; this suggests continued therapy for patients with predicted risks ≥ 8% and cessation of therapy otherwise. The post D-dimer model performed strongly and could be useful to predict individuals' risk of recurrence at any time up to 2-3 years, thereby aiding patient counselling and treatment decisions. A decision rule using this model may be cost-effective for informing clinical judgement and patient opinion in treatment decisions. Further research may investigate new predictors to enhance model performance and aim to further externally validate to confirm performance in new, non-trial populations. Finally, it is essential that further research is conducted to develop a model predicting bleeding risk on therapy, to manage the balance between the risks of recurrence and bleeding. This study is registered as PROSPERO CRD42013003494. The National Institute for Health Research Health Technology Assessment programme.

Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model.

PubMed

Resende, R T; Resende, M D V; Silva, F F; Azevedo, C F; Takahashi, E K; Silva-Junior, O B; Grattapaglia, D

2017-10-01

We report a genomic selection (GS) study of growth and wood quality traits in an outbred F 2 hybrid Eucalyptus population (n=768) using high-density single-nucleotide polymorphism (SNP) genotyping. Going beyond previous reports in forest trees, models were developed for different selection targets, namely, families, individuals within families and individuals across the entire population using a genomic model including dominance. To provide a more breeder-intelligible assessment of the performance of GS we calculated the expected response as the percentage gain over the population average expected genetic value (EGV) for different proportions of genomically selected individuals, using a rigorous cross-validation (CV) scheme that removed relatedness between training and validation sets. Predictive abilities (PAs) were 0.40-0.57 for individual selection and 0.56-0.75 for family selection. PAs under an additive+dominance model improved predictions by 5 to 14% for growth depending on the selection target, but no improvement was seen for wood traits. The good performance of GS with no relatedness in CV suggested that our average SNP density (~25 kb) captured some short-range linkage disequilibrium. Truncation GS successfully selected individuals with an average EGV significantly higher than the population average. Response to GS on a per year basis was ~100% more efficient than by phenotypic selection and more so with higher selection intensities. These results contribute further experimental data supporting the positive prospects of GS in forest trees. Because generation times are long, traits are complex and costs of DNA genotyping are plummeting, genomic prediction has good perspectives of adoption in tree breeding practice.

Competition for light between individual trees lowers reference canopy stomatal conductance: Results from a model

NASA Astrophysics Data System (ADS)

Loranty, Michael M.; Mackay, D. Scott; Ewers, Brent E.; Traver, Elizabeth; Kruger, Eric L.

2010-12-01

We have used an ecosystem model, TREES (Terrestrial Regional Ecosystem Exchange Simulator), to test the hypothesis that competition for light limits reference canopy stomatal conductance (GSref; conductance at 1 kPa vapor pressure deficit) for individual tree crowns. Sap flux (JS) data was collected at an aspen-dominated unmanaged early successional site, and at a sugar maple dominated midsuccessional site managed for timber production. Using a Monte Carlo approach, JS scaled canopy transpiration (EC) estimates were used to parameterize two versions of the model for each tree individually; a control model treated trees as isolated individuals, and a modified version incorporated the shading effects of neighboring individuals on incident radiation. Agreement between simulated and observed EC was better for maple than for aspen using the control model. Accounting for canopy heterogeneity using a three-dimensional canopy representation had minimal effects on estimates of GSref or model performance for individual maples. At the Aspen site the modified model resulted in improved EC estimates, particularly for trees with lower GSref and more shading by neighboring individuals. Our results imply a link between photosynthetic capacity, as mediated by competitive light environment, and GSref. We conclude that accounting for the effects of canopy heterogeneity on incident radiation improves modeled estimates of canopy carbon and water fluxes, especially for shade intolerant species. Furthermore our results imply a link between ecosystem structure and function that may be exploited to elucidate the impacts of forest structural heterogeneity on ecosystem fluxes of carbon and water via LiDAR remote sensing.

Formal implementation of a performance evaluation model for the face recognition system.

PubMed

Shin, Yong-Nyuo; Kim, Jason; Lee, Yong-Jun; Shin, Woochang; Choi, Jin-Young

2008-01-01

Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process.

Comprehensive system models: Strategies for evaluation

NASA Technical Reports Server (NTRS)

Field, Christopher; Kutzbach, John E.; Ramanathan, V.; Maccracken, Michael C.

1992-01-01

The task of evaluating comprehensive earth system models is vast involving validations of every model component at every scale of organization, as well as tests of all the individual linkages. Even the most detailed evaluation of each of the component processes and the individual links among them should not, however, engender confidence in the performance of the whole. The integrated earth system is so rich with complex feedback loops, often involving components of the atmosphere, oceans, biosphere, and cryosphere, that it is certain to exhibit emergent properties very difficult to predict from the perspective of a narrow focus on any individual component of the system. Therefore, a substantial share of the task of evaluating comprehensive earth system models must reside at the level of whole system evaluations. Since complete, integrated atmosphere/ ocean/ biosphere/ hydrology models are not yet operational, questions of evaluation must be addressed at the level of the kinds of earth system processes that the models should be competent to simulate, rather than at the level of specific performance criteria. Here, we have tried to identify examples of earth system processes that are difficult to simulate with existing models and that involve a rich enough suite of feedbacks that they are unlikely to be satisfactorily described by highly simplified or toy models. Our purpose is not to specify a checklist of evaluation criteria but to introduce characteristics of the earth system that may present useful opportunities for model testing and, of course, improvement.

Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

USGS Publications Warehouse

Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.

2009-01-01

This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in multi-model ensembles. The reasons behind these observations may relate to the effects of the weighting schemes, non-stationarity of the climate series and possible cross-correlations between models. Crown Copyright ?? 2008.

Redwing: A MOOSE application for coupling MPACT and BISON

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

Frederick N. Gleicher; Michael Rose; Tom Downar

Fuel performance and whole core neutron transport programs are often used to analyze fuel behavior as it is depleted in a reactor. For fuel performance programs, internal models provide the local intra-pin power density, fast neutron flux, burnup, and fission rate density, which are needed for a fuel performance analysis. The fuel performance internal models have a number of limitations. These include effects on the intra-pin power distribution by nearby assembly elements, such as water channels and control rods, and the further limitation of applicability to a specified fuel type such as low enriched UO2. In addition, whole core neutronmore » transport codes need an accurate intra-pin temperature distribution in order to calculate neutron cross sections. Fuel performance simulations are able to model the intra-pin fuel displacement as the fuel expands and densifies. These displacements must be accurately modeled in order to capture the eventual mechanical contact of the fuel and the clad; the correct radial gap width is needed for an accurate calculation of the temperature distribution of the fuel rod. Redwing is a MOOSE-based application that enables coupling between MPACT and BISON for transport and fuel performance coupling. MPACT is a 3D neutron transport and reactor core simulator based on the method of characteristics (MOC). The development of MPACT began at the University of Michigan (UM) and now is under the joint development of ORNL and UM as part of the DOE CASL Simulation Hub. MPACT is able to model the effects of local assembly elements and is able calculate intra-pin quantities such as the local power density on a volumetric mesh for any fuel type. BISON is a fuel performance application of Multi-physics Object Oriented Simulation Environment (MOOSE), which is under development at Idaho National Laboratory. BISON is able to solve the nonlinearly coupled mechanical deformation and heat transfer finite element equations that model a fuel element as it is depleted in a nuclear reactor. Redwing couples BISON and MPACT in a single application. Redwing maps and transfers the individual intra-pin quantities such as fission rate density, power density, and fast neutron flux from the MPACT volumetric mesh to the individual BISON finite element meshes. For a two-way coupling Redwing maps and transfers the individual pin temperature field and axially dependent coolant densities from the BISON mesh to the MPACT volumetric mesh. Details of the mapping are given. Redwing advances the simulation with the MPACT solution for each depletion time step and then advances the multiple BISON simulations for fuel performance calculations. Sub-cycle advancement can be applied to the individual BISON simulations and allows multiple time steps to be applied to the fuel performance simulations. Currently, only loose coupling where data from a previous time step is applied to the current time step is performed.« less

Development of a Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo): Study Protocol.

PubMed

Iorio, Alfonso; Keepanasseril, Arun; Foster, Gary; Navarro-Ruan, Tamara; McEneny-King, Alanna; Edginton, Andrea N; Thabane, Lehana

2016-12-15

Individual pharmacokinetic assessment is a critical component of tailored prophylaxis for hemophilia patients. Population pharmacokinetics allows using individual sparse data, thus simplifying individual pharmacokinetic studies. Implementing population pharmacokinetics capacity for the hemophilia community is beyond individual reach and requires a system effort. The Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) project aims to assemble a database of patient pharmacokinetic data for all existing factor concentrates, develop and validate population pharmacokinetics models, and integrate these models within a Web-based calculator for individualized pharmacokinetic estimation in patients at participating treatment centers. Individual pharmacokinetic studies on factor VIII and IX concentrates will be sourced from pharmaceutical companies and independent investigators. All factor concentrate manufacturers, hemophilia treatment centers (HTCs), and independent investigators (identified via a systematic review of the literature) having on file pharmacokinetic data and willing to contribute full or sparse pharmacokinetic data will be eligible for participation. Multicompartmental modeling will be performed using a mixed-model approach for derivation and Bayesian forecasting for estimation of individual sparse data. NONMEM (ICON Development Solutions) will be used as modeling software. The WAPPS-Hemo research network has been launched and is currently joined by 30 HTCs from across the world. We have gathered dense individual pharmacokinetic data on 878 subjects, including several replicates, on 21 different molecules from 17 different sources. We have collected sparse individual pharmacokinetic data on 289 subjects from the participating centers through the testing phase of the WAPPS-Hemo Web interface. We have developed prototypal population pharmacokinetics models for 11 molecules. The WAPPS-Hemo website (available at www.wapps-hemo.org, version 2.4), with core functionalities allowing hemophilia treaters to obtain individual pharmacokinetic estimates on sparse data points after 1 or more infusions of a factor concentrate, was launched for use within the research network in July 2015. The WAPPS-Hemo project and research network aims to make it easier to perform individual pharmacokinetic assessments on a reduced number of plasma samples by adoption of a population pharmacokinetics approach. The project will also gather data to substantially enhance the current knowledge about factor concentrate pharmacokinetics and sources of its variability in target populations. ClinicalTrials.gov NCT02061072; https://clinicaltrials.gov/ct2/show/NCT02061072 (Archived by WebCite at http://www.webcitation.org/6mRK9bKP6). ©Alfonso Iorio, Arun Keepanasseril, Gary Foster, Tamara Navarro-Ruan, Alanna McEneny-King, Andrea N Edginton, Lehana Thabane. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 15.12.2016.

The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.

PubMed

Viboud, Cécile; Sun, Kaiyuan; Gaffey, Robert; Ajelli, Marco; Fumanelli, Laura; Merler, Stefano; Zhang, Qian; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro

2018-03-01

Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014-2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and "fog of war" in outbreak data made available for predictions. Prediction targets included 1-4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario - mirroring an uncontrolled Ebola outbreak with substantial data reporting noise - was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such "peace time" forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and to assess model forecasting accuracy for a variety of known and hypothetical pathogens. Published by Elsevier B.V.

Risk of dependence associated with health, social support, and lifestyle

PubMed Central

Alcañiz, Manuela; Brugulat, Pilar; Guillén, Montserrat; Medina-Bustos, Antonia; Mompart-Penina, Anna; Solé-Auró, Aïda

2015-01-01

OBJECTIVE To analyze the prevalence of individuals at risk of dependence and its associated factors. METHODS The study was based on data from the Catalan Health Survey, Spain conducted in 2010 and 2011. Logistic regression models from a random sample of 3,842 individuals aged ≥ 15 years were used to classify individuals according to the state of their personal autonomy. Predictive models were proposed to identify indicators that helped distinguish dependent individuals from those at risk of dependence. Variables on health status, social support, and lifestyles were considered. RESULTS We found that 18.6% of the population presented a risk of dependence, especially after age 65. Compared with this group, individuals who reported dependence (11.0%) had difficulties performing activities of daily living and had to receive support to perform them. Habits such as smoking, excessive alcohol consumption, and being sedentary were associated with a higher probability of dependence, particularly for women. CONCLUSIONS Difficulties in carrying out activities of daily living precede the onset of dependence. Preserving personal autonomy and function without receiving support appear to be a preventive factor. Adopting an active and healthy lifestyle helps reduce the risk of dependence. PMID:26018786

Risk of dependence associated with health, social support, and lifestyle.

PubMed

Alcañiz, Manuela; Brugulat, Pilar; Guillén, Montserrat; Medina-Bustos, Antonia; Mompart-Penina, Anna; Solé-Auró, Aïda

2015-01-01

OBJECTIVE To analyze the prevalence of individuals at risk of dependence and its associated factors. METHODS The study was based on data from the Catalan Health Survey, Spain conducted in 2010 and 2011. Logistic regression models from a random sample of 3,842 individuals aged ≥ 15 years were used to classify individuals according to the state of their personal autonomy. Predictive models were proposed to identify indicators that helped distinguish dependent individuals from those at risk of dependence. Variables on health status, social support, and lifestyles were considered. RESULTS We found that 18.6% of the population presented a risk of dependence, especially after age 65. Compared with this group, individuals who reported dependence (11.0%) had difficulties performing activities of daily living and had to receive support to perform them. Habits such as smoking, excessive alcohol consumption, and being sedentary were associated with a higher probability of dependence, particularly for women. CONCLUSIONS Difficulties in carrying out activities of daily living precede the onset of dependence. Preserving personal autonomy and function without receiving support appear to be a preventive factor. Adopting an active and healthy lifestyle helps reduce the risk of dependence.

Multilevel Models for Intensive Longitudinal Data with Heterogeneous Autoregressive Errors: The Effect of Misspecification and Correction with Cholesky Transformation

PubMed Central

Jahng, Seungmin; Wood, Phillip K.

2017-01-01

Intensive longitudinal studies, such as ecological momentary assessment studies using electronic diaries, are gaining popularity across many areas of psychology. Multilevel models (MLMs) are most widely used analytical tools for intensive longitudinal data (ILD). Although ILD often have individually distinct patterns of serial correlation of measures over time, inferences of the fixed effects, and random components in MLMs are made under the assumption that all variance and autocovariance components are homogenous across individuals. In the present study, we introduced a multilevel model with Cholesky transformation to model ILD with individually heterogeneous covariance structure. In addition, the performance of the transformation method and the effects of misspecification of heterogeneous covariance structure were investigated through a Monte Carlo simulation. We found that, if individually heterogeneous covariances are incorrectly assumed as homogenous independent or homogenous autoregressive, MLMs produce highly biased estimates of the variance of random intercepts and the standard errors of the fixed intercept and the fixed effect of a level 2 covariate when the average autocorrelation is high. For intensive longitudinal data with individual specific residual covariance, the suggested transformation method showed lower bias in those estimates than the misspecified models when the number of repeated observations within individuals is 50 or more. PMID:28286490

Validation of a Cochlear Implant Patient-Specific Model of the Voltage Distribution in a Clinical Setting

PubMed Central

Nogueira, Waldo; Schurzig, Daniel; Büchner, Andreas; Penninger, Richard T.; Würfel, Waldemar

2016-01-01

Cochlear Implants (CIs) are medical implantable devices that can restore the sense of hearing in people with profound hearing loss. Clinical trials assessing speech intelligibility in CI users have found large intersubject variability. One possibility to explain the variability is the individual differences in the interface created between electrodes of the CI and the auditory nerve. In order to understand the variability, models of the voltage distribution of the electrically stimulated cochlea may be useful. With this purpose in mind, we developed a parametric model that can be adapted to each CI user based on landmarks from individual cone beam computed tomography (CBCT) scans of the cochlea before and after implantation. The conductivity values of each cochlea compartment as well as the weighting factors of different grounding modes have also been parameterized. Simulations were performed modeling the cochlea and electrode positions of 12 CI users. Three models were compared with different levels of detail: a homogeneous model (HM), a non-patient-specific model (NPSM), and a patient-specific model (PSM). The model simulations were compared with voltage distribution measurements obtained from the backward telemetry of the 12 CI users. Results show that the PSM produces the lowest error when predicting individual voltage distributions. Given a patient-specific geometry and electrode positions, we show an example on how to optimize the parameters of the model and how to couple it to an auditory nerve model. The model here presented may help to understand speech performance variability and support the development of new sound coding strategies for CIs. PMID:27933290

Beyond pain: modeling decision-making deficits in chronic pain.

PubMed

Hess, Leonardo Emanuel; Haimovici, Ariel; Muñoz, Miguel Angel; Montoya, Pedro

2014-01-01

Risky decision-making seems to be markedly disrupted in patients with chronic pain, probably due to the high cost that impose pain and negative mood on executive control functions. Patients' behavioral performance on decision-making tasks such as the Iowa Gambling Task (IGT) is characterized by selecting cards more frequently from disadvantageous than from advantageous decks, and by switching often between competing responses in comparison with healthy controls (HCs). In the present study, we developed a simple heuristic model to simulate individuals' choice behavior by varying the level of decision randomness and the importance given to gains and losses. The findings revealed that the model was able to differentiate the behavioral performance of patients with chronic pain and HCs at the group, as well as at the individual level. The best fit of the model in patients with chronic pain was yielded when decisions were not based on previous choices and when gains were considered more relevant than losses. By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains. In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis.

A comparative analysis of 9 multi-model averaging approaches in hydrological continuous streamflow simulation

NASA Astrophysics Data System (ADS)

Arsenault, Richard; Gatien, Philippe; Renaud, Benoit; Brissette, François; Martel, Jean-Luc

2015-10-01

This study aims to test whether a weighted combination of several hydrological models can simulate flows more accurately than the models taken individually. In addition, the project attempts to identify the most efficient model averaging method and the optimal number of models to include in the weighting scheme. In order to address the first objective, streamflow was simulated using four lumped hydrological models (HSAMI, HMETS, MOHYSE and GR4J-6), each of which were calibrated with three different objective functions on 429 watersheds. The resulting 12 hydrographs (4 models × 3 metrics) were weighted and combined with the help of 9 averaging methods which are the simple arithmetic mean (SAM), Akaike information criterion (AICA), Bates-Granger (BGA), Bayes information criterion (BICA), Bayesian model averaging (BMA), Granger-Ramanathan average variant A, B and C (GRA, GRB and GRC) and the average by SCE-UA optimization (SCA). The same weights were then applied to the hydrographs in validation mode, and the Nash-Sutcliffe Efficiency metric was measured between the averaged and observed hydrographs. Statistical analyses were performed to compare the accuracy of weighted methods to that of individual models. A Kruskal-Wallis test and a multi-objective optimization algorithm were then used to identify the most efficient weighted method and the optimal number of models to integrate. Results suggest that the GRA, GRB, GRC and SCA weighted methods perform better than the individual members. Model averaging from these four methods were superior to the best of the individual members in 76% of the cases. Optimal combinations on all watersheds included at least one of each of the four hydrological models. None of the optimal combinations included all members of the ensemble of 12 hydrographs. The Granger-Ramanathan average variant C (GRC) is recommended as the best compromise between accuracy, speed of execution, and simplicity.

A mechanistic spatio-temporal framework for modelling individual-to-individual transmission—With an application to the 2014-2015 West Africa Ebola outbreak

PubMed Central

McClelland, Amanda; Zelner, Jon; Streftaris, George; Funk, Sebastian; Metcalf, Jessica; Dalziel, Benjamin D.; Grenfell, Bryan T.

2017-01-01

In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging. PMID:29084216

A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak.

PubMed

Lau, Max S Y; Gibson, Gavin J; Adrakey, Hola; McClelland, Amanda; Riley, Steven; Zelner, Jon; Streftaris, George; Funk, Sebastian; Metcalf, Jessica; Dalziel, Benjamin D; Grenfell, Bryan T

2017-10-01

In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging.

Individual Resistance to Change

DTIC Science & Technology

2012-09-13

important aspects of the model that were not included elsewhere. As expressed by Burke and Litwin (1992), leadership is a cornerstone in understanding...The Study of Leadership Danvilie, IL: Interstate Printers and Publishers Burke W., Litwin G. (1992). A Causal Model of Organizational Performance

Impact of individual resilience and safety climate on safety performance and psychological stress of construction workers: A case study of the Ontario construction industry.

PubMed

Chen, Yuting; McCabe, Brenda; Hyatt, Douglas

2017-06-01

The construction industry has hit a plateau in terms of safety performance. Safety climate is regarded as a leading indicator of safety performance; however, relatively little safety climate research has been done in the Canadian construction industry. Safety climate may be geographically sensitive, thus it is necessary to examine how the construct of safety climate is defined and used to improve safety performance in different regions. On the other hand, more and more attention has been paid to job related stress in the construction industry. Previous research proposed that individual resilience may be associated with a better safety performance and may help employees manage stress. Unfortunately, few empirical research studies have examined this hypothesis. This paper aims to examine the role of safety climate and individual resilience in safety performance and job stress in the Canadian construction industry. The research was based on 837 surveys collected in Ontario between June 2015 and June 2016. Structural equation modeling (SEM) techniques were used to explore the impact of individual resilience and safety climate on physical safety outcomes and on psychological stress among construction workers. The results show that safety climate not only affected construction workers' safety performance but also indirectly affected their psychological stress. In addition, it was found that individual resilience had a direct negative impact on psychological stress but had no impact on physical safety outcomes. These findings highlight the roles of both organizational and individual factors in individual safety performance and in psychological well-being. Construction organizations need to not only monitor employees' safety performance, but also to assess their employees' psychological well-being. Promoting a positive safety climate together with developing training programs focusing on improving employees' psychological health - especially post-trauma psychological health - can improve the safety performance of an organization. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

An Approach to Building a Learning Management System that Emphasizes on Incorporating Individualized Dissemination with Intelligent Tutoring

NASA Astrophysics Data System (ADS)

Ghosh, Sreya

2017-02-01

This article proposes a new six-model architecture for an intelligent tutoring system to be incorporated in a learning management system with domain-independence feature and individualized dissemination. The present six model architecture aims to simulate a human tutor. Some recent extensions of using intelligent tutoring system (ITS) explores learning management systems to behave as a real teacher during a teaching-learning process, by taking care of, mainly, the dynamic response system. However, the present paper argues that to mimic a human teacher it needs not only the dynamic response but also the incorporation of the teacher's dynamic review of students' performance and keeping track of their current level of understanding. Here, the term individualization has been used to refer to tailor making of contents and its dissemination fitting to the individual needs and capabilities of learners who is taking a course online and is subjected to teaching in absentia. This paper describes how the individual models of the proposed architecture achieves the features of ITS.

Truncated Lévy flights and agenda-based mobility are useful for the assessment of personal human exposure.

PubMed

Schlink, Uwe; Ragas, Ad M J

2011-01-01

Receptor-oriented approaches can assess the individual-specific exposure to air pollution. In such an individual-based model we analyse the impact of human mobility to the personal exposure that is perceived by individuals simulated in an exemplified urban area. The mobility models comprise random walk (reference point mobility, RPM), truncated Lévy flights (TLF), and agenda-based walk (RPMA). We describe and review the general concepts and provide an inter-comparison of these concepts. Stationary and ergodic behaviour are explained and applied as well as performance criteria for a comparative evaluation of the investigated algorithms. We find that none of the studied algorithm results in purely random trajectories. TLF and RPMA prove to be suitable for human mobility modelling, because they provide conditions for very individual-specific trajectories and exposure. Suggesting these models we demonstrate the plausibility of their results for exposure to air-borne benzene and the combined exposure to benzene and nonane. Copyright © 2011 Elsevier Ltd. All rights reserved.

Constraints on decision making: implications from genetics, personality, and addiction.

PubMed

Baker, Travis E; Stockwell, Tim; Holroyd, Clay B

2013-09-01

An influential neurocomputational theory of the biological mechanisms of decision making, the "basal ganglia go/no-go model," holds that individual variability in decision making is determined by differences in the makeup of a striatal system for approach and avoidance learning. The model has been tested empirically with the probabilistic selection task (PST), which determines whether individuals learn better from positive or negative feedback. In accordance with the model, in the present study we examined whether an individual's ability to learn from positive and negative reinforcement can be predicted by genetic factors related to the midbrain dopamine system. We also asked whether psychiatric and personality factors related to substance dependence and dopamine affect PST performance. Although we found characteristics that predicted individual differences in approach versus avoidance learning, these observations were qualified by additional findings that appear inconsistent with the predictions of the go/no-go model. These results highlight a need for future research to validate the PST as a measure of basal ganglia reward learning.

Social capital and administrative contextual determinants of lack of access to a regular doctor: a multilevel analysis in southern Sweden.

PubMed

Lindström, Martin; Axén, Elin; Lindström, Christine; Beckman, Anders; Moghaddassi, Mahnaz; Merlo, Juan

2006-12-01

The aim of this study was to investigate the influence of contextual (social capital and administrative/neo-materialist) and individual factors on lack of access to a regular doctor. The 2000 public health survey in Scania is a cross-sectional study. A total of 13,715 persons answered a postal questionnaire, which is 59% of the random sample. A multilevel logistic regression model, with individuals at the first level and municipalities at the second, was performed. The effect (intra-class correlations, cross-level modification and odds ratios) of individual and municipality (social capital and health care district) factors on lack of access to a regular doctor was analysed using simulation method. The Deviance Information Criterion (DIC) was used as information criterion for the models. The second level municipality variance in lack of access to a regular doctor is substantial even in the final models with all individual and contextual variables included. The model that results in the largest reduction in DIC is the model including age, sex and individual social participation (which is a network aspect of social capital), but the models which include administrative and social capital second level factors also reduced the DIC values. This study suggests that both administrative health care district and social capital may partly explain the individual's self reported lack of access to a regular doctor.

SUMMA and Model Mimicry: Understanding Differences Among Land Models

NASA Astrophysics Data System (ADS)

Nijssen, B.; Nearing, G. S.; Ou, G.; Clark, M. P.

2016-12-01

Model inter-comparison and model ensemble experiments suffer from an inability to explain the mechanisms behind differences in model outcomes. We can clearly demonstrate that the models are different, but we cannot necessarily identify the reasons why, because most models exhibit myriad differences in process representations, model parameterizations, model parameters and numerical solution methods. This inability to identify the reasons for differences in model performance hampers our understanding and limits model improvement, because we cannot easily identify the most promising paths forward. We have developed the Structure for Unifying Multiple Modeling Alternatives (SUMMA) to allow for controlled experimentation with model construction, numerical techniques, and parameter values and therefore isolate differences in model outcomes to specific choices during the model development process. In developing SUMMA, we recognized that hydrologic models can be thought of as individual instantiations of a master modeling template that is based on a common set of conservation equations for energy and water. Given this perspective, SUMMA provides a unified approach to hydrologic modeling that integrates different modeling methods into a consistent structure with the ability to instantiate alternative hydrologic models at runtime. Here we employ SUMMA to revisit a previous multi-model experiment and demonstrate its use for understanding differences in model performance. Specifically, we implement SUMMA to mimic the spread of behaviors exhibited by the land models that participated in the Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) and draw conclusions about the relative performance of specific model parameterizations for water and energy fluxes through the soil-vegetation continuum. SUMMA's ability to mimic the spread of model ensembles and the behavior of individual models can be an important tool in focusing model development and improvement efforts.

Use of posterior predictive checks as an inferential tool for investigating individual heterogeneity in animal population vital rates

PubMed Central

Chambert, Thierry; Rotella, Jay J; Higgs, Megan D

2014-01-01

The investigation of individual heterogeneity in vital rates has recently received growing attention among population ecologists. Individual heterogeneity in wild animal populations has been accounted for and quantified by including individually varying effects in models for mark–recapture data, but the real need for underlying individual effects to account for observed levels of individual variation has recently been questioned by the work of Tuljapurkar et al. (Ecology Letters, 12, 93, 2009) on dynamic heterogeneity. Model-selection approaches based on information criteria or Bayes factors have been used to address this question. Here, we suggest that, in addition to model-selection, model-checking methods can provide additional important insights to tackle this issue, as they allow one to evaluate a model's misfit in terms of ecologically meaningful measures. Specifically, we propose the use of posterior predictive checks to explicitly assess discrepancies between a model and the data, and we explain how to incorporate model checking into the inferential process used to assess the practical implications of ignoring individual heterogeneity. Posterior predictive checking is a straightforward and flexible approach for performing model checks in a Bayesian framework that is based on comparisons of observed data to model-generated replications of the data, where parameter uncertainty is incorporated through use of the posterior distribution. If discrepancy measures are chosen carefully and are relevant to the scientific context, posterior predictive checks can provide important information allowing for more efficient model refinement. We illustrate this approach using analyses of vital rates with long-term mark–recapture data for Weddell seals and emphasize its utility for identifying shortfalls or successes of a model at representing a biological process or pattern of interest. We show how posterior predictive checks can be used to strengthen inferences in ecological studies. We demonstrate the application of this method on analyses dealing with the question of individual reproductive heterogeneity in a population of Antarctic pinnipeds. PMID:24834335

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

Oberije, Cary, E-mail: cary.oberije@maastro.nl; De Ruysscher, Dirk; Universitaire Ziekenhuizen Leuven, KU Leuven

Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing andmore » validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Methods and Materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability ( (www.predictcancer.org)). The data set can be downloaded at (https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048). Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.« less

Blending Individual and Group Assessment: A Model for Measuring Student Performance

ERIC Educational Resources Information Center

Reiser, Elana

2017-01-01

Two sections of a college discrete mathematics class were taught using cooperative learning techniques throughout the semester. The 33 students attending these sections were randomly assigned into groups of three. Their final examination consisted of an individual and group blended examination where students worked in their groups and discussed…

Clinical Cognition and Diagnostic Error: Applications of a Dual Process Model of Reasoning

ERIC Educational Resources Information Center

Croskerry, Pat

2009-01-01

Both systemic and individual factors contribute to missed or delayed diagnoses. Among the multiple factors that impact clinical performance of the individual, the caliber of cognition is perhaps the most relevant and deserves our attention and understanding. In the last few decades, cognitive psychologists have gained substantial insights into the…

A Framework for Implementing Individualized Self-Regulated Learning Strategies in the Classroom

ERIC Educational Resources Information Center

Ness, Bryan M.; Middleton, Michael J.

2012-01-01

Self-regulated learning (SRL) is a conceptual model that can be used to design and implement individualized learning strategies for students with learning disabilities. Students who self-regulate their learning engage in planning, performance, and self-evaluation during academic tasks. This article highlights one approach for teaching SRL skills…

Consistent Individual Differences Drive Collective Behavior and Group Functioning of Schooling Fish.

PubMed

Jolles, Jolle W; Boogert, Neeltje J; Sridhar, Vivek H; Couzin, Iain D; Manica, Andrea

2017-09-25

The ubiquity of consistent inter-individual differences in behavior ("animal personalities") [1, 2] suggests that they might play a fundamental role in driving the movements and functioning of animal groups [3, 4], including their collective decision-making, foraging performance, and predator avoidance. Despite increasing evidence that highlights their importance [5-16], we still lack a unified mechanistic framework to explain and to predict how consistent inter-individual differences may drive collective behavior. Here we investigate how the structure, leadership, movement dynamics, and foraging performance of groups can emerge from inter-individual differences by high-resolution tracking of known behavioral types in free-swimming stickleback (Gasterosteus aculeatus) shoals. We show that individual's propensity to stay near others, measured by a classic "sociability" assay, was negatively linked to swim speed across a range of contexts, and predicted spatial positioning and leadership within groups as well as differences in structure and movement dynamics between groups. In turn, this trait, together with individual's exploratory tendency, measured by a classic "boldness" assay, explained individual and group foraging performance. These effects of consistent individual differences on group-level states emerged naturally from a generic model of self-organizing groups composed of individuals differing in speed and goal-orientedness. Our study provides experimental and theoretical evidence for a simple mechanism to explain the emergence of collective behavior from consistent individual differences, including variation in the structure, leadership, movement dynamics, and functional capabilities of groups, across social and ecological scales. In addition, we demonstrate individual performance is conditional on group composition, indicating how social selection may drive behavioral differentiation between individuals. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Neural network submodel as an abstraction tool: relating network performance to combat outcome

NASA Astrophysics Data System (ADS)

Jablunovsky, Greg; Dorman, Clark; Yaworsky, Paul S.

2000-06-01

Simulation of Command and Control (C2) networks has historically emphasized individual system performance with little architectural context or credible linkage to `bottom- line' measures of combat outcomes. Renewed interest in modeling C2 effects and relationships stems from emerging network intensive operational concepts. This demands improved methods to span the analytical hierarchy between C2 system performance models and theater-level models. Neural network technology offers a modeling approach that can abstract the essential behavior of higher resolution C2 models within a campaign simulation. The proposed methodology uses off-line learning of the relationships between network state and campaign-impacting performance of a complex C2 architecture and then approximation of that performance as a time-varying parameter in an aggregated simulation. Ultimately, this abstraction tool offers an increased fidelity of C2 system simulation that captures dynamic network dependencies within a campaign context.

What could they have been thinking? How sociotechnical system design influences cognition: a case study of the Stockwell shooting.

PubMed

Jenkins, Daniel P; Salmon, Paul M; Stanton, Neville A; Walker, Guy H; Rafferty, Laura

2011-02-01

Understanding why an individual acted in a certain way is of fundamental importance to the human factors community, especially when the choice of action results in an undesirable outcome. This challenge is typically tackled by applying retrospective interview techniques to generate models of what happened, recording deviations from a 'correct procedure'. While such approaches may have great utility in tightly constrained procedural environments, they are less applicable in complex sociotechnical systems that require individuals to modify procedures in real time to respond to a changing environment. For complex sociotechnical systems, a formative approach is required that maps the information available to the individual and considers its impact on performance and action. A context-specific, activity-independent, constraint-based model forms the basis of this approach. To illustrate, an example of the Stockwell shooting is used, where an innocent man, mistaken for a suicide bomber, was shot dead. Transferable findings are then presented. STATEMENT OF RELEVANCE: This paper presents a new approach that can be applied proactively to consider how sociotechnical system design, and the information available to an individual, can affect their performance. The approach is proposed to be complementary to the existing tools in the mental models phase of the cognitive work analysis framework.

Validation of simplified centre of mass models during gait in individuals with chronic stroke.

PubMed

Huntley, Andrew H; Schinkel-Ivy, Alison; Aqui, Anthony; Mansfield, Avril

2017-10-01

The feasibility of using a multiple segment (full-body) kinematic model in clinical gait assessment is difficult when considering obstacles such as time and cost constraints. While simplified gait models have been explored in healthy individuals, no such work to date has been conducted in a stroke population. The aim of this study was to quantify the errors of simplified kinematic models for chronic stroke gait assessment. Sixteen individuals with chronic stroke (>6months), outfitted with full body kinematic markers, performed a series of gait trials. Three centre of mass models were computed: (i) 13-segment whole-body model, (ii) 3 segment head-trunk-pelvis model, and (iii) 1 segment pelvis model. Root mean squared error differences were compared between models, along with correlations to measures of stroke severity. Error differences revealed that, while both models were similar in the mediolateral direction, the head-trunk-pelvis model had less error in the anteroposterior direction and the pelvis model had less error in the vertical direction. There was some evidence that the head-trunk-pelvis model error is influenced in the mediolateral direction for individuals with more severe strokes, as a few significant correlations were observed between the head-trunk-pelvis model and measures of stroke severity. These findings demonstrate the utility and robustness of the pelvis model for clinical gait assessment in individuals with chronic stroke. Low error in the mediolateral and vertical directions is especially important when considering potential stability analyses during gait for this population, as lateral stability has been previously linked to fall risk. Copyright © 2017 Elsevier Ltd. All rights reserved.

Working under a clinic-level quality incentive: primary care clinicians' perceptions.

PubMed

Greene, Jessica; Kurtzman, Ellen T; Hibbard, Judith H; Overton, Valerie

2015-01-01

A key consideration in designing pay-for-performance programs is determining what entity the incentive should be awarded to-individual clinicians or to groups of clinicians working in teams. Some argue that team-level incentives, in which clinicians who are part of a team receive the same incentive based on the team's performance, are most effective; others argue for the efficacy of clinician-level incentives. This study examines primary care clinicians' perceptions of a team-based quality incentive awarded at the clinic level. This research was conducted with Fairview Health Services, where 40% of the primary care compensation model was based on clinic-level quality performance. We conducted 48 in-depth interviews to explore clinicians' perceptions of the clinic-level incentive, as well as an online survey of 150 clinicians (response rate 56%) to investigate which entity the clinicians would consider optimal to target for quality incentives. Clinicians reported the strengths of the clinic-based quality incentive were quality improvement for the team and less patient "dumping," or shifting patients with poor outcomes to other clinicians. The weaknesses were clinicians' lack of control and colleagues riding the coattails of higher performers. There were mixed reports on the model's impact on team dynamics. Although clinicians reported greater interaction with colleagues, some described an increase in tension. Most clinicians surveyed (73%) believed that there should be a mix of clinic and individual-level incentives to maintain collaboration and recognize individual performance. The study highlights the important advantages and disadvantages of using incentives based upon clinic-level performance. Future research should test whether hybrid incentives that mix group and individual incentives can maintain some of the best elements of each design while mitigating the negative impacts. © 2015 Annals of Family Medicine, Inc.

Smooth individual level covariates adjustment in disease mapping.

PubMed

Huque, Md Hamidul; Anderson, Craig; Walton, Richard; Woolford, Samuel; Ryan, Louise

2018-05-01

Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparing colon cancer outcomes: The impact of low hospital case volume and case-mix adjustment.

PubMed

Fischer, C; Lingsma, H F; van Leersum, N; Tollenaar, R A E M; Wouters, M W; Steyerberg, E W

2015-08-01

When comparing performance across hospitals it is essential to consider the noise caused by low hospital case volume and to perform adequate case-mix adjustment. We aimed to quantify the role of noise and case-mix adjustment on standardized postoperative mortality and anastomotic leakage (AL) rates. We studied 13,120 patients who underwent colon cancer resection in 85 Dutch hospitals. We addressed differences between hospitals in postoperative mortality and AL, using fixed (ignoring noise) and random effects (incorporating noise) logistic regression models with general and additional, disease specific, case-mix adjustment. Adding disease specific variables improved the performance of the case-mix adjustment models for postoperative mortality (c-statistic increased from 0.77 to 0.81). The overall variation in standardized mortality ratios was similar, but some individual hospitals changed considerably. For the standardized AL rates the performance of the adjustment models was poor (c-statistic 0.59 and 0.60) and overall variation was small. Most of the observed variation between hospitals was actually noise. Noise had a larger effect on hospital performance than extended case-mix adjustment, although some individual hospital outcome rates were affected by more detailed case-mix adjustment. To compare outcomes between hospitals it is crucial to consider noise due to low hospital case volume with a random effects model. Copyright © 2015 Elsevier Ltd. All rights reserved.

EEG Characteristic Extraction Method of Listening Music and Objective Estimation Method Based on Latency Structure Model in Individual Characteristics

NASA Astrophysics Data System (ADS)

Ito, Shin-Ichi; Mitsukura, Yasue; Nakamura Miyamura, Hiroko; Saito, Takafumi; Fukumi, Minoru

EEG is characterized by the unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The real-coded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to a real EEG data. The result of our experiment shows the effectiveness of the proposed method.

Quality of asthma care under different primary care models in Canada: a population-based study.

PubMed

To, Teresa; Guan, Jun; Zhu, Jingqin; Lougheed, M Diane; Kaplan, Alan; Tamari, Itamar; Stanbrook, Matthew B; Simatovic, Jacqueline; Feldman, Laura; Gershon, Andrea S

2015-02-14

Previous research has shown variations in quality of care and patient outcomes under different primary care models. The objective of this study was to use previously validated, evidence-based performance indicators to measure quality of asthma care over time and to compare quality of care between different primary care models. Data were obtained for years 2006 to 2010 from the Ontario Asthma Surveillance Information System, which uses health administrative databases to track individuals with asthma living in the province of Ontario, Canada. Individuals with asthma (n=1,813,922) were divided into groups based on the practice model of their primary care provider (i.e., fee-for-service, blended fee-for-service, blended capitation). Quality of asthma care was measured using six validated, evidence-based asthma care performance indicators. All of the asthma performance indicators improved over time within each of the primary care models. Compared to the traditional fee-for-service model, the blended fee-for-service and blended capitation models had higher use of spirometry for asthma diagnosis and monitoring, higher rates of inhaled corticosteroid prescription, and lower outpatient claims. Emergency department visits were lowest in the blended fee-for-service group. Quality of asthma care improved over time within each of the primary care models. However, the amount by which they improved differed between the models. The newer primary care models (i.e., blended fee-for-service, blended capitation) appear to provide better quality of asthma care compared to the traditional fee-for-service model.

BinQuasi: a peak detection method for ChIP-sequencing data with biological replicates.

PubMed

Goren, Emily; Liu, Peng; Wang, Chao; Wang, Chong

2018-04-19

ChIP-seq experiments that are aimed at detecting DNA-protein interactions require biological replication to draw inferential conclusions, however there is no current consensus on how to analyze ChIP-seq data with biological replicates. Very few methodologies exist for the joint analysis of replicated ChIP-seq data, with approaches ranging from combining the results of analyzing replicates individually to joint modeling of all replicates. Combining the results of individual replicates analyzed separately can lead to reduced peak classification performance compared to joint modeling. Currently available methods for joint analysis may fail to control the false discovery rate at the nominal level. We propose BinQuasi, a peak caller for replicated ChIP-seq data, that jointly models biological replicates using a generalized linear model framework and employs a one-sided quasi-likelihood ratio test to detect peaks. When applied to simulated data and real datasets, BinQuasi performs favorably compared to existing methods, including better control of false discovery rate than existing joint modeling approaches. BinQuasi offers a flexible approach to joint modeling of replicated ChIP-seq data which is preferable to combining the results of replicates analyzed individually. Source code is freely available for download at https://cran.r-project.org/package=BinQuasi, implemented in R. pliu@iastate.edu or egoren@iastate.edu. Supplementary material is available at Bioinformatics online.

Kinematic predictors of star excursion balance test performance in individuals with chronic ankle instability.

PubMed

Hoch, Matthew C; Gaven, Stacey L; Weinhandl, Joshua T

2016-06-01

The Star Excursion Balance Test has identified dynamic postural control deficits in individuals with chronic ankle instability. While kinematic predictors of Star Excursion Balance Test performance have been evaluated in healthy individuals, this has not been thoroughly examined in individuals with chronic ankle instability. Fifteen individuals with chronic ankle instability completed the anterior reach direction of the Star Excursion Balance Test and weight-bearing dorsiflexion assessments. Maximum reach distances on the Star Excursion Balance Test were measured in cm and normalized to leg length. Three-dimensional trunk, hip, knee, and ankle motion of the stance limb were recorded during each anterior reach trial using a motion capture system. Sagittal, frontal, and transverse plane displacement observed from trial initiation to the point of maximum reach was calculated for each joint or segment and averaged for analysis. Pearson product-moment correlations were performed to examine the relationships between kinematic variables, maximal reach, and weight-bearing dorsiflexion. A backward multiple linear regression model was developed with maximal reach as the criterion variable and kinematic variables as predictors. Frontal plane displacement of the trunk, hip, and ankle and sagittal plane knee displacement were entered into the analysis. The final model (p=0.004) included all three frontal plane variables and explained 81% of the variance in maximal reach. Maximal reach distance and several kinematic variables were significantly related to weight-bearing dorsiflexion. Individuals with chronic ankle instability who demonstrated greater lateral trunk displacement toward the stance limb, hip adduction, and ankle eversion achieved greater maximal reach. Copyright © 2016. Published by Elsevier Ltd.

A Separable Two-Dimensional Random Field Model of Binary Response Data from Multi-Day Behavioral Experiments.

PubMed

Malem-Shinitski, Noa; Zhang, Yingzhuo; Gray, Daniel T; Burke, Sara N; Smith, Anne C; Barnes, Carol A; Ba, Demba

2018-04-18

The study of learning in populations of subjects can provide insights into the changes that occur in the brain with aging, drug intervention, and psychiatric disease. We introduce a separable two-dimensional (2D) random field (RF) model for analyzing binary response data acquired during the learning of object-reward associations across multiple days. The method can quantify the variability of performance within a day and across days, and can capture abrupt changes in learning. We apply the method to data from young and aged macaque monkeys performing a reversal-learning task. The method provides an estimate of performance within a day for each age group, and a learning rate across days for each monkey. We find that, as a group, the older monkeys require more trials to learn the object discriminations than do the young monkeys, and that the cognitive flexibility of the younger group is higher. We also use the model estimates of performance as features for clustering the monkeys into two groups. The clustering results in two groups that, for the most part, coincide with those formed by the age groups. Simulation studies suggest that clustering captures inter-individual differences in performance levels. In comparison with generalized linear models, this method is better able to capture the inherent two-dimensional nature of the data and find between group differences. Applied to binary response data from groups of individuals performing multi-day behavioral experiments, the model discriminates between-group differences and identifies subgroups. Copyright © 2018. Published by Elsevier B.V.

Proceedings of the Symposium on Psychology in the Department of Defense (12th) Held in Colorado Springs, Colorado on 18-20 April 1990

DTIC Science & Technology

1990-04-01

structure or level of performance. To the extant this increased motivation result in greater...application users and not power users, they will quickly abandon the use of these models in favor of those that require less of a data entry burden, even if... behavior in Individuals is best measured by convergence over time of the value structures of the individual and those of a model group (e.g., tu•ity

Source-Modeling Auditory Processes of EEG Data Using EEGLAB and Brainstorm.

PubMed

Stropahl, Maren; Bauer, Anna-Katharina R; Debener, Stefan; Bleichner, Martin G

2018-01-01

Electroencephalography (EEG) source localization approaches are often used to disentangle the spatial patterns mixed up in scalp EEG recordings. However, approaches differ substantially between experiments, may be strongly parameter-dependent, and results are not necessarily meaningful. In this paper we provide a pipeline for EEG source estimation, from raw EEG data pre-processing using EEGLAB functions up to source-level analysis as implemented in Brainstorm. The pipeline is tested using a data set of 10 individuals performing an auditory attention task. The analysis approach estimates sources of 64-channel EEG data without the prerequisite of individual anatomies or individually digitized sensor positions. First, we show advanced EEG pre-processing using EEGLAB, which includes artifact attenuation using independent component analysis (ICA). ICA is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals and is further a powerful tool to attenuate stereotypical artifacts (e.g., eye movements or heartbeat). Data submitted to ICA are pre-processed to facilitate good-quality decompositions. Aiming toward an objective approach on component identification, the semi-automatic CORRMAP algorithm is applied for the identification of components representing prominent and stereotypic artifacts. Second, we present a step-wise approach to estimate active sources of auditory cortex event-related processing, on a single subject level. The presented approach assumes that no individual anatomy is available and therefore the default anatomy ICBM152, as implemented in Brainstorm, is used for all individuals. Individual noise modeling in this dataset is based on the pre-stimulus baseline period. For EEG source modeling we use the OpenMEEG algorithm as the underlying forward model based on the symmetric Boundary Element Method (BEM). We then apply the method of dynamical statistical parametric mapping (dSPM) to obtain physiologically plausible EEG source estimates. Finally, we show how to perform group level analysis in the time domain on anatomically defined regions of interest (auditory scout). The proposed pipeline needs to be tailored to the specific datasets and paradigms. However, the straightforward combination of EEGLAB and Brainstorm analysis tools may be of interest to others performing EEG source localization.

Detection of food intake from swallowing sequences by supervised and unsupervised methods.

PubMed

Lopez-Meyer, Paulo; Makeyev, Oleksandr; Schuckers, Stephanie; Melanson, Edward L; Neuman, Michael R; Sazonov, Edward

2010-08-01

Studies of food intake and ingestive behavior in free-living conditions most often rely on self-reporting-based methods that can be highly inaccurate. Methods of Monitoring of Ingestive Behavior (MIB) rely on objective measures derived from chewing and swallowing sequences and thus can be used for unbiased study of food intake with free-living conditions. Our previous study demonstrated accurate detection of food intake in simple models relying on observation of both chewing and swallowing. This article investigates methods that achieve comparable accuracy of food intake detection using only the time series of swallows and thus eliminating the need for the chewing sensor. The classification is performed for each individual swallow rather than for previously used time slices and thus will lead to higher accuracy in mass prediction models relying on counts of swallows. Performance of a group model based on a supervised method (SVM) is compared to performance of individual models based on an unsupervised method (K-means) with results indicating better performance of the unsupervised, self-adapting method. Overall, the results demonstrate that highly accurate detection of intake of foods with substantially different physical properties is possible by an unsupervised system that relies on the information provided by the swallowing alone.

Detection of Food Intake from Swallowing Sequences by Supervised and Unsupervised Methods

PubMed Central

Lopez-Meyer, Paulo; Makeyev, Oleksandr; Schuckers, Stephanie; Melanson, Edward L.; Neuman, Michael R.; Sazonov, Edward

2010-01-01

Studies of food intake and ingestive behavior in free-living conditions most often rely on self-reporting-based methods that can be highly inaccurate. Methods of Monitoring of Ingestive Behavior (MIB) rely on objective measures derived from chewing and swallowing sequences and thus can be used for unbiased study of food intake with free-living conditions. Our previous study demonstrated accurate detection of food intake in simple models relying on observation of both chewing and swallowing. This article investigates methods that achieve comparable accuracy of food intake detection using only the time series of swallows and thus eliminating the need for the chewing sensor. The classification is performed for each individual swallow rather than for previously used time slices and thus will lead to higher accuracy in mass prediction models relying on counts of swallows. Performance of a group model based on a supervised method (SVM) is compared to performance of individual models based on an unsupervised method (K-means) with results indicating better performance of the unsupervised, self-adapting method. Overall, the results demonstrate that highly accurate detection of intake of foods with substantially different physical properties is possible by an unsupervised system that relies on the information provided by the swallowing alone. PMID:20352335

Evaluation of Student Performance through a Multidimensional Finite Mixture IRT Model.

PubMed

Bacci, Silvia; Bartolucci, Francesco; Grilli, Leonardo; Rampichini, Carla

2017-01-01

In the Italian academic system, a student can enroll for an exam immediately after the end of the teaching period or can postpone it; in this second case the exam result is missing. We propose an approach for the evaluation of a student performance throughout the course of study, accounting also for nonattempted exams. The approach is based on an item response theory model that includes two discrete latent variables representing student performance and priority in selecting the exams to take. We explicitly account for nonignorable missing observations as the indicators of attempted exams also contribute to measure the performance (within-item multidimensionality). The model also allows for individual covariates in its structural part.

Using sensors to measure activity in people with stroke.

PubMed

Fulk, George D; Sazonov, Edward

2011-01-01

The purpose of this study was to determine the ability of a novel shoe-based sensor that uses accelerometers, pressure sensors, and pattern recognition with a support vector machine (SVM) to accurately identify sitting, standing, and walking postures in people with stroke. Subjects with stroke wore the shoe-based sensor while randomly assuming 3 main postures: sitting, standing, and walking. A SVM classifier was used to train and validate the data to develop individual and group models, which were tested for accuracy, recall, and precision. Eight subjects participated. Both individual and group models were able to accurately identify the different postures (99.1% to 100% individual models and 76.9% to 100% group models). Recall and precision were also high for both individual (0.99 to 1.00) and group (0.82 to 0.99) models. The unique combination of accelerometer and pressure sensors built into the shoe was able to accurately identify postures. This shoe sensor could be used to provide accurate information on community performance of activities in people with stroke as well as provide behavioral enhancing feedback as part of a telerehabilitation intervention.

Heavy physician workloads: impact on physician attitudes and outcomes.

PubMed

Williams, Eric S; Rondeau, Kent V; Xiao, Qian; Francescutti, Louis H

2007-11-01

The intensity of physician workload has been increasing with the well-documented changes in the financing, organization and delivery of care. It is possible that these stressors have reached a point where they pose a serious policy issue for the entire healthcare system through their diminution of physician's ability to effectively interact with patients as they are burned out, stressed and dissatisfied. This policy question is framed in a conceptual model linking workloads with five key outcomes (patient care quality, individual performance, absenteeism, turnover and organizational performance) mediated by physician stress and satisfaction. This model showed a good fit to the data in a structural equation analysis. Ten of the 12 hypothesized pathways between variables were significant and supported the mediating role of stress and satisfaction. These results suggest that workloads, stress and satisfaction have significant and material impacts on patient care quality, individual performance, absenteeism, turnover and organizational performance. Implications of these results and directions for future research are discussed.

A longitudinal cross-level model of leader and salesperson influences on sales force technology use and performance.

PubMed

Mathieu, John; Ahearne, Michael; Taylor, Scott R

2007-03-01

The authors examined the influence of the introduction of a new suite of technology tools on the performance of 592 salespersons. They hypothesized that the salespersons' work experience would have a negative effect on their technology self-efficacy, which in turn would relate positively to their use of technology. Sales performance was hypothesized to be positively related to both past performance and the use of new technology tools. Further, the authors hypothesized that leaders' commitment to sales technology would enhance salespersons' technology self-efficacy and usage, and leaders' empowering behaviors would influence salespersons' technology self-efficacy and moderate the individual-level relationships. Hierarchical linear modeling analyses confirmed all of the hypothesized individual-level relationships and most of the cross-level relationships stemming from average leader behaviors. In particular, empowering leadership exhibited multiple cross-level interactions, as anticipated. Results are discussed in terms of the importance of social-psychological factors related to the success of sales force technology interventions. (c) 2007 APA, all rights reserved.

Dynamic aspects of voluntary turnover: an integrated approach to curvilinearity in the performance-turnover relationship.

PubMed

Becker, William J; Cropanzano, Russell

2011-03-01

Previous research pertaining to job performance and voluntary turnover has been guided by 2 distinct theoretical perspectives. First, the push-pull model proposes that there is a quadratic or curvilinear relationship existing between these 2 variables. Second, the unfolding model of turnover posits that turnover is a dynamic process and that a downward performance change may increase the likelihood of organizational separation. Drawing on decision theory, we propose and test an integrative framework. This approach incorporates both of these earlier models. Specifically, we argue that individuals are most likely to voluntarily exit when they are below-average performers who are also experiencing a downward performance change. Furthermore, the interaction between this downward change and performance partially accounts for the curvilinear relationship proposed by the push-pull model. Findings from a longitudinal field study supported this integrative theory. PsycINFO Database Record (c) 2011 APA, all rights reserved.

Development and Validation of the PREMM5 Model for Comprehensive Risk Assessment of Lynch Syndrome.

PubMed

Kastrinos, Fay; Uno, Hajime; Ukaegbu, Chinedu; Alvero, Carmelita; McFarland, Ashley; Yurgelun, Matthew B; Kulke, Matthew H; Schrag, Deborah; Meyerhardt, Jeffrey A; Fuchs, Charles S; Mayer, Robert J; Ng, Kimmie; Steyerberg, Ewout W; Syngal, Sapna

2017-07-01

Purpose Current Lynch syndrome (LS) prediction models quantify the risk to an individual of carrying a pathogenic germline mutation in three mismatch repair (MMR) genes: MLH1, MSH2, and MSH6. We developed a new prediction model, PREMM 5 , that incorporates the genes PMS2 and EPCAM to provide comprehensive LS risk assessment. Patients and Methods PREMM 5 was developed to predict the likelihood of a mutation in any of the LS genes by using polytomous logistic regression analysis of clinical and germline data from 18,734 individuals who were tested for all five genes. Predictors of mutation status included sex, age at genetic testing, and proband and family cancer histories. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUC), and clinical impact was determined by decision curve analysis; comparisons were made to the existing PREMM 1,2,6 model. External validation of PREMM 5 was performed in a clinic-based cohort of 1,058 patients with colorectal cancer. Results Pathogenic mutations were detected in 1,000 (5%) of 18,734 patients in the development cohort; mutations included MLH1 (n = 306), MSH2 (n = 354), MSH6 (n = 177), PMS2 (n = 141), and EPCAM (n = 22). PREMM 5 distinguished carriers from noncarriers with an AUC of 0.81 (95% CI, 0.79 to 0.82), and performance was similar in the validation cohort (AUC, 0.83; 95% CI, 0.75 to 0.92). Prediction was more difficult for PMS2 mutations (AUC, 0.64; 95% CI, 0.60 to 0.68) than for other genes. Performance characteristics of PREMM 5 exceeded those of PREMM 1,2,6 . Decision curve analysis supported germline LS testing for PREMM 5 scores ≥ 2.5%. Conclusion PREMM 5 provides comprehensive risk estimation of all five LS genes and supports LS genetic testing for individuals with scores ≥ 2.5%. At this threshold, PREMM 5 provides performance that is superior to the existing PREMM 1,2,6 model in the identification of carriers of LS, including those with weaker phenotypes and individuals unaffected by cancer.

Development and Validation of the PREMM5 Model for Comprehensive Risk Assessment of Lynch Syndrome

PubMed Central

Uno, Hajime; Ukaegbu, Chinedu; Alvero, Carmelita; McFarland, Ashley; Yurgelun, Matthew B.; Kulke, Matthew H.; Schrag, Deborah; Meyerhardt, Jeffrey A.; Fuchs, Charles S.; Mayer, Robert J.; Ng, Kimmie; Steyerberg, Ewout W.; Syngal, Sapna

2017-01-01

Purpose Current Lynch syndrome (LS) prediction models quantify the risk to an individual of carrying a pathogenic germline mutation in three mismatch repair (MMR) genes: MLH1, MSH2, and MSH6. We developed a new prediction model, PREMM5, that incorporates the genes PMS2 and EPCAM to provide comprehensive LS risk assessment. Patients and Methods PREMM5 was developed to predict the likelihood of a mutation in any of the LS genes by using polytomous logistic regression analysis of clinical and germline data from 18,734 individuals who were tested for all five genes. Predictors of mutation status included sex, age at genetic testing, and proband and family cancer histories. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUC), and clinical impact was determined by decision curve analysis; comparisons were made to the existing PREMM1,2,6 model. External validation of PREMM5 was performed in a clinic-based cohort of 1,058 patients with colorectal cancer. Results Pathogenic mutations were detected in 1,000 (5%) of 18,734 patients in the development cohort; mutations included MLH1 (n = 306), MSH2 (n = 354), MSH6 (n = 177), PMS2 (n = 141), and EPCAM (n = 22). PREMM5 distinguished carriers from noncarriers with an AUC of 0.81 (95% CI, 0.79 to 0.82), and performance was similar in the validation cohort (AUC, 0.83; 95% CI, 0.75 to 0.92). Prediction was more difficult for PMS2 mutations (AUC, 0.64; 95% CI, 0.60 to 0.68) than for other genes. Performance characteristics of PREMM5 exceeded those of PREMM1,2,6. Decision curve analysis supported germline LS testing for PREMM5 scores ≥ 2.5%. Conclusion PREMM5 provides comprehensive risk estimation of all five LS genes and supports LS genetic testing for individuals with scores ≥ 2.5%. At this threshold, PREMM5 provides performance that is superior to the existing PREMM1,2,6 model in the identification of carriers of LS, including those with weaker phenotypes and individuals unaffected by cancer. PMID:28489507

Electrostatic actuation and electromechanical switching behavior of one-dimensional nanostructures.

PubMed

Subramanian, Arunkumar; Alt, Andreas R; Dong, Lixin; Kratochvil, Bradley E; Bolognesi, Colombo R; Nelson, Bradley J

2009-10-27

We report on the electromechanical actuation and switching performance of nanoconstructs involving doubly clamped, individual multiwalled carbon nanotubes. Batch-fabricated, three-state switches with low ON-state voltages (6.7 V average) are demonstrated. A nanoassembly architecture that permits individual probing of one device at a time without crosstalk from other nanotubes, which are originally assembled in parallel, is presented. Experimental investigations into device performance metrics such as hysteresis, repeatability and failure modes are presented. Furthermore, current-driven shell etching is demonstrated as a tool to tune the nanomechanical clamping configuration, stiffness, and actuation voltage of fabricated devices. Computational models, which take into account the nonlinearities induced by stress-stiffening of 1-D nanowires at large deformations, are presented. Apart from providing accurate estimates of device performance, these models provide new insights into the extension of stable travel range in electrostatically actuated nanowire-based constructs as compared to their microscale counterparts.

Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning?

PubMed

Jaspers, Arne; De Beéck, Tim Op; Brink, Michel S; Frencken, Wouter G P; Staes, Filip; Davis, Jesse J; Helsen, Werner F

2018-05-01

Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. Training data were collected from 38 professional soccer players over 2 seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using 2 machine learning techniques, artificial neural networks and least absolute shrinkage and selection operator (LASSO) models, and 1 naive baseline method. The predictions were based on a large set of ELIs. Using each technique, 1 group model involving all players and 1 individual model for each player were constructed. These models' performance on predicting the reported RPE values for future training sessions was compared with the naive baseline's performance. Both the artificial neural network and LASSO models outperformed the baseline. In addition, the LASSO model made more accurate predictions for the RPE than did the artificial neural network model. Furthermore, decelerations were identified as important ELIs. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. Machine learning techniques may have added value in predicting RPE for future sessions to optimize training design and evaluation. These techniques may also be used in conjunction with expert knowledge to select key ELIs for load monitoring.

Individualized Biomathematical Modeling of Fatigue and Performance

DTIC Science & Technology

2008-05-29

NUMBER Fatigue and Performance 5b. GRANT NUMBER FA9550-06-1-0055 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Hans P.A. Van Dongen, Ph.D...5d. PROJECT NUMBER (Principal Investigator) 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES...Sleep and Performance Research Center 8. PERFORMING ORGANIZATION REPORT NUMBER Washington State University, Spokane P.O. Box 1495 Spokane, WA

Modeling Mode Choice Behavior Incorporating Household and Individual Sociodemographics and Travel Attributes Based on Rough Sets Theory

PubMed Central

Chen, Xuewu; Wei, Ming; Wu, Jingxian; Hou, Xianyao

2014-01-01

Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem reflected from the explanatory variables of determining the choices between alternatives. The paper applies the knowledge discovery technique of rough sets theory to model travel mode choices incorporating household and individual sociodemographics and travel information, and to identify the significance of each attribute. The study uses the detailed travel diary survey data of Changxing county which contains information on both household and individual travel behaviors for model estimation and evaluation. The knowledge is presented in the form of easily understood IF-THEN statements or rules which reveal how each attribute influences mode choice behavior. These rules are then used to predict travel mode choices from information held about previously unseen individuals and the classification performance is assessed. The rough sets model shows high robustness and good predictive ability. The most significant condition attributes identified to determine travel mode choices are gender, distance, household annual income, and occupation. Comparative evaluation with the MNL model also proves that the rough sets model gives superior prediction accuracy and coverage on travel mode choice modeling. PMID:25431585

Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping.

PubMed

Shafizadeh-Moghadam, Hossein; Valavi, Roozbeh; Shahabi, Himan; Chapi, Kamran; Shirzadi, Ataollah

2018-07-01

In this research, eight individual machine learning and statistical models are implemented and compared, and based on their results, seven ensemble models for flood susceptibility assessment are introduced. The individual models included artificial neural networks, classification and regression trees, flexible discriminant analysis, generalized linear model, generalized additive model, boosted regression trees, multivariate adaptive regression splines, and maximum entropy, and the ensemble models were Ensemble Model committee averaging (EMca), Ensemble Model confidence interval Inferior (EMciInf), Ensemble Model confidence interval Superior (EMciSup), Ensemble Model to estimate the coefficient of variation (EMcv), Ensemble Model to estimate the mean (EMmean), Ensemble Model to estimate the median (EMmedian), and Ensemble Model based on weighted mean (EMwmean). The data set covered 201 flood events in the Haraz watershed (Mazandaran province in Iran) and 10,000 randomly selected non-occurrence points. Among the individual models, the Area Under the Receiver Operating Characteristic (AUROC), which showed the highest value, belonged to boosted regression trees (0.975) and the lowest value was recorded for generalized linear model (0.642). On the other hand, the proposed EMmedian resulted in the highest accuracy (0.976) among all models. In spite of the outstanding performance of some models, nevertheless, variability among the prediction of individual models was considerable. Therefore, to reduce uncertainty, creating more generalizable, more stable, and less sensitive models, ensemble forecasting approaches and in particular the EMmedian is recommended for flood susceptibility assessment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Making sense of "consumer engagement" initiatives to improve health and health care: a conceptual framework to guide policy and practice.

PubMed

Mittler, Jessica N; Martsolf, Grant R; Telenko, Shannon J; Scanlon, Dennis P

2013-03-01

Policymakers and practitioners continue to pursue initiatives designed to engage individuals in their health and health care despite discordant views and mixed evidence regarding the ability to cultivate greater individual engagement that improves Americans' health and well-being and helps manage health care costs. There is limited and mixed evidence regarding the value of different interventions. Based on our involvement in evaluating various community-based consumer engagement initiatives and a targeted literature review of models of behavior change, we identified the need for a framework to classify the universe of consumer engagement initiatives toward advancing policymakers' and practitioners' knowledge of their value and fit in various contexts. We developed a framework that expanded our conceptualization of consumer engagement, building on elements of two common models, the individually focused transtheoretical model of behavior and the broader, multilevel social ecological model. Finally, we applied this framework to one community's existing consumer engagement program. Consumer engagement in health and health care refers to the performance of specific behaviors ("engaged behaviors") and/or an individual's capacity and motivation to perform these behaviors ("activation"). These two dimensions are related but distinct and thus should be differentiated. The framework creates four classification schemas, by (1) targeted behavior types (self-management, health care encounter, shopping, and health behaviors) and by (2) individual, (3) group, and (4) community dimensions. Our example illustrates that the framework can systematically classify a variety of consumer engagement programs, and that this exercise and resulting characterization can provide a structured way to consider the program and how its components fit program goals both individually and collectively. Applying the framework could help advance the field by making policymakers and practitioners aware of the wide range of approaches, providing a structured way to organize and characterize interventions retrospectively, and helping them consider how they can meet the program's goals both individually and collectively. © 2013 Milbank Memorial Fund.

Making Sense of “Consumer Engagement” Initiatives to Improve Health and Health Care: A Conceptual Framework to Guide Policy and Practice

PubMed Central

Mittler, Jessica N; Martsolf, Grant R; Telenko, Shannon J; Scanlon, Dennis P

2013-01-01

Context Policymakers and practitioners continue to pursue initiatives designed to engage individuals in their health and health care despite discordant views and mixed evidence regarding the ability to cultivate greater individual engagement that improves Americans’ health and well-being and helps manage health care costs. There is limited and mixed evidence regarding the value of different interventions. Methods Based on our involvement in evaluating various community-based consumer engagement initiatives and a targeted literature review of models of behavior change, we identified the need for a framework to classify the universe of consumer engagement initiatives toward advancing policymakers' and practitioners' knowledge of their value and fit in various contexts. We developed a framework that expanded our conceptualization of consumer engagement, building on elements of two common models, the individually focused transtheoretical model of behavior and the broader, multilevel social ecological model. Finally, we applied this framework to one community's existing consumer engagement program. Findings Consumer engagement in health and health care refers to the performance of specific behaviors (“engaged behaviors”) and/or an individual's capacity and motivation to perform these behaviors (“activation”). These two dimensions are related but distinct and thus should be differentiated. The framework creates four classification schemas, by (1) targeted behavior types (self-management, health care encounter, shopping, and health behaviors) and by (2) individual, (3) group, and (4) community dimensions. Our example illustrates that the framework can systematically classify a variety of consumer engagement programs, and that this exercise and resulting characterization can provide a structured way to consider the program and how its components fit program goals both individually and collectively. Conclusions Applying the framework could help advance the field by making policymakers and practitioners aware of the wide range of approaches, providing a structured way to organize and characterize interventions retrospectively, and helping them consider how they can meet the program's goals both individually and collectively. PMID:23488711

Phenotypic variation in metabolism and morphology correlating with animal swimming activity in the wild: relevance for the OCLTT (oxygen- and capacity-limitation of thermal tolerance), allocation and performance models

PubMed Central

Baktoft, Henrik; Jacobsen, Lene; Skov, Christian; Koed, Anders; Jepsen, Niels; Berg, Søren; Boel, Mikkel; Aarestrup, Kim; Svendsen, Jon C.

2016-01-01

Ongoing climate change is affecting animal physiology in many parts of the world. Using metabolism, the oxygen- and capacity-limitation of thermal tolerance (OCLTT) hypothesis provides a tool to predict the responses of ectothermic animals to variation in temperature, oxygen availability and pH in the aquatic environment. The hypothesis remains controversial, however, and has been questioned in several studies. A positive relationship between aerobic metabolic scope and animal activity would be consistent with the OCLTT but has rarely been tested. Moreover, the performance model and the allocation model predict positive and negative relationships, respectively, between standard metabolic rate and activity. Finally, animal activity could be affected by individual morphology because of covariation with cost of transport. Therefore, we hypothesized that individual variation in activity is correlated with variation in metabolism and morphology. To test this prediction, we captured 23 wild European perch (Perca fluviatilis) in a lake, tagged them with telemetry transmitters, measured standard and maximal metabolic rates, aerobic metabolic scope and fineness ratio and returned the fish to the lake to quantify individual in situ activity levels. Metabolic rates were measured using intermittent flow respirometry, whereas the activity assay involved high-resolution telemetry providing positions every 30 s over 12 days. We found no correlation between individual metabolic traits and activity, whereas individual fineness ratio correlated with activity. Independent of body length, and consistent with physics theory, slender fish maintained faster mean and maximal swimming speeds, but this variation did not result in a larger area (in square metres) explored per 24 h. Testing assumptions and predictions of recent conceptual models, our study indicates that individual metabolism is not a strong determinant of animal activity, in contrast to individual morphology, which is correlated with in situ activity patterns. PMID:27382465

Occupational problems and barriers reported by individuals with obesity.

PubMed

Nossum, Randi; Johansen, Ann-Elin; Kjeken, Ingvild

2018-03-01

Even if occupational therapists meet many people with obesity in the course of their work, a majority of them do not seem to view weight management as within their area of professional practice. To explore the occupational problems and barriers among persons with severe obesity from an occupational therapy perspective. The study used the Canadian Model of Occupation and Engagement (CMOP-E) and Canadian Occupational Performance Measure (COPM) to identify and analyze prioritized occupational performance problems and barriers perceived by 63 individuals with obesity. The occupational problems individuals with obesity most frequently prioritized comprised playing with (grand)children, purchasing clothes, implementing regular meals and going to the swimming pool, while the barriers they most frequently described were dyspnea, musculoskeletal disorders, narrow chairs and seats, fear of glances and comments from others, and social anxiety. Persons with obesity struggle with a large variety of occupational performance problems, which occur in the dynamic relationship between these individuals, their environment and their occupation. Occupational therapists have the skills to take more active role in helping persons with obesity to perform valued occupations and establish healthier everyday routines.

Managing hospital doctors and their practice: what can we learn about human resource management from non-healthcare organisations?

PubMed

Trebble, Timothy M; Heyworth, Nicola; Clarke, Nicholas; Powell, Timothy; Hockey, Peter M

2014-11-21

Improved management of clinicians' time and practice is advocated to address increasing demands on healthcare provision in the UK National Health Service (NHS). Human resource management (HRM) is associated with improvements in organisational performance and outcomes within and outside of healthcare, but with limited use in managing individual clinicians. This may reflect the absence of effective and transferrable models. The current systems of managing the performance of individual clinicians in a secondary healthcare organisation were reviewed through the study of practice in 10 successful partnership organisations, including knowledge worker predominant, within commercial, public and voluntary sector operating environments. Reciprocal visits to the secondary healthcare environment were undertaken. Six themes in performance related HRM were identified across the external organisations representing best practice and considered transferrable to managing clinicians in secondary care organisations. These included: performance measurement through defined outcomes at the team level with decision making through local data interpretation; performance improvement through empowered formal leadership with organisational support; individual performance review (IPR); and reward, recognition and talent management. The role of the executive was considered essential to support and implement effective HRM, with management of staff performance, behaviour and development integrated into organisational strategy, including through the use of universally applied values and effective communication. These approaches reflected many of the key aspects of high performance work systems and strategic HRM. There is the potential to develop systems of HRM of individual clinicians in secondary healthcare to improve practice. This should include both performance measurement and performance improvement but also engagement at an organisational level. This suggests that effective HRM and performance management of individual clinicians may be possible but requires an alternative approach for the NHS.

Individual Colorimetric Observer Model

PubMed Central

Asano, Yuta; Fairchild, Mark D.; Blondé, Laurent

2016-01-01

This study proposes a vision model for individual colorimetric observers. The proposed model can be beneficial in many color-critical applications such as color grading and soft proofing to assess ranges of color matches instead of a single average match. We extended the CIE 2006 physiological observer by adding eight additional physiological parameters to model individual color-normal observers. These eight parameters control lens pigment density, macular pigment density, optical densities of L-, M-, and S-cone photopigments, and λmax shifts of L-, M-, and S-cone photopigments. By identifying the variability of each physiological parameter, the model can simulate color matching functions among color-normal populations using Monte Carlo simulation. The variabilities of the eight parameters were identified through two steps. In the first step, extensive reviews of past studies were performed for each of the eight physiological parameters. In the second step, the obtained variabilities were scaled to fit a color matching dataset. The model was validated using three different datasets: traditional color matching, applied color matching, and Rayleigh matches. PMID:26862905

Tracking career performance of successful triathletes.

PubMed

Malcata, Rita M; Hopkins, Will G; Pearson, Simon N

2014-06-01

Tracking athletes' performances over time is important but problematic for sports with large environmental effects. Here we have developed career performance trajectories for elite triathletes, investigating changes in swim, cycle, run stages, and total performance times while accounting for environmental and other external factors. Performance times of 337 female and 427 male triathletes competing in 419 international races between 2000 and 2012 were obtained from triathlon.org. Athletes were categorized according to any top 16 placing at World Championships or Olympics between 2008 and 2012. A mixed linear model accounting for race distance (sprint and Olympic), level of competition, calendar-year trend, athlete's category, and clustering of times within athletes and races was used to derive athletes' individual quadratic performance trajectories. These trajectories provided estimates of age of peak performance and predictions for the 2012 London Olympic Games. By markedly reducing the scatter of individual race times, the model produced well-fitting trajectories suitable for comparison of triathletes. Trajectories for top 16 triathletes showed different patterns for race stages and differed more among women than among men, but ages of peak total performance were similar for men and women (28 ± 3 yr, mean ± SD). Correlations between observed and predicted placings at Olympics were slightly higher than those provided by placings in races before the Olympics. Athletes' trajectories will help identify talented athletes and their weakest and strongest stages. The wider range of trajectories among women should be taken into account when setting talent identification criteria. Trajectories offer a small advantage over usual race placings for predicting men's performance. Further refinements, such as accounting for individual responses to race conditions, may improve utility of performance trajectories.

The effect of response modality on immediate serial recall in dementia of the Alzheimer type.

PubMed

Macé, Anne-Laure; Ergis, Anne-Marie; Caza, Nicole

2012-09-01

Contrary to traditional models of verbal short-term memory (STM), psycholinguistic accounts assume that temporary retention of verbal materials is an intrinsic property of word processing. Therefore, memory performance will depend on the nature of the STM tasks, which vary according to the linguistic representations they engage. The aim of this study was to explore the effect of response modality on verbal STM performance in individuals with dementia of the Alzheimer Type (DAT), and its relationship with the patients' word-processing deficits. Twenty individuals with mild DAT and 20 controls were tested on an immediate serial recall (ISR) task using the same items across two response modalities (oral and picture pointing) and completed a detailed language assessment. When scoring of ISR performance was based on item memory regardless of item order, a response modality effect was found for all participants, indicating that they recalled more items with picture pointing than with oral response. However, this effect was less marked in patients than in controls, resulting in an interaction. Interestingly, when recall of both item and order was considered, results indicated similar performance between response modalities in controls, whereas performance was worse for pointing than for oral response in patients. Picture-naming performance was also reduced in patients relative to controls. However, in the word-to-picture matching task, a similar pattern of responses was found between groups for incorrectly named pictures of the same items. The finding of a response modality effect in item memory for all participants is compatible with the assumption that semantic influences are greater in picture pointing than in oral response, as predicted by psycholinguistic models. Furthermore, patients' performance was modulated by their word-processing deficits, showing a reduced advantage relative to controls. Overall, the response modality effect observed in this study for item memory suggests that verbal STM performance is intrinsically linked with word processing capacities in both healthy controls and individuals with mild DAT, supporting psycholinguistic models of STM.

Selection methods regulate evolution of cooperation in digital evolution

PubMed Central

Lichocki, Paweł; Floreano, Dario; Keller, Laurent

2014-01-01

A key, yet often neglected, component of digital evolution and evolutionary models is the ‘selection method’ which assigns fitness (number of offspring) to individuals based on their performance scores (efficiency in performing tasks). Here, we study with formal analysis and numerical experiments the evolution of cooperation under the five most common selection methods (proportionate, rank, truncation-proportionate, truncation-uniform and tournament). We consider related individuals engaging in a Prisoner's Dilemma game where individuals can either cooperate or defect. A cooperator pays a cost, whereas its partner receives a benefit, which affect their performance scores. These performance scores are translated into fitness by one of the five selection methods. We show that cooperation is positively associated with the relatedness between individuals under all selection methods. By contrast, the change in the performance benefit of cooperation affects the populations’ average level of cooperation only under the proportionate methods. We also demonstrate that the truncation and tournament methods may introduce negative frequency-dependence and lead to the evolution of polymorphic populations. Using the example of the evolution of cooperation, we show that the choice of selection method, though it is often marginalized, can considerably affect the evolutionary dynamics. PMID:24152811

PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA.

PubMed

Schwartz, H Andrew; Sap, Maarten; Kern, Margaret L; Eichstaedt, Johannes C; Kapelner, Adam; Agrawal, Megha; Blanco, Eduardo; Dziurzynski, Lukasz; Park, Gregory; Stillwell, David; Kosinski, Michal; Seligman, Martin E P; Ungar, Lyle H

2016-01-01

We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health. Through crowd-sourced ratings of tweets and Facebook status updates, we create message-level predictive models for multiple components of well-being. However, well-being is ultimately attributed to people, so we perform an additional evaluation at the user-level, finding that a multi-level cascaded model, using both message-level predictions and userlevel features, performs best and outperforms popular lexicon-based happiness models. Finally, we suggest that analyses of language go beyond prediction by identifying the language that characterizes well-being.

Occupational health management system: A study of expatriate construction professionals.

PubMed

Chan, I Y S; Leung, M Y; Liu, A M M

2016-08-01

Due to its direct impact on the safety and function of organizations, occupational health has been a concern of the construction industry for many years. The inherent complexity of occupational health management presents challenges that make a systems approach essential. From a systems perspective, health is conceptualized as an emergent property of a system in which processes operating at the individual and organizational level are inextricably connected. Based on the fundamental behavior-to-performance-to-outcome (B-P-O) theory of industrial/organizational psychology, this study presents the development of an I-CB-HP-O (Input-Coping Behaviors-Health Performance-Outcomes) health management systems model spanning individual and organizational boundaries. The model is based on a survey of Hong Kong expatriate construction professionals working in Mainland China. Such professionals tend to be under considerable stress due not only to an adverse work environment with dynamic tasks, but also the need to confront the cross-cultural issues arising from expatriation. A questionnaire was designed based on 6 focus groups involving 44 participants, and followed by a pilot study. Of the 500 questionnaires distributed in the main study, 137 valid returns were received, giving a response rate of 27.4%. The data were analyzed using statistical techniques such as factor analysis, reliability testing, Pearson correlation analysis, multiple regression modeling, and structural equation modeling. Theories of coping behaviors and health performance tend to focus on the isolated causal effects of single factors and/or posits the model at single, individual level; while industrial practices on health management tend to focus on organizational policy and training. By developing the I-CB-HP-O health management system, incorporating individual, interpersonal, and organizational perspectives, this study bridges the gap between theory and practice while providing empirical support for a systems view of health management. Copyright © 2015 Elsevier Ltd. All rights reserved.

A reciprocal model of face recognition and autistic traits: evidence from an individual differences perspective.

PubMed

Halliday, Drew W R; MacDonald, Stuart W S; Scherf, K Suzanne; Sherf, Suzanne K; Tanaka, James W

2014-01-01

Although not a core symptom of the disorder, individuals with autism often exhibit selective impairments in their face processing abilities. Importantly, the reciprocal connection between autistic traits and face perception has rarely been examined within the typically developing population. In this study, university participants from the social sciences, physical sciences, and humanities completed a battery of measures that assessed face, object and emotion recognition abilities, general perceptual-cognitive style, and sub-clinical autistic traits (the Autism Quotient (AQ)). We employed separate hierarchical multiple regression analyses to evaluate which factors could predict face recognition scores and AQ scores. Gender, object recognition performance, and AQ scores predicted face recognition behaviour. Specifically, males, individuals with more autistic traits, and those with lower object recognition scores performed more poorly on the face recognition test. Conversely, university major, gender and face recognition performance reliably predicted AQ scores. Science majors, males, and individuals with poor face recognition skills showed more autistic-like traits. These results suggest that the broader autism phenotype is associated with lower face recognition abilities, even among typically developing individuals.

A Reciprocal Model of Face Recognition and Autistic Traits: Evidence from an Individual Differences Perspective

PubMed Central

Halliday, Drew W. R.; MacDonald, Stuart W. S.; Sherf, Suzanne K.; Tanaka, James W.

2014-01-01

Although not a core symptom of the disorder, individuals with autism often exhibit selective impairments in their face processing abilities. Importantly, the reciprocal connection between autistic traits and face perception has rarely been examined within the typically developing population. In this study, university participants from the social sciences, physical sciences, and humanities completed a battery of measures that assessed face, object and emotion recognition abilities, general perceptual-cognitive style, and sub-clinical autistic traits (the Autism Quotient (AQ)). We employed separate hierarchical multiple regression analyses to evaluate which factors could predict face recognition scores and AQ scores. Gender, object recognition performance, and AQ scores predicted face recognition behaviour. Specifically, males, individuals with more autistic traits, and those with lower object recognition scores performed more poorly on the face recognition test. Conversely, university major, gender and face recognition performance reliably predicted AQ scores. Science majors, males, and individuals with poor face recognition skills showed more autistic-like traits. These results suggest that the broader autism phenotype is associated with lower face recognition abilities, even among typically developing individuals. PMID:24853862

Human Cognition and Performance.

DTIC Science & Technology

1985-05-01

implications. In D. LaBerge & S. J. Samuels (Eds.), Basic processes in reading: Perception and comprehesion. Hillsdale, NJ: Eribaum. Anderson, J. A...Also pub- lished individually as follows: Some observations on mental models, in D. Gentner and A. Stevens (Eds.), Mental models, Hillsdale, NJ: Erlbaum...A. Stevens (Eds.), Mental Models. Hillsdale, NJ: Erlbaunm. Norman, D.A. (1983). Theories and models in cognitive psychology. In E. Douclkin (Ed

Lessons learned in using IPE model for IPEEE study

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

Guey, C.

1995-12-31

This paper summarizes lessons learned in applying the plant model developed in the Individual Plant Examination (IPE) to the IPE for External Events (IPEEE). Both core damage frequency and containment performance features are addressed. The IPE model applications are discussed for internal fires, hurricanes, and tornadoes. Areas in which the IPE model may be improved and general findings are described.

A prospective study of Mexican American adolescents' academic success: considering family and individual factors.

PubMed

Roosa, Mark W; O'Donnell, Megan; Cham, Heining; Gonzales, Nancy A; Zeiders, Katherine H; Tein, Jenn-Yun; Knight, George P; Umaña-Taylor, Adriana

2012-03-01

Mexican American youth are at greater risk of school failure than their peers. To identify factors that may contribute to academic success in this population, this study examined the prospective relationships from 5th grade to 7th grade of family (i.e., human capital [a parent with at least a high school education], residential stability, academically and occupationally positive family role models, and family structure) and individual characteristics (i.e., externalizing symptoms, bilingualism, gender, and immigrant status) to the academic performance of 749 Mexican American early adolescents (average age = 10.4 years and 48.7% were girls in 5th grade) from economically and culturally diverse families as these youth made the transition to junior high school. Results indicated that while controlling for prior academic performance, human capital and positive family role models assessed when adolescents were in 5th grade positively related to academic performance in 7th grade. Further, being a girl also was related to greater 7th grade academic success, whereas externalizing symptoms were negatively related to 7th grade academic performance. No other variables in the model were significantly and prospectively related to 7th grade academic performance. Implications for future research and interventions are discussed.

A Prospective Study of Mexican American Adolescents’ Academic Success: Considering Family and Individual Factors

PubMed Central

Roosa, Mark W.; O’Donnell, Megan; Cham, Heining; Gonzales, Nancy A.; Zeiders, Katherine H.; Tein, Jenn-Yun; Knight, George P.; Umaña-Taylor, Adriana

2011-01-01

Mexican American youth are at greater risk of school failure than their peers. To identify factors that may contribute to academic success in this population, this study examined the prospective relationships from 5th grade to 7th grade of family (i.e., human capital [a parent with at least a high school education], residential stability, academically and occupationally positive family role models, and family structure) and individual characteristics (i.e., externalizing symptoms, bilingualism, gender, and immigrant status) to the academic performance of 749 Mexican American early adolescents (average age = 10.4 years and 48.7% were girls in 5th grade) from economically and culturally diverse families as these youth made the transition to junior high school. Results indicated that while controlling for prior academic performance, human capital and positive family role models assessed when adolescents were in in 5th grade positively related to academic performance in 7th grade. Further, being a girl also was related to greater 7th grade academic success, whereas externalizing symptoms were negatively related to 7th grade academic performance. No other variables in the model were significantly and prospectively related to 7th grade academic performance. Implications for future research and interventions are discussed. PMID:21863379

Case management: a randomized controlled study comparing a neighborhood team and a centralized individual model.

PubMed

Eggert, G M; Zimmer, J G; Hall, W J; Friedman, B

1991-10-01

This randomized controlled study compared two types of case management for skilled nursing level patients living at home: the centralized individual model and the neighborhood team model. The team model differed from the individual model in that team case managers performed client assessments, care planning, some direct services, and reassessments; they also had much smaller caseloads and were assigned a specific catchment area. While patients in both groups incurred very high estimated health services costs, the average annual cost during 1983-85 for team cases was 13.6 percent less than that of individual model cases. While the team cases were 18.3 percent less expensive among "old" patients (patients who entered the study from the existing ACCESS caseload), they were only 2.7 percent less costly among "new" cases. The lower costs were due to reductions in hospital days and home care. Team cases averaged 26 percent fewer hospital days per year and 17 percent fewer home health aide hours. Nursing home use was 48 percent higher for the team group than for the individual model group. Mortality was almost exactly the same for both groups during the first year (about 30 percent), but was lower for team patients during the second year (11 percent as compared to 16 percent). Probable mechanisms for the observed results are discussed.

Spatial Visualization--A Gateway to Computer-Based Technology.

ERIC Educational Resources Information Center

Norman, Kent L.

1994-01-01

A model is proposed for the influence of individual differences on performance when computer-based technology is introduced. The primary cognitive factor driving differences in performance is spatial visualization ability. Four techniques for mitigating the negative impact of low spatial visualization are discussed: spatial metaphors, graphical…

A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model

PubMed Central

Song, Yulei; Yan, Xuedong

2016-01-01

The prediction of evacuation demand curves is a crucial step in the disaster evacuation plan making, which directly affects the performance of the disaster evacuation. In this paper, we discuss the factors influencing individual evacuation decision making (whether and when to leave) and summarize them into four kinds: individual characteristics, social influence, geographic location, and warning degree. In the view of social contagion of decision making, a method based on Susceptible-Infective (SI) model is proposed to formulize the disaster evacuation demand curves to address both social influence and other factors’ effects. The disaster event of the “Tianjin Explosions” is used as a case study to illustrate the modeling results influenced by the four factors and perform the sensitivity analyses of the key parameters of the model. Some interesting phenomena are found and discussed, which is meaningful for authorities to make specific evacuation plans. For example, due to the lower social influence in isolated communities, extra actions might be taken to accelerate evacuation process in those communities. PMID:27735875

Predicting coin flips: using resampling and hierarchical models to help untangle the NHL's shoot-out.

PubMed

Lopez, Michael J; Schuckers, Michael

2017-05-01

Roughly 14% of regular season National Hockey League games since the 2005-06 season have been decided by a shoot-out, and the resulting allocation of points has impacted play-off races each season. But despite interest from fans, players and league officials, there is little in the way of published research on team or individual shoot-out performance. This manuscript attempts to fill that void. We present both generalised linear mixed model and Bayesian hierarchical model frameworks to model shoot-out outcomes, with results suggesting that there are (i) small but statistically significant talent gaps between shooters, (ii) marginal differences in performance among netminders and (iii) few, if any, predictors of player success after accounting for individual talent. We also provide a resampling strategy to highlight a selection bias with respect to shooter assignment, in which coaches choose their most skilled offensive players early in shoot-out rounds and are less likely to select players with poor past performances. Finally, given that per-shot data for shoot-outs do not currently exist in a single location for public use, we provide both our data and source code for other researchers interested in studying shoot-out outcomes.

Using risk-adjustment models to identify high-cost risks.

PubMed

Meenan, Richard T; Goodman, Michael J; Fishman, Paul A; Hornbrook, Mark C; O'Keeffe-Rosetti, Maureen C; Bachman, Donald J

2003-11-01

We examine the ability of various publicly available risk models to identify high-cost individuals and enrollee groups using multi-HMO administrative data. Five risk-adjustment models (the Global Risk-Adjustment Model [GRAM], Diagnostic Cost Groups [DCGs], Adjusted Clinical Groups [ACGs], RxRisk, and Prior-expense) were estimated on a multi-HMO administrative data set of 1.5 million individual-level observations for 1995-1996. Models produced distributions of individual-level annual expense forecasts for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution. The area under the receiver operating characteristic curve (AUC) for "high-cost" prevalences of 1% and 0.5% was calculated, as was the proportion of "high-cost" dollars correctly identified. Results are based on a separate 106,000-observation validation dataset. For "high-cost" prevalence targets of 1% and 0.5%, ACGs, DCGs, GRAM, and Prior-expense are very comparable in overall discrimination (AUCs, 0.83-0.86). Given a 0.5% prevalence target and a 0.5% prediction threshold, DCGs, GRAM, and Prior-expense captured $963,000 (approximately 3%) more "high-cost" sample dollars than other models. DCGs captured the most "high-cost" dollars among enrollees with asthma, diabetes, and depression; predictive performance among demographic groups (Medicaid members, members over 64, and children under 13) varied across models. Risk models can efficiently identify enrollees who are likely to generate future high costs and who could benefit from case management. The dollar value of improved prediction performance of the most accurate risk models should be meaningful to decision-makers and encourage their broader use for identifying high costs.

Measurement Structure of the Trait Hope Scale in Persons with Spinal Cord Injury: A Confirmatory Factor Analysis

ERIC Educational Resources Information Center

Smedema, Susan Miller; Pfaller, Joseph; Moser, Erin; Tu, Wei-Mo; Chan, Fong

2013-01-01

Objective: To evaluate the measurement structure of the Trait Hope Scale (THS) among individuals with spinal cord injury. Design: Confirmatory factor analysis and reliability and validity analyses were performed. Participants: 242 individuals with spinal cord injury. Results: Results support the two-factor measurement model for the THS with agency…

Correlates of Individual, and Age-Related, Differences in Short-Term Learning

ERIC Educational Resources Information Center

Zhang, Zhiyong; Davis, Hasker P.; Salthouse, Timothy A.; Tucker-Drob, Elliot M.

2007-01-01

Latent growth models were applied to data on multitrial verbal and spatial learning tasks from two independent studies. Although significant individual differences in both initial level of performance and subsequent learning were found in both tasks, age differences were found only in mean initial level, and not in mean learning. In neither task…

Pre-Service Special Education Teachers Acceptance and Use of ICT: A Structural Equation Model

ERIC Educational Resources Information Center

Yeni, Sabiha; Gecu-Parmaksiz, Zeynep

2016-01-01

Information and communication technology (ICT) supported education helps the individuals with special educational needs to take their attention to the course content and to concentrate their attention on the task they need to perform. The mechanical advantages of ICT tools make them attractive for individuals with special educational needs. If…

A Model to Improve Teacher Performance in Implementing Individual Instructional Programs for Exceptional Children in a Mainstream Education. Midi Report.

ERIC Educational Resources Information Center

Vidaurri, Otilia V.

Described is a teacher development center, an inservice program designed to develop competencies for individualizing instruction in 73 regular and special educators attending 2-week training sessions. It is explained that training focused on 12 content modules (including teacher communication and guidance, classroom management, and organization of…

Mathematical analysis of a lymphatic filariasis model with quarantine and treatment.

PubMed

Mwamtobe, Peter M; Simelane, Simphiwe M; Abelman, Shirley; Tchuenche, Jean M

2017-03-16

Lymphatic filariasis is a globally neglected tropical parasitic disease which affects individuals of all ages and leads to an altered lymphatic system and abnormal enlargement of body parts. A mathematical model of lymphatic filariaris with intervention strategies is developed and analyzed. Control of infections is analyzed within the model through medical treatment of infected-acute individuals and quarantine of infected-chronic individuals. We derive the effective reproduction number, [Formula: see text] and its interpretation/investigation suggests that treatment contributes to a reduction in lymphatic filariasis cases faster than quarantine. However, this reduction is greater when the two intervention approaches are applied concurrently. Numerical simulations are carried out to monitor the dynamics of the filariasis model sub-populations for various parameter values of the associated reproduction threshold. Lastly, sensitivity analysis on key parameters that drive the disease dynamics is performed in order to identify their relative importance on the disease transmission.

Stream macroinvertebrate response models for bioassessment metrics: addressing the issue of spatial scale

USGS Publications Warehouse

White, Ian R.; Kennen, Jonathan G.; May, Jason T.; Brown, Larry R.; Cuffney, Thomas F.; Jones, Kimberly A.; Orlando, James L.

2014-01-01

We developed independent predictive disturbance models for a full regional data set and four individual ecoregions (Full Region vs. Individual Ecoregion models) to evaluate effects of spatial scale on the assessment of human landscape modification, on predicted response of stream biota, and the effect of other possible confounding factors, such as watershed size and elevation, on model performance. We selected macroinvertebrate sampling sites for model development (n = 591) and validation (n = 467) that met strict screening criteria from four proximal ecoregions in the northeastern U.S.: North Central Appalachians, Ridge and Valley, Northeastern Highlands, and Northern Piedmont. Models were developed using boosted regression tree (BRT) techniques for four macroinvertebrate metrics; results were compared among ecoregions and metrics. Comparing within a region but across the four macroinvertebrate metrics, the average richness of tolerant taxa (RichTOL) had the highest R2 for BRT models. Across the four metrics, final BRT models had between four and seven explanatory variables and always included a variable related to urbanization (e.g., population density, percent urban, or percent manmade channels), and either a measure of hydrologic runoff (e.g., minimum April, average December, or maximum monthly runoff) and(or) a natural landscape factor (e.g., riparian slope, precipitation, and elevation), or a measure of riparian disturbance. Contrary to our expectations, Full Region models explained nearly as much variance in the macroinvertebrate data as Individual Ecoregion models, and taking into account watershed size or elevation did not appear to improve model performance. As a result, it may be advantageous for bioassessment programs to develop large regional models as a preliminary assessment of overall disturbance conditions as long as the range in natural landscape variability is not excessive.

Stream Macroinvertebrate Response Models for Bioassessment Metrics: Addressing the Issue of Spatial Scale

PubMed Central

Waite, Ian R.; Kennen, Jonathan G.; May, Jason T.; Brown, Larry R.; Cuffney, Thomas F.; Jones, Kimberly A.; Orlando, James L.

2014-01-01

We developed independent predictive disturbance models for a full regional data set and four individual ecoregions (Full Region vs. Individual Ecoregion models) to evaluate effects of spatial scale on the assessment of human landscape modification, on predicted response of stream biota, and the effect of other possible confounding factors, such as watershed size and elevation, on model performance. We selected macroinvertebrate sampling sites for model development (n = 591) and validation (n = 467) that met strict screening criteria from four proximal ecoregions in the northeastern U.S.: North Central Appalachians, Ridge and Valley, Northeastern Highlands, and Northern Piedmont. Models were developed using boosted regression tree (BRT) techniques for four macroinvertebrate metrics; results were compared among ecoregions and metrics. Comparing within a region but across the four macroinvertebrate metrics, the average richness of tolerant taxa (RichTOL) had the highest R2 for BRT models. Across the four metrics, final BRT models had between four and seven explanatory variables and always included a variable related to urbanization (e.g., population density, percent urban, or percent manmade channels), and either a measure of hydrologic runoff (e.g., minimum April, average December, or maximum monthly runoff) and(or) a natural landscape factor (e.g., riparian slope, precipitation, and elevation), or a measure of riparian disturbance. Contrary to our expectations, Full Region models explained nearly as much variance in the macroinvertebrate data as Individual Ecoregion models, and taking into account watershed size or elevation did not appear to improve model performance. As a result, it may be advantageous for bioassessment programs to develop large regional models as a preliminary assessment of overall disturbance conditions as long as the range in natural landscape variability is not excessive. PMID:24675770

A biologically plausible computational model for auditory object recognition.

PubMed

Larson, Eric; Billimoria, Cyrus P; Sen, Kamal

2009-01-01

Object recognition is a task of fundamental importance for sensory systems. Although this problem has been intensively investigated in the visual system, relatively little is known about the recognition of complex auditory objects. Recent work has shown that spike trains from individual sensory neurons can be used to discriminate between and recognize stimuli. Multiple groups have developed spike similarity or dissimilarity metrics to quantify the differences between spike trains. Using a nearest-neighbor approach the spike similarity metrics can be used to classify the stimuli into groups used to evoke the spike trains. The nearest prototype spike train to the tested spike train can then be used to identify the stimulus. However, how biological circuits might perform such computations remains unclear. Elucidating this question would facilitate the experimental search for such circuits in biological systems, as well as the design of artificial circuits that can perform such computations. Here we present a biologically plausible model for discrimination inspired by a spike distance metric using a network of integrate-and-fire model neurons coupled to a decision network. We then apply this model to the birdsong system in the context of song discrimination and recognition. We show that the model circuit is effective at recognizing individual songs, based on experimental input data from field L, the avian primary auditory cortex analog. We also compare the performance and robustness of this model to two alternative models of song discrimination: a model based on coincidence detection and a model based on firing rate.

Personalized Modeling for Prediction with Decision-Path Models

PubMed Central

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

2015-01-01

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

Effect of the learning climate of residency programs on faculty's teaching performance as evaluated by residents.

PubMed

Lombarts, Kiki M J M H; Heineman, Maas Jan; Scherpbier, Albert J J A; Arah, Onyebuchi A

2014-01-01

To understand teaching performance of individual faculty, the climate in which residents' learning takes place, the learning climate, may be important. There is emerging evidence that specific climates do predict specific outcomes. Until now, the effect of learning climate on the performance of the individual faculty who actually do the teaching was unknown. THIS STUDY: (i) tested the hypothesis that a positive learning climate was associated with better teaching performance of individual faculty as evaluated by residents, and (ii) explored which dimensions of learning climate were associated with faculty's teaching performance. We conducted two cross-sectional questionnaire surveys amongst residents from 45 residency training programs and multiple specialties in 17 hospitals in the Netherlands. Residents evaluated the teaching performance of individual faculty using the robust System for Evaluating Teaching Qualities (SETQ) and evaluated the learning climate of residency programs using the Dutch Residency Educational Climate Test (D-RECT). The validated D-RECT questionnaire consisted of 11 subscales of learning climate. Main outcome measure was faculty's overall teaching (SETQ) score. We used multivariable adjusted linear mixed models to estimate the separate associations of overall learning climate and each of its subscales with faculty's teaching performance. In total 451 residents completed 3569 SETQ evaluations of 502 faculty. Residents also evaluated the learning climate of 45 residency programs in 17 hospitals in the Netherlands. Overall learning climate was positively associated with faculty's teaching performance (regression coefficient 0.54, 95% confidence interval: 0.37 to 0.71; P<0.001). Three out of 11 learning climate subscales were substantially associated with better teaching performance: 'coaching and assessment', 'work is adapted to residents' competence', and 'formal education'. Individual faculty's teaching performance evaluations are positively affected by better learning climate of residency programs.

MANOVA vs nonlinear mixed effects modeling: The comparison of growth patterns of female and male quail

NASA Astrophysics Data System (ADS)

Gürcan, Eser Kemal

2017-04-01

The most commonly used methods for analyzing time-dependent data are multivariate analysis of variance (MANOVA) and nonlinear regression models. The aim of this study was to compare some MANOVA techniques and nonlinear mixed modeling approach for investigation of growth differentiation in female and male Japanese quail. Weekly individual body weight data of 352 male and 335 female quail from hatch to 8 weeks of age were used to perform analyses. It is possible to say that when all the analyses are evaluated, the nonlinear mixed modeling is superior to the other techniques because it also reveals the individual variation. In addition, the profile analysis also provides important information.

Enhancing the effectiveness of human-robot teaming with a closed-loop system.

PubMed

Teo, Grace; Reinerman-Jones, Lauren; Matthews, Gerald; Szalma, James; Jentsch, Florian; Hancock, Peter

2018-02-01

With technological developments in robotics and their increasing deployment, human-robot teams are set to be a mainstay in the future. To develop robots that possess teaming capabilities, such as being able to communicate implicitly, the present study implemented a closed-loop system. This system enabled the robot to provide adaptive aid without the need for explicit commands from the human teammate, through the use of multiple physiological workload measures. Such measures of workload vary in sensitivity and there is large inter-individual variability in physiological responses to imposed taskload. Workload models enacted via closed-loop system should accommodate such individual variability. The present research investigated the effects of the adaptive robot aid vs. imposed aid on performance and workload. Results showed that adaptive robot aid driven by an individualized workload model for physiological response resulted in greater improvements in performance compared to aid that was simply imposed by the system. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effect of Alzheimer's disease risk and protective factors on cognitive trajectories in subjective memory complainers.

PubMed

Teipel, Stefan J; Cavedo, Enrica; Lista, Simone; Habert, Marie-Odile; Potier, Marie-Claude; Grothe, Michel J; Epelbaum, Stephane; Sambati, Luisa; Gagliardi, Geoffroy; Toschi, Nicola; Greicius, Michael; Dubois, Bruno; Hampel, Harald

2018-05-21

Cognitive change in people at risk of Alzheimer's disease (AD) such as subjective memory complainers is highly variable across individuals. We used latent class growth modeling to identify distinct classes of nonlinear trajectories of cognitive change over 2 years follow-up from 265 subjective memory complainers individuals (age 70 years and older) of the INSIGHT-preAD cohort. We determined the effect of cortical amyloid load, hippocampus and basal forebrain volumes, and education on the cognitive trajectory classes. Latent class growth modeling identified distinct nonlinear cognitive trajectories. Education was associated with higher performing trajectories, whereas global amyloid load and basal forebrain atrophy were associated with lower performing trajectories. Distinct classes of cognitive trajectories were associated with risk and protective factors of AD. These associations support the notion that the identified cognitive trajectories reflect different risk for AD that may be useful for selecting high-risk individuals for intervention trials. Copyright © 2018. Published by Elsevier Inc.

A dual-stage moderated mediation model linking authoritarian leadership to follower outcomes.

PubMed

Schaubroeck, John M; Shen, Yimo; Chong, Sinhui

2017-02-01

Although authoritarian leadership is viewed pejoratively in the literature, in general it is not strongly related to important follower outcomes. We argue that relationships between authoritarian leadership and individual employee outcomes are mediated by perceived insider status, yet in different ways depending on work unit power distance climate and individual role breadth self-efficacy. Results from technology company employees in China largely supported our hypothesized model. We observed negative indirect effects of authoritarian leadership on job performance, affective organizational commitment, and intention to stay among employees in units with relatively low endorsement of power distance, whereas the indirect relationships were not significant among employees in relatively high power distance units. These conditional indirect effects of authoritarian leadership on performance and intention to stay were significantly stronger among employees with relatively high role breadth self-efficacy. We discuss how the model and findings promote understanding of how, and under what circumstances, authoritarian leadership may influence followers' performance and psychological connections to their organizations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A model of motor performance during surface penetration: from physics to voluntary control.

PubMed

Klatzky, Roberta L; Gershon, Pnina; Shivaprabhu, Vikas; Lee, Randy; Wu, Bing; Stetten, George; Swendsen, Robert H

2013-10-01

The act of puncturing a surface with a hand-held tool is a ubiquitous but complex motor behavior that requires precise force control to avoid potentially severe consequences. We present a detailed model of puncture over a time course of approximately 1,000 ms, which is fit to kinematic data from individual punctures, obtained via a simulation with high-fidelity force feedback. The model describes puncture as proceeding from purely physically determined interactions between the surface and tool, through decline of force due to biomechanical viscosity, to cortically mediated voluntary control. When fit to the data, it yields parameters for the inertial mass of the tool/person coupling, time characteristic of force decline, onset of active braking, stopping time and distance, and late oscillatory behavior, all of which the analysis relates to physical variables manipulated in the simulation. While the present data characterize distinct phases of motor performance in a group of healthy young adults, the approach could potentially be extended to quantify the performance of individuals from other populations, e.g., with sensory-motor impairments. Applications to surgical force control devices are also considered.

The relation between social anxiety and audience perception: examining Clark and Wells' (1995) model among adolescents.

PubMed

Blöte, Anke W; Miers, Anne C; Heyne, David A; Clark, David M; Westenberg, P Michiel

2014-09-01

Clark and Wells' cognitive model of social anxiety proposes that socially anxious individuals have negative expectations of performance prior to a social event, focus their attention predominantly on themselves and on their negative self-evaluations during an event, and use this negative self-processing to infer that other people are judging them harshly. The present study tested these propositions. The study used a community sample of 161 adolescents aged 14-18 years. The participants gave a speech in front of a pre-recorded audience acting neutrally, and participants were aware that the projected audience was pre-recorded. As expected, participants with higher levels of social anxiety had more negative performance expectations, higher self-focused attention, and more negative perceptions of the audience. Negative performance expectations and self-focused attention were found to mediate the relationship between social anxiety and audience perception. The findings support Clark and Wells' cognitive model of social anxiety, which poses that socially anxious individuals have distorted perceptions of the responses of other people because their perceptions are coloured by their negative thoughts and feelings.

A Parameter Subset Selection Algorithm for Mixed-Effects Models

DOE PAGES

Schmidt, Kathleen L.; Smith, Ralph C.

2016-01-01

Mixed-effects models are commonly used to statistically model phenomena that include attributes associated with a population or general underlying mechanism as well as effects specific to individuals or components of the general mechanism. This can include individual effects associated with data from multiple experiments. However, the parameterizations used to incorporate the population and individual effects are often unidentifiable in the sense that parameters are not uniquely specified by the data. As a result, the current literature focuses on model selection, by which insensitive parameters are fixed or removed from the model. Model selection methods that employ information criteria are applicablemore » to both linear and nonlinear mixed-effects models, but such techniques are limited in that they are computationally prohibitive for large problems due to the number of possible models that must be tested. To limit the scope of possible models for model selection via information criteria, we introduce a parameter subset selection (PSS) algorithm for mixed-effects models, which orders the parameters by their significance. In conclusion, we provide examples to verify the effectiveness of the PSS algorithm and to test the performance of mixed-effects model selection that makes use of parameter subset selection.« less

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

PubMed Central

Chiu, Chi-yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-ling; Xiong, Momiao; Fan, Ruzong

2017-01-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data. PMID:28000696

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.

PubMed

Chiu, Chi-Yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-Ling; Xiong, Momiao; Fan, Ruzong

2017-02-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.

Selecting global climate models for regional climate change studies

PubMed Central

Pierce, David W.; Barnett, Tim P.; Santer, Benjamin D.; Gleckler, Peter J.

2009-01-01

Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures. PMID:19439652

A Hybrid Actuation System Demonstrating Significantly Enhanced Electromechanical Performance

NASA Technical Reports Server (NTRS)

Su, Ji; Xu, Tian-Bing; Zhang, Shujun; Shrout, Thomas R.; Zhang, Qiming

2004-01-01

A hybrid actuation system (HYBAS) utilizing advantages of a combination of electromechanical responses of an electroactive polymer (EAP), an electrostrictive copolymer, and an electroactive ceramic single crystal, PZN-PT single crystal, has been developed. The system employs the contribution of the actuation elements cooperatively and exhibits a significantly enhanced electromechanical performance compared to the performances of the device made of each constituting material, the electroactive polymer or the ceramic single crystal, individually. The theoretical modeling of the performances of the HYBAS is in good agreement with experimental observation. The consistence between the theoretical modeling and experimental test make the design concept an effective route for the development of high performance actuating devices for many applications. The theoretical modeling, fabrication of the HYBAS and the initial experimental results will be presented and discussed.

Estimation of aquifer radionuclide concentrations by postprocessing of conservative tracer model results

NASA Astrophysics Data System (ADS)

Gedeon, M.; Vandersteen, K.; Rogiers, B.

2012-04-01

Radionuclide concentrations in aquifers represent an important indicator in estimating the impact of a planned surface disposal for low and medium level short-lived radioactive waste in Belgium, developed by the Belgian Agency for Radioactive Waste and Enriched Fissile Materials (ONDRAF/NIRAS), who also coordinates and leads the corresponding research. Estimating aquifer concentrations for individual radionuclides represents a computational challenge because (a) different retardation values are applied to different hydrogeologic units and (b) sequential decay reactions with radionuclides of various sorption characteristics cause long computational times until a steady-state is reached. The presented work proposes a methodology reducing substantially the computational effort by postprocessing the results of a prior non-reactive tracer simulation. These advective transport results represent the steady-state concentration - source flux ratio and the break-through time at each modelling cell. These two variables are further used to estimate the individual radionuclide concentrations by (a) scaling the steady-state concentrations to the source fluxes of individual radionuclides; (b) applying the radioactive decay and ingrowth in a decay chain; (c) scaling the travel time by the retardation factor and (d) applying linear sorption. While all steps except (b) require solving simple linear equations, applying ingrowth of individual radionuclides in decay chains requires solving the differential Bateman equation. This equation needs to be solved once for a unit radionuclide activity at all arrival times found in the numerical grid. The ratios between the parent nuclide activity and the progeny activities are then used in the postprocessing. Results are presented for discrete points and examples of radioactive plume maps are given. These results compare well to the results achieved using a full numerical simulation including the respective chemical reaction processes. Although the proposed method represents a fast way to estimate the radionuclide concentrations without performing timely challenging simulations, its applicability has some limits. The radionuclide source needs to be assumed constant during the period of achieving a steady-state in the model. Otherwise, the source variability of individual radionuclides needs to be modelled using a numerical simulation. However, such a situation only occurs in cases of source variability in a period until steady-state is reached and such a simulation takes a relatively short time. The proposed method enables an effective estimation of individual radionuclide concentrations in the frame of performance assessment of a radioactive waste disposal. Reducing the calculation time to a minimum enables performing sensitivity and uncertainty analyses, testing alternative models, etc. thus enhancing the overall quality of the modelling analysis.

A fast least-squares algorithm for population inference.

PubMed

Parry, R Mitchell; Wang, May D

2013-01-23

Population inference is an important problem in genetics used to remove population stratification in genome-wide association studies and to detect migration patterns or shared ancestry. An individual's genotype can be modeled as a probabilistic function of ancestral population memberships, Q, and the allele frequencies in those populations, P. The parameters, P and Q, of this binomial likelihood model can be inferred using slow sampling methods such as Markov Chain Monte Carlo methods or faster gradient based approaches such as sequential quadratic programming. This paper proposes a least-squares simplification of the binomial likelihood model motivated by a Euclidean interpretation of the genotype feature space. This results in a faster algorithm that easily incorporates the degree of admixture within the sample of individuals and improves estimates without requiring trial-and-error tuning. We show that the expected value of the least-squares solution across all possible genotype datasets is equal to the true solution when part of the problem has been solved, and that the variance of the solution approaches zero as its size increases. The Least-squares algorithm performs nearly as well as Admixture for these theoretical scenarios. We compare least-squares, Admixture, and FRAPPE for a variety of problem sizes and difficulties. For particularly hard problems with a large number of populations, small number of samples, or greater degree of admixture, least-squares performs better than the other methods. On simulated mixtures of real population allele frequencies from the HapMap project, Admixture estimates sparsely mixed individuals better than Least-squares. The least-squares approach, however, performs within 1.5% of the Admixture error. On individual genotypes from the HapMap project, Admixture and least-squares perform qualitatively similarly and within 1.2% of each other. Significantly, the least-squares approach nearly always converges 1.5- to 6-times faster. The computational advantage of the least-squares approach along with its good estimation performance warrants further research, especially for very large datasets. As problem sizes increase, the difference in estimation performance between all algorithms decreases. In addition, when prior information is known, the least-squares approach easily incorporates the expected degree of admixture to improve the estimate.

Predictors of Biased Self-perception in Individuals with High Social Anxiety: The Effect of Self-consciousness in the Private and Public Self Domains.

PubMed

Nordahl, Henrik; Plummer, Alice; Wells, Adrian

2017-01-01

"Biased self-perception," the tendency to perceive one's social performance as more negative than observers do, is characteristic of socially anxious individuals. Self-attention processes are hypothesised to underlie biased self-perception, however, different models emphasise different aspects of self-attention, with attention to the public aspects of the self being prominent. The current study aimed to investigate the relative contribution of two types of dispositional self-attention; public- and private self-consciousness to biased self-perception in a high ( n = 48) versus a low ( n = 48) social anxiety group undergoing an interaction task. The main finding was that private self-consciousness explained substantial and unique variance in biased negative self-perception in individuals with high social anxiety, while public self-consciousness did not. This relationship was independent of increments in state anxiety. Private self-consciousness appeared to have a specific association with bias related to overestimation of negative social performance rather than underestimation of positive social performance. The implication of this finding is that current treatment models of Social anxiety disorder might include broader aspects of self-focused attention, especially in the context of formulating self-evaluation biases.

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models.

PubMed

Dumont, Cyrielle; Lestini, Giulia; Le Nagard, Hervé; Mentré, France; Comets, Emmanuelle; Nguyen, Thu Thuy; Group, For The Pfim

2018-03-01

Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr. Copyright © 2018 Elsevier B.V. All rights reserved.

Modeling Cognitive Strategies during Complex Task Performing Process

ERIC Educational Resources Information Center

Mazman, Sacide Guzin; Altun, Arif

2012-01-01

The purpose of this study is to examine individuals' computer based complex task performing processes and strategies in order to determine the reasons of failure by cognitive task analysis method and cued retrospective think aloud with eye movement data. Study group was five senior students from Computer Education and Instructional Technologies…

Fostering Organizational Performance: The Role of Learning and Intrapreneurship

ERIC Educational Resources Information Center

Molina, Carlos; Callahan, Jamie L.

2009-01-01

Purpose: The purpose of this paper is to explore the connections between individual learning, intrapreneurship, and organizational learning to create an alternative model of how learning facilitates performance in organizations. Design/methodology/approach: This is a conceptual paper selecting targeted scholarly works that provide support for the…

Characterizing Discourse Deficits Following Penetrating Head Injury: A Preliminary Model

ERIC Educational Resources Information Center

Coelho, Carl; Le, Karen; Mozeiko, Jennifer; Hamilton, Mark; Tyler, Elizabeth; Krueger, Frank; Grafman, Jordan

2013-01-01

Purpose: Discourse analyses have demonstrated utility for delineating subtle communication deficits following closed head injuries (CHIs). The present investigation examined the discourse performance of a large group of individuals with penetrating head injury (PHI). Performance was also compared across 6 subgroups of PHI based on lesion locale. A…

Use of Factor Mixture Modeling to Capture Spearman's Law of Diminishing Returns

ERIC Educational Resources Information Center

Reynolds, Matthew R.; Keith, Timothy Z.; Beretvas, S. Natasha

2010-01-01

Spearman's law of diminishing returns (SLODR) posits that at higher levels of general cognitive ability the general factor ("g") performs less well in explaining individual differences in cognitive test performance. Research has generally supported SLODR, but previous research has required the a priori division of respondents into…

Freshman Learning Communities, College Performance, and Retention. Working Paper 2005-22

ERIC Educational Resources Information Center

Hotchkiss, Julie L.; Moore, Robert E.; Pitts, M. Melinda

2005-01-01

This paper applies a standard treatment effects model to determine that participation in Freshman Learning Communities (FLCs) improves academic performance and retention. Not controlling for individual self-selection into FLC participation leads one to incorrectly conclude that the impact is the same across race and gender groups. Accurately…

Evaluating the Effectiveness of Reference Models in Federating Enterprise Architectures

ERIC Educational Resources Information Center

Wilson, Jeffery A.

2012-01-01

Agencies need to collaborate with each other to perform missions, improve mission performance, and find efficiencies. The ability of individual government agencies to collaborate with each other for mission and business success and efficiency is complicated by the different techniques used to describe their Enterprise Architectures (EAs).…

Decomposing Achievement Gaps among OECD Countries

ERIC Educational Resources Information Center

Zhang, Liang; Lee, Kristen A.

2011-01-01

In this study, we use decomposition methods on PISA 2006 data to compare student academic performance across OECD countries. We first establish an empirical model to explain the variation in academic performance across individuals, and then use the Oaxaca-Blinder decomposition method to decompose the achievement gap between each of the OECD…

Toward an Ecological Perspective of Resident Teaching Clinic

ERIC Educational Resources Information Center

Smith, C. Scott; Francovich, Chris; Morris, Magdalena; Hill, William; Langlois-Winkle, Francine; Rupper, Randall; Roth, Craig; Wheeler, Stephanie; Vo, Anthony

2010-01-01

Teaching clinic managers struggle to convert performance data into meaningful behavioral change in their trainees, and quality improvement measures in medicine have had modest results. This may be due to several factors including clinical performance being based more on team function than individual action, models of best practice that are…

An alternative covariance estimator to investigate genetic heterogeneity in populations

USDA-ARS?s Scientific Manuscript database

Genomic predictions and GWAS have used mixed models for identification of associations and trait predictions. In both cases, the covariance between individuals for performance is estimated using molecular markers. Mixed model properties indicate that the use of the data for prediction is optimal if ...

Understanding individual resilience in the workplace: the international collaboration of workforce resilience model

PubMed Central

Rees, Clare S.; Breen, Lauren J.; Cusack, Lynette; Hegney, Desley

2015-01-01

When not managed effectively, high levels of workplace stress can lead to several negative personal and performance outcomes. Some professional groups work in highly stressful settings and are therefore particularly at risk of conditions such as anxiety, depression, secondary traumatic stress, and burnout. However, some individuals are less affected by workplace stress and the associated negative outcomes. Such individuals have been described as “resilient.” A number of studies have found relationships between levels of individual resilience and specific negative outcomes such as burnout and compassion fatigue. However, because psychological resilience is a multi-dimensional construct it is necessary to more clearly delineate it from other related and overlapping constructs. The creation of a testable theoretical model of individual workforce resilience, which includes both stable traits (e.g., neuroticism) as well as more malleable intrapersonal factors (e.g., coping style), enables information to be derived that can eventually inform interventions aimed at enhancing individual resilience in the workplace. The purpose of this paper is to introduce a new theoretical model of individual workforce resilience that includes several intrapersonal constructs known to be central in the appraisal of and response to stressors and that also overlap with the construct of psychological resilience. We propose a model in which psychological resilience is hypothesized to mediate the relationship between neuroticism, mindfulness, self-efficacy, coping, and psychological adjustment. PMID:25698999

Understanding individual resilience in the workplace: the international collaboration of workforce resilience model.

PubMed

Rees, Clare S; Breen, Lauren J; Cusack, Lynette; Hegney, Desley

2015-01-01

When not managed effectively, high levels of workplace stress can lead to several negative personal and performance outcomes. Some professional groups work in highly stressful settings and are therefore particularly at risk of conditions such as anxiety, depression, secondary traumatic stress, and burnout. However, some individuals are less affected by workplace stress and the associated negative outcomes. Such individuals have been described as "resilient." A number of studies have found relationships between levels of individual resilience and specific negative outcomes such as burnout and compassion fatigue. However, because psychological resilience is a multi-dimensional construct it is necessary to more clearly delineate it from other related and overlapping constructs. The creation of a testable theoretical model of individual workforce resilience, which includes both stable traits (e.g., neuroticism) as well as more malleable intrapersonal factors (e.g., coping style), enables information to be derived that can eventually inform interventions aimed at enhancing individual resilience in the workplace. The purpose of this paper is to introduce a new theoretical model of individual workforce resilience that includes several intrapersonal constructs known to be central in the appraisal of and response to stressors and that also overlap with the construct of psychological resilience. We propose a model in which psychological resilience is hypothesized to mediate the relationship between neuroticism, mindfulness, self-efficacy, coping, and psychological adjustment.

Automatically updating predictive modeling workflows support decision-making in drug design.

PubMed

Muegge, Ingo; Bentzien, Jörg; Mukherjee, Prasenjit; Hughes, Robert O

2016-09-01

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure-activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.

Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology.

PubMed

Metzger, Gregory J; Kalavagunta, Chaitanya; Spilseth, Benjamin; Bolan, Patrick J; Li, Xiufeng; Hutter, Diane; Nam, Jung W; Johnson, Andrew D; Henriksen, Jonathan C; Moench, Laura; Konety, Badrinath; Warlick, Christopher A; Schmechel, Stephen C; Koopmeiners, Joseph S

2016-06-01

Purpose To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel-wise composite biomarker score (CBS) for detection of prostate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Methods Institutional review board approval and informed consent were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area under the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (K(trans)), reflux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and noncancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Four-parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual-level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34) of patients with peripheral-zone and whole-gland models, respectively, compared with ADC alone. Model-based CBS maps for cancer detection showed improved visualization of cancer location and extent. Conclusion Quantitative multiparametric MR imaging models developed by using coregistered correlative histopathologic data yielded a voxel-wise CBS that outperformed single quantitative MR imaging parameters for detection of prostate cancer, especially when the models were assessed at the individual level. (©) RSNA, 2016 Online supplemental material is available for this article.

SEIPS 2.0: A human factors framework for studying and improving the work of healthcare professionals and patients

PubMed Central

Holden, Richard J.; Carayon, Pascale; Gurses, Ayse P.; Hoonakker, Peter; Hundt, Ann Schoofs; Ozok, A. Ant; Rivera-Rodriguez, A. Joy

2013-01-01

Healthcare practitioners, patient safety leaders, educators, and researchers increasingly recognize the value of human factors/ergonomics and make use of the discipline’s person-centered models of sociotechnical systems. This paper first reviews one of the most widely used healthcare human factors systems models, the Systems Engineering Initiative for Patient Safety (SEIPS) model, and then introduces an extended model, “SEIPS 2.0.” SEIPS 2.0 incorporates three novel concepts into the original model: configuration, engagement, and adaptation. The concept of configuration highlights the dynamic, hierarchical, and interactive properties of sociotechnical systems, making it possible to depict how health-related performance is shaped at “a moment in time.” Engagement conveys that various individuals and teams can perform health-related activities separately and collaboratively. Engaged individuals often include patients, family caregivers, and other non-professionals. Adaptation is introduced as a feedback mechanism that explains how dynamic systems evolve in planned and unplanned ways. Key implications and future directions for human factors research in healthcare are discussed. PMID:24088063

GRIN: "GRoup versus INdividual physiotherapy following lower limb intra-muscular Botulinum Toxin-A injections for ambulant children with cerebral palsy: an assessor-masked randomised comparison trial": study protocol.

PubMed

Thomas, Rachel E; Johnston, Leanne M; Boyd, Roslyn N; Sakzewski, Leanne; Kentish, Megan J

2014-02-07

Cerebral palsy is the most common cause of physical disability in childhood. Spasticity is a significant contributor to the secondary impairments impacting functional performance and participation. The most common lower limb spasticity management is focal intramuscular injections of Botulinum Toxin-Type A accompanied by individually-delivered (one on one) physiotherapy rehabilitation. With increasing emphasis on improving goal-directed functional activity and participation within a family-centred framework, it is timely to explore whether physiotherapy provided in a group could achieve comparable outcomes, encouraging providers to offer flexible models of physiotherapy delivery. This study aims to compare individual to group-based physiotherapy following intramuscular Botulinum Toxin-A injections to the lower limbs for ambulant children with cerebral palsy aged four to fourteen years. An assessor-masked, block randomised comparison trial will be conducted with random allocation to either group-based or individual physiotherapy. A sample size of 30 (15 in each study arm) will be recruited. Both groups will receive six hours of direct therapy following Botulinum Toxin-A injections in either an individual or group format with additional home programme activities (three exercises to be performed three times a week). Study groups will be compared at baseline (T1), then at 10 weeks (T2, efficacy) and 26 weeks (T3, retention) post Botulinum Toxin-A injections. Primary outcomes will be caregiver/s perception of and satisfaction with their child's occupational performance goals (Canadian Occupational Performance Measure) and quality of gait (Edinburgh Visual Gait Score) with a range of secondary outcomes across domains of the International Classification of Disability, Functioning and Health. This paper outlines the study protocol including theoretical basis, study hypotheses and outcome measures for this assessor-masked, randomised comparison trial comparing group versus individual models of physiotherapy following intramuscular injections of Botulinum Toxin-A to the lower limbs for ambulant children with cerebral palsy. ACTRN12611000454976.

Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.

PubMed

Costalago Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

2016-03-01

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

The effects of individual status and group performance on network ties among teammates in the National Basketball Association

PubMed Central

Aven, Brandy

2018-01-01

For individuals, status is derived both from their personal attributes and the groups with whom they are affiliated. Depending on the performance of their groups, the status of individuals may benefit or suffer from identifying closely with the group. When the group excels, high-status members potentially receive much of the credit and increased status. Conversely, high-status members of underperforming groups potentially suffer disproportionate declines in their status relative to the low-status group members. We therefore predict an interaction between group performance and individual status on the willingness to associate with the group and its members. We test our prediction by examining social media ties among teammates in the National Basketball Association. Specifically, we investigate the “following” ties of teammates on Twitter at the end of the 2014–2015 season. Elections to All-Star games are used to measure the status of players, and team performance is measured by recent success in the postseason playoffs. The results show that compared to high-status players on successful teams, high-status players on underperforming teams are less likely to follow their teammates. This result aligns with research on status inconsistency, suggesting that individuals deemphasize their group affiliation when it jeopardizes their individual status. An additional contribution is the advancement of the probit Social Relations Model for the analysis of binary ties in social networks. PMID:29708984

The effects of individual status and group performance on network ties among teammates in the National Basketball Association.

PubMed

Koster, Jeremy; Aven, Brandy

2018-01-01

For individuals, status is derived both from their personal attributes and the groups with whom they are affiliated. Depending on the performance of their groups, the status of individuals may benefit or suffer from identifying closely with the group. When the group excels, high-status members potentially receive much of the credit and increased status. Conversely, high-status members of underperforming groups potentially suffer disproportionate declines in their status relative to the low-status group members. We therefore predict an interaction between group performance and individual status on the willingness to associate with the group and its members. We test our prediction by examining social media ties among teammates in the National Basketball Association. Specifically, we investigate the "following" ties of teammates on Twitter at the end of the 2014-2015 season. Elections to All-Star games are used to measure the status of players, and team performance is measured by recent success in the postseason playoffs. The results show that compared to high-status players on successful teams, high-status players on underperforming teams are less likely to follow their teammates. This result aligns with research on status inconsistency, suggesting that individuals deemphasize their group affiliation when it jeopardizes their individual status. An additional contribution is the advancement of the probit Social Relations Model for the analysis of binary ties in social networks.

Individual differences and the development of perceived control.

PubMed

Skinner, E A; Zimmer-Gembeck, M J; Connell, J P

1998-01-01

Research on individual differences demonstrates that children's perceived control exerts a strong effect on their academic achievement and that, in turn, children's actual school performance influences their sense of control. At the same time, developmental research shows systematic age-graded changes in the processes that children use to regulate and interpret control experiences. Drawing on both these perspectives, the current study examines (1) age differences in the operation of beliefs-performance cycles and (2) the effects of these cycles on the development of children's perceived control and classroom engagement from the third to the seventh grade. Longitudinal data on about 1,600 children were collected six times (every fall and spring) over 3 consecutive school years, including children's reports of their perceived control and individual interactions with teachers; teachers' reports of each student's engagement in class; and, for a subset of students, grades and achievement tests. Analyses of individual differences and individual growth curves (estimated using hierarchical linear modeling procedures) were consistent, not only with a cyclic model of context, self, action, and outcomes, but also with predictors of individual development over 5 years from grade 3 to grade 7. Children who experienced teachers as warm and contingent were more likely to develop optimal profiles of control; these beliefs supported more active engagement in the classroom, resulting in better academic performance; success in turn predicted the maintenance of optimistic beliefs about the effectiveness of effort. In contrast, children who experienced teachers as unsupportive were more likely to develop beliefs that emphasized external causes; these profiles of control predicted escalating classroom disaffection and lower scholastic achievement; in turn, these poor performances led children to increasingly doubt their own capacities and to believe even more strongly in the power of luck and unknown causes. Systematic age differences in analyses suggested that the aspects of control around which these cycles are organized change with development. The beliefs that regulated engagement shifted from effort to ability and from beliefs about the causes of school performance (strategy beliefs) to beliefs about the self's capacities. The feedback loop from individual performance to subsequent perceived control also became more pronounced and more focused on ability. These relatively linear developmental changes may have contributed to an abrupt decline in children's classroom engagement as they negotiated the transition to middle school and experienced losses in teacher support. Implications are discussed for future study of individual differences and development, especially the role of changing school contexts, mechanisms of influence, and developmentally appropriate interventions to optimize children's perceived control and engagement.

Comparative evaluation of a new lactation curve model for pasture-based Holstein-Friesian dairy cows.

PubMed

Adediran, S A; Ratkowsky, D A; Donaghy, D J; Malau-Aduli, A E O

2012-09-01

Fourteen lactation models were fitted to average and individual cow lactation data from pasture-based dairy systems in the Australian states of Victoria and Tasmania. The models included a new "log-quadratic" model, and a major objective was to evaluate and compare the performance of this model with the other models. Nine empirical and 5 mechanistic models were first fitted to average test-day milk yield of Holstein-Friesian dairy cows using the nonlinear procedure in SAS. Two additional semiparametric models were fitted using a linear model in ASReml. To investigate the influence of days to first test-day and the number of test-days, 5 of the best-fitting models were then fitted to individual cow lactation data. Model goodness of fit was evaluated using criteria such as the residual mean square, the distribution of residuals, the correlation between actual and predicted values, and the Wald-Wolfowitz runs test. Goodness of fit was similar in all but one of the models in terms of fitting average lactation but they differed in their ability to predict individual lactations. In particular, the widely used incomplete gamma model most displayed this failing. The new log-quadratic model was robust in fitting average and individual lactations, and was less affected by sampled data and more parsimonious in having only 3 parameters, each of which lends itself to biological interpretation. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Facial Recognition in a Discus Fish (Cichlidae): Experimental Approach Using Digital Models

PubMed Central

Satoh, Shun; Tanaka, Hirokazu; Kohda, Masanori

2016-01-01

A number of mammals and birds are known to be capable of visually discriminating between familiar and unfamiliar individuals, depending on facial patterns in some species. Many fish also visually recognize other conspecifics individually, and previous studies report that facial color patterns can be an initial signal for individual recognition. For example, a cichlid fish and a damselfish will use individual-specific color patterns that develop only in the facial area. However, it remains to be determined whether the facial area is an especially favorable site for visual signals in fish, and if so why? The monogamous discus fish, Symphysopdon aequifasciatus (Cichlidae), is capable of visually distinguishing its pair-partner from other conspecifics. Discus fish have individual-specific coloration patterns on entire body including the facial area, frontal head, trunk and vertical fins. If the facial area is an inherently important site for the visual cues, this species will use facial patterns for individual recognition, but otherwise they will use patterns on other body parts as well. We used modified digital models to examine whether discus fish use only facial coloration for individual recognition. Digital models of four different combinations of familiar and unfamiliar fish faces and bodies were displayed in frontal and lateral views. Focal fish frequently performed partner-specific displays towards partner-face models, and did aggressive displays towards models of non-partner’s faces. We conclude that to identify individuals this fish does not depend on frontal color patterns but does on lateral facial color patterns, although they have unique color patterns on the other parts of body. We discuss the significance of facial coloration for individual recognition in fish compared with birds and mammals. PMID:27191162

Facial Recognition in a Discus Fish (Cichlidae): Experimental Approach Using Digital Models.

PubMed

Satoh, Shun; Tanaka, Hirokazu; Kohda, Masanori

2016-01-01

A number of mammals and birds are known to be capable of visually discriminating between familiar and unfamiliar individuals, depending on facial patterns in some species. Many fish also visually recognize other conspecifics individually, and previous studies report that facial color patterns can be an initial signal for individual recognition. For example, a cichlid fish and a damselfish will use individual-specific color patterns that develop only in the facial area. However, it remains to be determined whether the facial area is an especially favorable site for visual signals in fish, and if so why? The monogamous discus fish, Symphysopdon aequifasciatus (Cichlidae), is capable of visually distinguishing its pair-partner from other conspecifics. Discus fish have individual-specific coloration patterns on entire body including the facial area, frontal head, trunk and vertical fins. If the facial area is an inherently important site for the visual cues, this species will use facial patterns for individual recognition, but otherwise they will use patterns on other body parts as well. We used modified digital models to examine whether discus fish use only facial coloration for individual recognition. Digital models of four different combinations of familiar and unfamiliar fish faces and bodies were displayed in frontal and lateral views. Focal fish frequently performed partner-specific displays towards partner-face models, and did aggressive displays towards models of non-partner's faces. We conclude that to identify individuals this fish does not depend on frontal color patterns but does on lateral facial color patterns, although they have unique color patterns on the other parts of body. We discuss the significance of facial coloration for individual recognition in fish compared with birds and mammals.

Multitasking in multiple sclerosis: can it inform vocational functioning?

PubMed

Morse, Chelsea L; Schultheis, Maria T; McKeever, Joshua D; Leist, Thomas

2013-12-01

To examine associations between multitasking ability defined by performance on a complex task integrating multiple cognitive domains and vocational functioning in multiple sclerosis (MS). Survey data collection. Laboratory with referrals from an outpatient clinic. Community-dwelling individuals with MS (N=30) referred between October 2011 and June 2012. Not applicable. The modified Six Elements Test (SET) to measure multitasking ability, Fatigue Severity Scale to measure fatigue, several neuropsychological measures of executive functioning, and vocational status. Among the sample, 60% of individuals have reduced their work hours because of MS symptoms (cutback employment group) and 40% had maintained their work hours. Among both groups, SET performance was significantly associated with performance on several measures of neuropsychological functioning. Individuals in the cutback employment group demonstrated significantly worse overall performance on the SET (P=.041). Logistic regression was used to evaluate associations between SET performance and vocational status, while accounting for neuropsychological performance and fatigue. The overall model was significant (χ(2)3=8.65, P=.032), with fatigue [Exp(B)=.83, P=.01] and multitasking ability [Exp(B)=.60, P=.043] retained as significant predictors. Multitasking ability may play an important role in performance at work for individuals with MS. Given that multitasking was associated with vocational functioning, future efforts should assess the usefulness of incorporating multitasking ability into rehabilitation planning. Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

The difference engine: a model of diversity in speeded cognition.

PubMed

Myerson, Joel; Hale, Sandra; Zheng, Yingye; Jenkins, Lisa; Widaman, Keith F

2003-06-01

A theory of diversity in speeded cognition, the difference engine, is proposed, in which information processing is represented as a series of generic computational steps. Some individuals tend to perform all of these computations relatively quickly and other individuals tend to perform them all relatively slowly, reflecting the existence of a general cognitive speed factor, but the time required for response selection and execution is assumed to be independent of cognitive speed. The difference engine correctly predicts the positively accelerated form of the relation between diversity of performance, as measured by the standard deviation for the group, and task difficulty, as indexed by the mean response time (RT) for the group. In addition, the difference engine correctly predicts approximately linear relations between the RTs of any individual and average performance for the group, with the regression lines for fast individuals having slopes less than 1.0 (and positive intercepts) and the regression lines for slow individuals having slopes greater than 1.0 (and negative intercepts). Similar predictions are made for comparisons of slow, average, and fast subgroups, regardless of whether those subgroups are formed on the basis of differences in ability, age, or health status. These predictions are consistent with evidence from studies of healthy young and older adults as well as from studies of depressed and age-matched control groups.

Individual differences in transcranial electrical stimulation current density

PubMed Central

Russell, Michael J; Goodman, Theodore; Pierson, Ronald; Shepherd, Shane; Wang, Qiang; Groshong, Bennett; Wiley, David F

2013-01-01

Transcranial electrical stimulation (TCES) is effective in treating many conditions, but it has not been possible to accurately forecast current density within the complex anatomy of a given subject's head. We sought to predict and verify TCES current densities and determine the variability of these current distributions in patient-specific models based on magnetic resonance imaging (MRI) data. Two experiments were performed. The first experiment estimated conductivity from MRIs and compared the current density results against actual measurements from the scalp surface of 3 subjects. In the second experiment, virtual electrodes were placed on the scalps of 18 subjects to model simulated current densities with 2 mA of virtually applied stimulation. This procedure was repeated for 4 electrode locations. Current densities were then calculated for 75 brain regions. Comparison of modeled and measured external current in experiment 1 yielded a correlation of r = .93. In experiment 2, modeled individual differences were greatest near the electrodes (ten-fold differences were common), but simulated current was found in all regions of the brain. Sites that were distant from the electrodes (e.g. hypothalamus) typically showed two-fold individual differences. MRI-based modeling can effectively predict current densities in individual brains. Significant variation occurs between subjects with the same applied electrode configuration. Individualized MRI-based modeling should be considered in place of the 10-20 system when accurate TCES is needed. PMID:24285948

A generic framework for individual-based modelling and physical-biological interaction

PubMed Central

2018-01-01

The increased availability of high-resolution ocean data globally has enabled more detailed analyses of physical-biological interactions and their consequences to the ecosystem. We present IBMlib, which is a versatile, portable and computationally effective framework for conducting Lagrangian simulations in the marine environment. The purpose of the framework is to handle complex individual-level biological models of organisms, combined with realistic 3D oceanographic model of physics and biogeochemistry describing the environment of the organisms without assumptions about spatial or temporal scales. The open-source framework features a minimal robust interface to facilitate the coupling between individual-level biological models and oceanographic models, and we provide application examples including forward/backward simulations, habitat connectivity calculations, assessing ocean conditions, comparison of physical circulation models, model ensemble runs and recently posterior Eulerian simulations using the IBMlib framework. We present the code design ideas behind the longevity of the code, our implementation experiences, as well as code performance benchmarking. The framework may contribute substantially to progresses in representing, understanding, predicting and eventually managing marine ecosystems. PMID:29351280

Individual differences and predictors of forgetting in old age: the role of processing speed and working memory.

PubMed

Zimprich, Daniel; Kurtz, Tanja

2013-01-01

The goal of the present study was to examine whether individual differences in basic cognitive abilities, processing speed, and working memory, are reliable predictors of individual differences in forgetting rates in old age. The sample for the present study comprised 364 participants aged between 65 and 80 years from the Zurich Longitudinal Study on Cognitive Aging. The impact of basic cognitive abilities on forgetting was analyzed by modeling working memory and processing speed as predictors of the amount of forgetting of 27 words, which had been learned across five trials. Forgetting was measured over a 30-minute interval by using parceling and a latent change model, in which the latent difference between recall performance after five learning trials and a delayed recall was modeled. Results implied reliable individual differences in forgetting. These individual differences in forgetting were strongly related to processing speed and working memory. Moreover, an age-related effect, which was significantly stronger for forgetting than for learning, emerged even after controlling effects of processing speed and working memory.

Poisson point process modeling for polyphonic music transcription.

PubMed

Peeling, Paul; Li, Chung-fai; Godsill, Simon

2007-04-01

Peaks detected in the frequency domain spectrum of a musical chord are modeled as realizations of a nonhomogeneous Poisson point process. When several notes are superimposed to make a chord, the processes for individual notes combine to give another Poisson process, whose likelihood is easily computable. This avoids a data association step linking individual harmonics explicitly with detected peaks in the spectrum. The likelihood function is ideal for Bayesian inference about the unknown note frequencies in a chord. Here, maximum likelihood estimation of fundamental frequencies shows very promising performance on real polyphonic piano music recordings.

A comparison of DEA and SFA using micro- and macro-level perspectives: Efficiency of Chinese local banks

NASA Astrophysics Data System (ADS)

Silva, Thiago Christiano; Tabak, Benjamin Miranda; Cajueiro, Daniel Oliveira; Dias, Marina Villas Boas

2017-03-01

This study investigates to which extent results produced by a single frontier model are reliable, based on the application of data envelopment analysis and stochastic frontier approach to a sample of Chinese local banks. Our findings show they produce a consistent trend on global efficiency scores over the years. However, rank correlations indicate they diverge with respect to individual performance diagnoses. Therefore, these models provide steady information on the efficiency of the banking system as a whole, but they become divergent at the individual level.

Applying mathematical models to predict resident physician performance and alertness on traditional and novel work schedules.

PubMed

Klerman, Elizabeth B; Beckett, Scott A; Landrigan, Christopher P

2016-09-13

In 2011 the U.S. Accreditation Council for Graduate Medical Education began limiting first year resident physicians (interns) to shifts of ≤16 consecutive hours. Controversy persists regarding the effectiveness of this policy for reducing errors and accidents while promoting education and patient care. Using a mathematical model of the effects of circadian rhythms and length of time awake on objective performance and subjective alertness, we quantitatively compared predictions for traditional intern schedules to those that limit work to ≤ 16 consecutive hours. We simulated two traditional schedules and three novel schedules using the mathematical model. The traditional schedules had extended duration work shifts (≥24 h) with overnight work shifts every second shift (including every third night, Q3) or every third shift (including every fourth night, Q4) night; the novel schedules had two different cross-cover (XC) night team schedules (XC-V1 and XC-V2) and a Rapid Cycle Rotation (RCR) schedule. Predicted objective performance and subjective alertness for each work shift were computed for each individual's schedule within a team and then combined for the team as a whole. Our primary outcome was the amount of time within a work shift during which a team's model-predicted objective performance and subjective alertness were lower than that expected after 16 or 24 h of continuous wake in an otherwise rested individual. The model predicted fewer hours with poor performance and alertness, especially during night-time work hours, for all three novel schedules than for either the traditional Q3 or Q4 schedules. Three proposed schedules that eliminate extended shifts may improve performance and alertness compared with traditional Q3 or Q4 schedules. Predicted times of worse performance and alertness were at night, which is also a time when supervision of trainees is lower. Mathematical modeling provides a quantitative comparison approach with potential to aid residency programs in schedule analysis and redesign.

A Hierarchical Bayesian Model for Crowd Emotions

PubMed Central

Urizar, Oscar J.; Baig, Mirza S.; Barakova, Emilia I.; Regazzoni, Carlo S.; Marcenaro, Lucio; Rauterberg, Matthias

2016-01-01

Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions. This paper proposes a hierarchical Bayesian model to learn in unsupervised manner the behavior of individuals and of the crowd as a single entity, and explore the relation between behavior and emotions to infer emotional states. Information about the motion patterns of individuals are described using a self-organizing map, and a hierarchical Bayesian network builds probabilistic models to identify behaviors and infer the emotional state of individuals and the crowd. This model is trained and tested using data produced from simulated scenarios that resemble real-life environments. The conducted experiments tested the efficiency of our method to learn, detect and associate behaviors with emotional states yielding accuracy levels of 74% for individuals and 81% for the crowd, similar in performance with existing methods for pedestrian behavior detection but with novel concepts regarding the analysis of crowds. PMID:27458366

Comparison of different artificial neural network architectures in modeling of Chlorella sp. flocculation.

PubMed

Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon

2017-07-03

Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.

Brain drain? An examination of stereotype threat effects during training on knowledge acquisition and organizational effectiveness.

PubMed

Grand, James A

2017-02-01

Stereotype threat describes a situation in which individuals are faced with the risk of upholding a negative stereotype about their subgroup based on their actions. Empirical work in this area has primarily examined the impact of negative stereotypes on performance for threatened individuals. However, this body of research seldom acknowledges that performance is a function of learning-which may also be impaired by pervasive group stereotypes. This study presents evidence from a 3-day self-guided training program demonstrating that stereotype threat impairs acquisition of cognitive learning outcomes for females facing a negative group stereotype. Using hierarchical Bayesian modeling, results revealed that stereotyped females demonstrated poorer declarative knowledge acquisition, spent less time reflecting on learning activities, and developed less efficiently organized knowledge structures compared with females in a control condition. Findings from a Bayesian mediation model also suggested that despite stereotyped individuals "working harder" to perform well, their underachievement was largely attributable to failures in learning to "work smarter." Building upon these empirical results, a computational model and computer simulation is also presented to demonstrate the practical significance of stereotype-induced impairments to learning on the development of an organization's human capital resources and capabilities. The simulation results show that even the presence of small effects of stereotype threat during learning/training have the potential to exert a significant negative impact on an organization's performance potential. Implications for future research and practice examining stereotype threat during learning are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Discovering the Power of Individual-Based Modelling in Teaching and Learning: The Study of a Predator-Prey System

NASA Astrophysics Data System (ADS)

Ginovart, Marta

2014-08-01

The general aim is to promote the use of individual-based models (biological agent-based models) in teaching and learning contexts in life sciences and to make their progressive incorporation into academic curricula easier, complementing other existing modelling strategies more frequently used in the classroom. Modelling activities for the study of a predator-prey system for a mathematics classroom in the first year of an undergraduate program in biosystems engineering have been designed and implemented. These activities were designed to put two modelling approaches side by side, an individual-based model and a set of ordinary differential equations. In order to organize and display this, a system with wolves and sheep in a confined domain was considered and studied. With the teaching material elaborated and a computer to perform the numerical resolutions involved and the corresponding individual-based simulations, the students answered questions and completed exercises to achieve the learning goals set. Students' responses regarding the modelling of biological systems and these two distinct methodologies applied to the study of a predator-prey system were collected via questionnaires, open-ended queries and face-to-face dialogues. Taking into account the positive responses of the students when they were doing these activities, it was clear that using a discrete individual-based model to deal with a predator-prey system jointly with a set of ordinary differential equations enriches the understanding of the modelling process, adds new insights and opens novel perspectives of what can be done with computational models versus other models. The complementary views given by the two modelling approaches were very well assessed by students.

Genomic prediction in a nuclear population of layers using single-step models.

PubMed

Yan, Yiyuan; Wu, Guiqin; Liu, Aiqiao; Sun, Congjiao; Han, Wenpeng; Li, Guangqi; Yang, Ning

2018-02-01

Single-step genomic prediction method has been proposed to improve the accuracy of genomic prediction by incorporating information of both genotyped and ungenotyped animals. The objective of this study is to compare the prediction performance of single-step model with a 2-step models and the pedigree-based models in a nuclear population of layers. A total of 1,344 chickens across 4 generations were genotyped by a 600 K SNP chip. Four traits were analyzed, i.e., body weight at 28 wk (BW28), egg weight at 28 wk (EW28), laying rate at 38 wk (LR38), and Haugh unit at 36 wk (HU36). In predicting offsprings, individuals from generation 1 to 3 were used as training data and females from generation 4 were used as validation set. The accuracies of predicted breeding values by pedigree BLUP (PBLUP), genomic BLUP (GBLUP), SSGBLUP and single-step blending (SSBlending) were compared for both genotyped and ungenotyped individuals. For genotyped females, GBLUP performed no better than PBLUP because of the small size of training data, while the 2 single-step models predicted more accurately than the PBLUP model. The average predictive ability of SSGBLUP and SSBlending were 16.0% and 10.8% higher than the PBLUP model across traits, respectively. Furthermore, the predictive abilities for ungenotyped individuals were also enhanced. The average improvements of prediction abilities were 5.9% and 1.5% for SSGBLUP and SSBlending model, respectively. It was concluded that single-step models, especially the SSGBLUP model, can yield more accurate prediction of genetic merits and are preferable for practical implementation of genomic selection in layers. © 2017 Poultry Science Association Inc.

Dynamic User Modeling within a Game-Based ITS

ERIC Educational Resources Information Center

Snow, Erica L.

2015-01-01

Intelligent tutoring systems are adaptive learning environments designed to support individualized instruction. The adaptation embedded within these systems is often guided by user models that represent one or more aspects of students' domain knowledge, actions, or performance. The proposed project focuses on the development and testing of user…

Pharmacokinetic/pharmacodynamic modeling of psychomotor impairment induced by oral clonazepam in healthy volunteers.

PubMed

dos Santos, Fábio Monteiro; Gonçalves, José Carlos Saraiva; Caminha, Ricardo; da Silveira, Gabriel Estolano; Neves, Claúdia Silvana de Miranda; Gram, Karla Regina da Silva; Ferreira, Carla Teixeira; Jacqmin, Philippe; Noël, François

2009-10-01

This study was undertaken to model the relationship between clonazepam plasma concentrations and a central nervous system adverse effect (impairment of the psychomotor performance) following the oral administration of immediate-release tablets of clonazepam in healthy volunteers. Such a (P)pharmacokinetic/(P)pharmacodynamic (PK/PD) study is important to interpret properly the consequences of determined levels of plasma concentrations of psychoactive therapeutic drugs reported to be involved in road-traffic accidents. Twenty-three male subjects received a single oral dose of 4 mg clonazepam. Plasma concentration, determined by on-line solid phase extraction coupled with high-performance liquid chromatography tandem mass spectrometry, and psychomotor performance, quantified through the Digit Symbol Substitution Test, were monitored for 72 hours. A 2-compartment open model with first order absorption and lag-time better fitted the plasma clonazepam concentrations. Clonazepam decreased the psychomotor performance by 72 +/- 3.7% (observed maximum effect), 1.5 to 4 hours (25th-75th percentile) after drug administration. A simultaneous population PK/PD model based on a sigmoid Emax model with time-dependent tolerance described well the time course of effect. Such acute tolerance could minimize the risk of accident as a result of impairment of motor skill after a single dose of clonazepam. However, an individual analysis of the data revealed a great interindividual variation in the relationship between clonazepam effect and plasma concentration, indicating that the phenomenon of acute tolerance can be predicted at a population, but not individual, level.

MOPADS (Models of Operator Performance in Air Defense Systems). Appendices

DTIC Science & Technology

1984-11-01

differential equation models which aggregate and smooth individual events to obtain overall average performance measures. The advantages, in the MOPADS...Positive z is up. Each critical asset is specified by its coordinates anod a label. Growth potential is allowed in the data base for differentiating among...OlUI Figure IX-1. (Continued) C- 115 MAIN CATEGORY SELtCTION:SAINT USER ITATISITICS SECONDARY CATEGORT HE ,U1 * lIlT PRINT COMMAND MN - TATIS [TICS

Latent class analysis of the feared situations of social anxiety disorder: A population-based study.

PubMed

Peyre, Hugo; Hoertel, Nicolas; Rivollier, Fabrice; Landman, Benjamin; McMahon, Kibby; Chevance, Astrid; Lemogne, Cédric; Delorme, Richard; Blanco, Carlos; Limosin, Frédéric

2016-12-01

Little is known about differences in mental health comorbidity and quality of life in individuals with social anxiety disorder (SAD) according to the number and the types of feared situations. Using a US nationally representative sample, the National Epidemiologic Survey on Alcohol and Related Conditions, we performed latent class analysis to compare the prevalence rates of mental disorders and quality of life measures across classes defined by the number and the types of feared social situations among individuals with SAD. Among the 2,448 participants with a lifetime diagnosis of SAD, we identified three classes of individuals who feared most social situations but differed in the number of feared social situations (generalized severe [N = 378], generalized moderate [N = 1,049] and generalized low [N = 443]) and a class of subjects who feared only performance situations [N = 578]. The magnitude of associations between each class and a wide range of mental disorders and quality of life measures were consistent with a continuum model, supporting that the deleterious effects of SAD on mental health may increase with the number of social situations feared. However, we found that individuals with the "performance only" specifier may constitute an exception to this model because these participants had significantly better mental health than other participants with SAD. Our findings give additional support to the recent changes made in the DSM-5, including the introduction of the "performance only" specifier and the removal of the "generalized" specifier to promote the dimensional approach of the number of social fears. © 2016 Wiley Periodicals, Inc.

Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk and the Prediction of Psychosis: Second Replication in an Independent National Health Service Trust.

PubMed

Fusar-Poli, Paolo; Werbeloff, Nomi; Rutigliano, Grazia; Oliver, Dominic; Davies, Cathy; Stahl, Daniel; McGuire, Philip; Osborn, David

2018-06-12

The benefits of indicated primary prevention among individuals at Clinical High Risk for Psychosis (CHR-P) are limited by the difficulty in detecting these individuals. To overcome this problem, a transdiagnostic, clinically based, individualized risk calculator has recently been developed and subjected to a first external validation in 2 different catchment areas of the South London and Maudsley (SLaM) NHS Trust. Second external validation of real world, real-time electronic clinical register-based cohort study. All individuals who received a first ICD-10 index diagnosis of nonorganic and nonpsychotic mental disorder within the Camden and Islington (C&I) NHS Trust between 2009 and 2016 were included. The model previously validated included age, gender, ethnicity, age by gender, and ICD-10 index diagnosis to predict the development of any ICD-10 nonorganic psychosis. The model's performance was measured using Harrell's C-index. This study included a total of 13702 patients with an average age of 40 (range 16-99), 52% were female, and most were of white ethnicity (64%). There were no CHR-P or child/adolescent services in the C&I Trust. The C&I and SLaM Trust samples also differed significantly in terms of age, gender, ethnicity, and distribution of index diagnosis. Despite these significant differences, the original model retained an acceptable predictive performance (Harrell's C of 0.73), which is comparable to that of CHR-P tools currently recommended for clinical use. This risk calculator may pragmatically support an improved transdiagnostic detection of at-risk individuals and psychosis prediction even in NHS Trusts in the United Kingdom where CHR-P services are not provided.

INDIVIDUAL-BASED MODELS: POWERFUL OR POWER STRUGGLE?

PubMed

Willem, L; Stijven, S; Hens, N; Vladislavleva, E; Broeckhove, J; Beutels, P

2015-01-01

Individual-based models (IBMs) offer endless possibilities to explore various research questions but come with high model complexity and computational burden. Large-scale IBMs have become feasible but the novel hardware architectures require adapted software. The increased model complexity also requires systematic exploration to gain thorough system understanding. We elaborate on the development of IBMs for vaccine-preventable infectious diseases and model exploration with active learning. Investment in IBM simulator code can lead to significant runtime reductions. We found large performance differences due to data locality. Sorting the population once, reduced simulation time by a factor two. Storing person attributes separately instead of using person objects also seemed more efficient. Next, we improved model performance up to 70% by structuring potential contacts based on health status before processing disease transmission. The active learning approach we present is based on iterative surrogate modelling and model-guided experimentation. Symbolic regression is used for nonlinear response surface modelling with automatic feature selection. We illustrate our approach using an IBM for influenza vaccination. After optimizing the parameter spade, we observed an inverse relationship between vaccination coverage and the clinical attack rate reinforced by herd immunity. These insights can be used to focus and optimise research activities, and to reduce both dimensionality and decision uncertainty.

Statistical analysis of the effect of temperature and inlet humidities on the parameters of a semiempirical model of the internal resistance of a polymer electrolyte membrane fuel cell

NASA Astrophysics Data System (ADS)

Giner-Sanz, J. J.; Ortega, E. M.; Pérez-Herranz, V.

2018-03-01

The internal resistance of a PEM fuel cell depends on the operation conditions and on the current delivered by the cell. This work's goal is to obtain a semiempirical model able to reproduce the effect of the operation current on the internal resistance of an individual cell of a commercial PEM fuel cell stack; and to perform a statistical analysis in order to study the effect of the operation temperature and the inlet humidities on the parameters of the model. First, the internal resistance of the individual fuel cell operating in different operation conditions was experimentally measured for different DC currents, using the high frequency intercept of the impedance spectra. Then, a semiempirical model based on Springer and co-workers' model was proposed. This model is able to successfully reproduce the experimental trends. Subsequently, the curves of resistance versus DC current obtained for different operation conditions were fitted to the semiempirical model, and an analysis of variance (ANOVA) was performed in order to determine which factors have a statistically significant effect on each model parameter. Finally, a response surface method was applied in order to obtain a regression model.

The contribution to immediate serial recall of rehearsal, search speed, access to lexical memory, and phonological coding: an investigation at the construct level.

PubMed

Tehan, Gerald; Fogarty, Gerard; Ryan, Katherine

2004-07-01

Rehearsal speed has traditionally been seen to be the prime determinant of individual differences in memory span. Recent studies, in the main using young children as the participant population, have suggested other contributors to span performance. In the present research, we used structural equation modeling to explore, at the construct level, individual differences in immediate serial recall with respect to rehearsal, search, phonological coding, and speed of access to lexical memory. We replicated standard short-term phenomena; we showed that the variables that influence children's span performance influence adult performance in the same way; and we showed that speed of access to lexical memory and facility with phonological codes appear to be more potent sources of individual differences in immediate memory than is either rehearsal speed or search factors.

Developmental Climate: A Cross-level Analysis of Voluntary Turnover and Job Performance

PubMed Central

Spell, Hannah B.; Eby, Lillian T.; Vandenberg, Robert J.

2014-01-01

This research investigates the influence of shared perceptions of developmental climate on individual-level perceptions of organizational commitment, engagement, and perceived competence, and whether these attitudes mediate the relationship between developmental climate and both individual voluntary turnover and supervisor-rated job performance. Survey data were collected from 361 intact employee-supervisory mentoring dyads and matched with employee turnover data collected one year later to test the proposed framework using multilevel modeling techniques. As expected, shared perceptions of developmental climate were significantly and positively related to all three individual work attitudes. In addition, both organizational commitment and perceived competence were significant mediators of the positive relationship between shared perceptions of developmental climate and voluntary turnover, as well as shared perceptions of developmental climate and supervisor-rated job performance. By contrast, no significant mediating effects were found for engagement. Theoretical implications, limitations, and future research are discussed. PMID:24748681

SIR model on a dynamical network and the endemic state of an infectious disease

NASA Astrophysics Data System (ADS)

Dottori, M.; Fabricius, G.

2015-09-01

In this work we performed a numerical study of an epidemic model that mimics the endemic state of whooping cough in the pre-vaccine era. We considered a stochastic SIR model on dynamical networks that involve local and global contacts among individuals and analysed the influence of the network properties on the characterization of the quasi-stationary state. We computed probability density functions (PDF) for infected fraction of individuals and found that they are well fitted by gamma functions, excepted the tails of the distributions that are q-exponentials. We also computed the fluctuation power spectra of infective time series for different networks. We found that network effects can be partially absorbed by rescaling the rate of infective contacts of the model. An explicit relation between the effective transmission rate of the disease and the correlation of susceptible individuals with their infective nearest neighbours was obtained. This relation quantifies the known screening of infective individuals observed in these networks. We finally discuss the goodness and limitations of the SIR model with homogeneous mixing and parameters taken from epidemiological data to describe the dynamic behaviour observed in the networks studied.

A model to assess the emission of individual isoprenoids emitted from Italian ecosystems

NASA Astrophysics Data System (ADS)

Kemper Pacheco, C. J.; Fares, S.; Loreto, F.; Ciccioli, P.

2012-04-01

The aim of this work was to develop a GIS-based model to estimate the emissions from the Italian forest ecosystems. The model was aimed at generating a species-specific emission inventory for isoprene and individual monoterpenes that could have been validated with experimental data collected in selected sites of the CARBOITALY network. The model was develop for the year 2006. At a resolution of 1 km2 with a daily time resolution. By using the emission rates of individual components obtained through several laboratory and field experiments carried out on different vegetation species of the Mediterranean basin, maps of individual isoprenoids were generated for the Italian ecosystems. The spatial distribution and fractional contents of vegetation species present in the Italian forest ecosystems was obtained by combining the CORINE IV land cover map with National Forest Inventory based on ground observations performed at local levels by individual Italian regions (22) in which the country is divided. In general, basal emission rates for individual isoprenoids was reported by Steinbrecher et al. 1997 and Karl et al. 2009 were used. In this case, classes were further subdivided into T and L+T emitters as functions of the active pool. In many instances, however they were revised based on the results obtained in our Institute through determinations performed at leaf, branch (cuvette method) or ecosystem level (REA and the gradient method). In the latter case, studies performed in Italy and/or Mediterranean countries were used. An empirical light extinction function as a function of the canopy type and structure was introduced. The algorithms proposed by (Guenther et al. 1993) were used, but, they were often adapted to fit with the experimental observations made in the Mediterranean Areas. They were corrected for a seasonality factor (Steinbrecher et al. 2009) taking into account a time lag in leaf sprouting due to the plant elevation. A simple parameterization with LAI was introduced to account for the amount of monoterpene biomass from the litter of stands composed by plants equipped with storage organs. Daily data of incident PAR and leaf temperature obtained from high resolved satellite observation were provided by the partners of the CARBOITALY Project. They were available for the entire year 2006. They were disaggregated into proper day-night cycles. Emission values predicted by the model are in perfect agreement with those that were measured by different micrometeorological techniques in Castelporzioano (Ciccioli et al, J Chromatogr., 2003) and in the Collelongo site (Ciccioli et al. unpublished). The good correlation between modeled and measured values emphasizes the fact that accurate predictions can be obtained if validated emission factors for individual VOC are used in the model. The almost equivalent potential emission of isoprene and monoterpenes reported in a previous work was confirmed, although lower values of total biogenic emissions were found for both classes of hydrocarbons. Data from individual monoterpenes indicates also that highly reactive cis- and b-ocimenes are also quite abundant in many Italian forest ecosystems, including those dominated by coniferous trees, such as Pinus pinaster and sylvestris. This may lead to rather low dominance of pinene generated particles in the air. The high spatial and temporal resolution, combined with the species-specific emission output makes our model particularly suitable for ozono and SOA prediction with both Eulerian and Lagrangian photochemical models, at the scale at which ozono pollution develops in Italy.

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction.

PubMed

Papantoniou, Panagiotis

2018-04-03

The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures. For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures. Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance. The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.

Ground temperature measurement by PRT-5 for maps experiment

NASA Technical Reports Server (NTRS)

Gupta, S. K.; Tiwari, S. N.

1978-01-01

A simple algorithm and computer program were developed for determining the actual surface temperature from the effective brightness temperature as measured remotely by a radiation thermometer called PRT-5. This procedure allows the computation of atmospheric correction to the effective brightness temperature without performing detailed radiative transfer calculations. Model radiative transfer calculations were performed to compute atmospheric corrections for several values of the surface and atmospheric parameters individually and in combination. Polynomial regressions were performed between the magnitudes or deviations of these parameters and the corresponding computed corrections to establish simple analytical relations between them. Analytical relations were also developed to represent combined correction for simultaneous variation of parameters in terms of their individual corrections.

Burnout in teachers: shattered dreams of impeccable professional performance.

PubMed

Friedman, I A

2000-05-01

Burnout usually is conceptualized as a work-related syndrome stemming from the individual's perception of a significant gap between expectations of successful professional performance and an observed, far less satisfying reality. The article examines this perception as a discrepancy between expected and observed levels of the individual's professional self-efficacy. The teaching profession and its service providers--teachers--serve as a model to illustrate and support this examination. Self-reports of novice teachers' experiences in their first year of teaching are given, reflecting a world of shattered dreams of idealistic performance. Finally, a number of suggestions for programs and activities that have proven helpful in alleviating stress and burnout among teachers are described.

Lower- extremity biomechanics and maintenance of vertical-jump height during prolonged intermittent exercise.

PubMed

Schmitz, Randy J; Cone, John C; Copple, Timothy J; Henson, Robert A; Shultz, Sandra J

2014-11-01

Potential biomechanical compensations allowing for maintenance of maximal explosive performance during prolonged intermittent exercise, with respect to the corresponding rise in injury rates during the later stages of exercise or competition, are relatively unknown. To identify lower-extremity countermovement-jump (CMJ) biomechanical factors using a principal-components approach and then examine how these factors changed during a 90-min intermittent-exercise protocol (IEP) while maintaining maximal jump height. Mixed-model design. Laboratory. Fifty-nine intermittent-sport athletes (30 male, 29 female) participated in experimental and control conditions. Before and after a dynamic warm-up and every 15 min during the 1st and 2nd halves of an individually prescribed 90-min IEP, participants were assessed on rating of perceived exertion, sprint/cut speed, and 3-dimensional CMJ biomechanics (experimental). On a separate day, the same measures were obtained every 15 min during 90 min of quiet rest (control). Univariate piecewise growth models analyzed progressive changes in CMJ performance and biomechanical factors extracted from a principal-components analysis of the individual biomechanical dependent variables. While CMJ height was maintained during the 1st and 2nd halves, the body descended less and knee kinetic and energetic magnitudes decreased as the IEP progressed. The results indicate that vertical-jump performance is maintained along with progressive biomechanical changes commonly associated with decreased performance. A better understanding of lower-extremity biomechanics during explosive actions in response to IEP allows us to further develop and individualize performance training programs.

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets

PubMed Central

2013-01-01

Background While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants. Results The amino acid descriptor sets compared here show similar performance (<0.1 log units RMSE difference and <0.1 difference in MCC), while errors for individual proteins were in some cases found to be larger than those resulting from descriptor set differences ( > 0.3 log units RMSE difference and >0.7 difference in MCC). Combining different descriptor sets generally leads to better modeling performance than utilizing individual sets. The best performers were Z-scales (3) combined with ProtFP (Feature), or Z-Scales (3) combined with an average Z-Scale value for each target, while ProtFP (PCA8), ST-Scales, and ProtFP (Feature) rank last. Conclusions While amino acid descriptor sets capture different aspects of amino acids their ability to be used for bioactivity modeling is still – on average – surprisingly similar. Still, combining sets describing complementary information consistently leads to small but consistent improvement in modeling performance (average MCC 0.01 better, average RMSE 0.01 log units lower). Finally, performance differences exist between the targets compared thereby underlining that choosing an appropriate descriptor set is of fundamental for bioactivity modeling, both from the ligand- as well as the protein side. PMID:24059743

A model for teaching and learning spinal thrust manipulation and its effect on participant confidence in technique performance.

PubMed

Wise, Christopher H; Schenk, Ronald J; Lattanzi, Jill Black

2016-07-01

Despite emerging evidence to support the use of high velocity thrust manipulation in the management of lumbar spinal conditions, utilization of thrust manipulation among clinicians remains relatively low. One reason for the underutilization of these procedures may be related to disparity in training in the performance of these techniques at the professional and post professional levels. To assess the effect of using a new model of active learning on participant confidence in the performance of spinal thrust manipulation and the implications for its use in the professional and post-professional training of physical therapists. A cohort of 15 DPT students in their final semester of entry-level professional training participated in an active training session emphasizing a sequential partial task practice (SPTP) strategy in which participants engaged in partial task practice over several repetitions with different partners. Participants' level of confidence in the performance of these techniques was determined through comparison of pre- and post-training session surveys and a post-session open-ended interview. The increase in scores across all items of the individual pre- and post-session surveys suggests that this model was effective in changing overall participant perception regarding the effectiveness and safety of these techniques and in increasing student confidence in their performance. Interviews revealed that participants greatly preferred the SPTP strategy, which enhanced their confidence in technique performance. Results indicate that this new model of psychomotor training may be effective at improving confidence in the performance of spinal thrust manipulation and, subsequently, may be useful for encouraging the future use of these techniques in the care of individuals with impairments of the spine. Inasmuch, this method of instruction may be useful for training of physical therapists at both the professional and post-professional levels.

Autonomous visual exploration creates developmental change in familiarity and novelty seeking behaviors

PubMed Central

Perone, Sammy; Spencer, John P.

2013-01-01

What motivates children to radically transform themselves during early development? We addressed this question in the domain of infant visual exploration. Over the first year, infants' exploration shifts from familiarity to novelty seeking. This shift is delayed in preterm relative to term infants and is stable within individuals over the course of the first year. Laboratory tasks have shed light on the nature of this familiarity-to-novelty shift, but it is not clear what motivates the infant to change her exploratory style. We probed this by letting a Dynamic Neural Field (DNF) model of visual exploration develop itself via accumulating experience in a virtual world. We then situated it in a canonical laboratory task. Much like infants, the model exhibited a familiarity-to-novelty shift. When we manipulated the initial conditions of the model, the model's performance was developmentally delayed much like preterm infants. This delay was overcome by enhancing the model's experience during development. We also found that the model's performance was stable at the level of the individual. Our simulations indicate that novelty seeking emerges with no explicit motivational source via the accumulation of visual experience within a complex, dynamical exploratory system. PMID:24065948

Differences in mobility at the range edge of an expanding invasive population of Xenopus laevis in the west of France.

PubMed

Louppe, Vivien; Courant, Julien; Herrel, Anthony

2017-01-15

Theoretical models predict that spatial sorting at the range edge of expanding populations should favor individuals with increased mobility relative to individuals at the center of the range. Despite the fact that empirical evidence for the evolution of locomotor performance at the range edge is rare, data on cane toads support this model. However, whether this can be generalized to other species remains largely unknown. Here, we provide data on locomotor stamina and limb morphology in individuals from two sites: one from the center and one from the periphery of an expanding population of the clawed frog Xenopus laevis in France where it was introduced about 30 years ago. Additionally, we provide data on the morphology of frogs from two additional sites to test whether the observed differences can be generalized across the range of this species in France. Given the known sexual size dimorphism in this species, we also test for differences between the sexes in locomotor performance and morphology. Our results show significant sexual dimorphism in stamina and morphology, with males having longer legs and greater stamina than females. Moreover, in accordance with the predictions from theoretical models, individuals from the range edge had a greater stamina. This difference in locomotor performance is likely to be driven by the significantly longer limb segments observed in animals in both sites sampled in different areas along the range edge. Our data have implications for conservation because spatial sorting on the range edge may lead to an accelerated increase in the spread of this invasive species in France. © 2017. Published by The Company of Biologists Ltd.

Intercomparison of the community multiscale air quality model and CALGRID using process analysis.

PubMed

O'Neill, Susan M; Lamb, Brian K

2005-08-01

This study was designed to examine the similarities and differences between two advanced photochemical air quality modeling systems: EPA Models-3/CMAQ and CALGRID/CALMET. Both modeling systems were applied to an ozone episode that occurred along the I-5 urban corridor in western Washington and Oregon during July 11-14, 1996. Both models employed the same modeling domain and used the same detailed gridded emission inventory. The CMAQ model was run using both the CB-IV and RADM2 chemical mechanisms, while CALGRID was used with the SAPRC-97 chemical mechanism. Outputfrom the Mesoscale Meteorological Model (MM5) employed with observational nudging was used in both models. The two modeling systems, representing three chemical mechanisms and two sets of meteorological inputs, were evaluated in terms of statistical performance measures for both 1- and 8-h average observed ozone concentrations. The results showed that the different versions of the systems were more similar than different, and all versions performed well in the Portland region and downwind of Seattle but performed poorly in the more rural region north of Seattle. Improving the meteorological input into the CALGRID/CALMET system with planetary boundary layer (PBL) parameters from the Models-3/CMAQ meteorology preprocessor (MCIP) improved the performance of the CALGRID/CALMET system. The 8-h ensemble case was often the best performer of all the cases indicating that the models perform better over longer analysis periods. The 1-h ensemble case, derived from all runs, was not necessarily an improvement over the five individual cases, but the standard deviation about the mean provided a measure of overall modeling uncertainty. Process analysis was applied to examine the contribution of the individual processes to the species conservation equation. The process analysis results indicated that the two modeling systems arrive at similar solutions by very different means. Transport rates are faster and exhibit greater fluctuations in the CMAQ cases than in the CALGRID cases, which lead to different placement of the urban ozone plumes. The CALGRID cases, which rely on the SAPRC97 chemical mechanism, exhibited a greater diurnal production/loss cycle of ozone concentrations per hour compared to either the RADM2 or CBIV chemical mechanisms in the CMAQ cases. These results demonstrate the need for specialized process field measurements to confirm whether we are modeling ozone with valid processes.

Beyond the blank slate: routes to learning new coordination patterns depend on the intrinsic dynamics of the learner—experimental evidence and theoretical model

PubMed Central

Kostrubiec, Viviane; Zanone, Pier-Giorgio; Fuchs, Armin; Kelso, J. A. Scott

2012-01-01

Using an approach that combines experimental studies of bimanual movements to visual stimuli and theoretical modeling, the present paper develops a dynamical account of sensorimotor learning, that is, how new skills are acquired and old ones modified. A significant aspect of our approach is the focus on the individual learner as the basic unit of analysis, in particular the quantification of predispositions and capabilities that the individual learner brings to the learning environment. Such predispositions constitute the learner's behavioral repertoire, captured here theoretically as a dynamical landscape (“intrinsic dynamics”). The learning process is demonstrated to not only lead to a relatively permanent improvement of performance in the required task—the usual outcome—but also to alter the individual's entire repertoire. Changes in the dynamical landscape due to learning are shown to result from two basic mechanisms or “routes”: bifurcation and shift. Which mechanism is selected depends the initial individual repertoire before new learning begins. Both bifurcation and shift mechanisms are accommodated by a dynamical model, a relatively straightforward development of the well-established HKB model of movement coordination. Model simulations show that although environmental or task demands may be met equally well using either mechanism, the bifurcation route results in greater stabilization of the to-be-learned behavior. Thus, stability not (or not only) error is demonstrated to be the basis of selection, both of a new pattern of behavior and the path (smooth shift versus abrupt qualitative change) that learning takes. In line with these results, recent neurophysiological evidence indicates that stability is a relevant feature around which brain activity is organized while an individual performs a coordination task. Finally, we explore the consequences of the dynamical approach to learning for theories of biological change. PMID:22876227

A Diffusion Model Analysis of Episodic Recognition in Individuals with a Family History for Alzheimer Disease: The Adult Children Study

PubMed Central

Aschenbrenner, Andrew J.; Balota, David A.; Gordon, Brian A.; Ratcliff, Roger; Morris, John C.

2015-01-01

Objective A family history of Alzheimer disease (AD) increases the risk of developing AD and can influence the accumulation of well-established AD biomarkers. There is some evidence that family history can influence episodic memory performance even in cognitively normal individuals. We attempted to replicate the effect of family history on episodic memory and used a specific computational model of binary decision making (the diffusion model) to understand precisely how family history influences cognition. Finally, we assessed the sensitivity of model parameters to family history controlling for standard neuropsychological test performance. Method Across two experiments, cognitively healthy participants from the Adult Children Study completed an episodic recognition test consisting of high and low frequency words. The diffusion model was applied to decompose accuracy and reaction time into latent parameters which were analyzed as a function of family history. Results In both experiments, individuals with a family history of AD exhibited lower recognition accuracy and this occurred in the absence of an apolipoprotein E (APOE) ε4 allele. The diffusion model revealed this difference was due to changes in the quality of information accumulation (the drift rate) and not differences in response caution or other model parameters. This difference remained after controlling for several standard neuropsychological tests. Conclusions These results confirm that the presence of a family history of AD confers a subtle cognitive deficit in episodic memory as reflected by decreased drift rate that cannot be attributed to APOE. This measure may serve as a novel cognitive marker of preclinical AD. PMID:26192539

Nomogram for Predicting the Benefit of Adjuvant Chemoradiotherapy for Resected Gallbladder Cancer

PubMed Central

Wang, Samuel J.; Lemieux, Andrew; Kalpathy-Cramer, Jayashree; Ord, Celine B.; Walker, Gary V.; Fuller, C. David; Kim, Jong-Sung; Thomas, Charles R.

2011-01-01

Purpose Although adjuvant chemoradiotherapy for resected gallbladder cancer may improve survival for some patients, identifying which patients will benefit remains challenging because of the rarity of this disease. The specific aim of this study was to create a decision aid to help make individualized estimates of the potential survival benefit of adjuvant chemoradiotherapy for patients with resected gallbladder cancer. Methods Patients with resected gallbladder cancer were selected from the Surveillance, Epidemiology, and End Results (SEER) –Medicare database who were diagnosed between 1995 and 2005. Covariates included age, race, sex, stage, and receipt of adjuvant chemotherapy or chemoradiotherapy (CRT). Propensity score weighting was used to balance covariates between treated and untreated groups. Several types of multivariate survival regression models were constructed and compared, including Cox proportional hazards, Weibull, exponential, log-logistic, and lognormal models. Model performance was compared using the Akaike information criterion. The primary end point was overall survival with or without adjuvant chemotherapy or CRT. Results A total of 1,137 patients met the inclusion criteria for the study. The lognormal survival model showed the best performance. A Web browser–based nomogram was built from this model to make individualized estimates of survival. The model predicts that certain subsets of patients with at least T2 or N1 disease will gain a survival benefit from adjuvant CRT, and the magnitude of benefit for an individual patient can vary. Conclusion A nomogram built from a parametric survival model from the SEER-Medicare database can be used as a decision aid to predict which gallbladder patients may benefit from adjuvant CRT. PMID:22067404

Village power options

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

Lilienthal, P.

1997-12-01

This paper describes three different computer codes which have been written to model village power applications. The reasons which have driven the development of these codes include: the existance of limited field data; diverse applications can be modeled; models allow cost and performance comparisons; simulations generate insights into cost structures. The models which are discussed are: Hybrid2, a public code which provides detailed engineering simulations to analyze the performance of a particular configuration; HOMER - the hybrid optimization model for electric renewables - which provides economic screening for sensitivity analyses; and VIPOR the village power model - which is amore » network optimization model for comparing mini-grids to individual systems. Examples of the output of these codes are presented for specific applications.« less

Personality and organizational influences on aerospace human performance

NASA Technical Reports Server (NTRS)

Helmreich, Robert L.

1989-01-01

Individual and organizational influences on performance in aerospace environments are discussed. A model of personality with demonstrated validity is described along with reasons why personality's effects on performance have been underestimated. Organizational forces including intergroup conflict and coercive pressures are also described. It is suggested that basic and applied research in analog situations is needed to provide necessary guidance for planning future space missions.

Chinese College Test Takers' Individual Differences and Reading Test Performance: A Structural Equation Modeling Approach.

PubMed

Zhang, Limei

2016-06-01

This study reports on the relationships between test takers' individual differences and their performance on a reading comprehension test. A total of 518 Chinese college students (252 women and 256 men; M age = 19.26 year, SD = 0.98) answered a questionnaire and sit for a reading comprehension test. The study found that test takers' L2 language proficiency was closely linked to their test performance. Test takers' employment of strategies was significantly and positively associated with their performance on the test. Test takers' motivation was found to be significantly associated with reading test performance. Test anxiety was negatively related to their use of reading strategies and test performance. The results of the study lent support to the threshold hypothesis of language proficiency. The implications for classroom teaching were provided. © The Author(s) 2016.

Disabled Students in the Performing Arts--Are We Setting Them up to Succeed?

ERIC Educational Resources Information Center

Band, Susan Ann; Lindsay, Geoff; Neelands, Jonothan; Freakley, Vivien

2011-01-01

Professional training opportunities for students with physical and learning disabilities in the performing arts are conceived and developed in the context of government policy initiatives for inclusion and models of disability that aim to ensure that educational provision is of a kind which does not stigmatise individuals or devalue their…

The Interaction of Functional and Dysfunctional Emotions during Balance Beam Performance

ERIC Educational Resources Information Center

Cottyn, Jorge; De Clercq, Dirk; Crombez, Geert; Lenoir, Matthieu

2012-01-01

The interaction between functional and dysfunctional emotions, as one of the major tenets of the Individual Zones of Optimal Functioning (IZOF) model (Hanin, 2000), was studied in a sport specific setting. Fourteen female gymnasts performed three attempts of a compulsory balance beam routine at three different heights. Heart rate and self-report…

The Influence of Proactive Socialization Behaviors and Team Socialization on Individual Performance in the Team

ERIC Educational Resources Information Center

Pennaforte, Antoine

2016-01-01

On the basis of the role and the social exchange theories, this research investigated the direct and indirect antecedents of three dimensions of team performance (proficiency, adaptivity, proactivity) developed through cooperative education. The theoretical model examined how proactive socialization behaviors led to team socialization and team…

Molecular Mechanisms Underlying Individual Differences in Response to Stress in a Previously Validated Animal Model of PTSD

DTIC Science & Technology

2012-04-01

CONTRACTING ORGANIZATION: Bronx Veterans Medical Research Foundation, Inc... Bronx , NY 10468-3904 REPORT DATE: April 2012 TYPE OF REPORT: Final PREPARED FOR: U.S. Army...WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER Bronx Veterans Medical Research

Extending Antecedents of Achievement Goals: The Double-Edged Sword Effect of Social-Oriented Achievement Motive and Gender Differences

ERIC Educational Resources Information Center

Nie, Youyan; Liem, Gregory Arief D.

2013-01-01

Underpinned by the hierarchical model of approach and avoidance motivation, the study examined the differential relations of individual-oriented and social-oriented achievement motives to approach and avoidance achievement goals (mastery-approach, performance-approach, mastery-avoidance, performance-avoidance). A total of 570 Chinese high school…

Individual Differences in Human Reliability Analysis

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

Jeffrey C. Joe; Ronald L. Boring

2014-06-01

While human reliability analysis (HRA) methods include uncertainty in quantification, the nominal model of human error in HRA typically assumes that operator performance does not vary significantly when they are given the same initiating event, indicators, procedures, and training, and that any differences in operator performance are simply aleatory (i.e., random). While this assumption generally holds true when performing routine actions, variability in operator response has been observed in multiple studies, especially in complex situations that go beyond training and procedures. As such, complexity can lead to differences in operator performance (e.g., operator understanding and decision-making). Furthermore, psychological research hasmore » shown that there are a number of known antecedents (i.e., attributable causes) that consistently contribute to observable and systematically measurable (i.e., not random) differences in behavior. This paper reviews examples of individual differences taken from operational experience and the psychological literature. The impact of these differences in human behavior and their implications for HRA are then discussed. We propose that individual differences should not be treated as aleatory, but rather as epistemic. Ultimately, by understanding the sources of individual differences, it is possible to remove some epistemic uncertainty from analyses.« less

Multiple measures of dispositional global/local bias predict attentional blink magnitude.

PubMed

Dale, Gillian; Arnell, Karen M

2015-07-01

When the second of two targets (T2) is presented temporally close to the first target (T1) in a rapid serial visual presentation stream, accuracy to identify T2 is markedly reduced-an attentional blink (AB). While most individuals show an AB, Dale and Arnell (Atten Percept Psychophys 72(3):602-606, 2010) demonstrated that individual differences in dispositional attentional focus predicted AB performance, such that individuals who showed a natural bias toward the global level of Navon letter stimuli were less susceptible to the AB and showed a smaller AB effect. For the current study, we extended the findings of Dale and Arnell (Atten Percept Psychophys 72(3):602-606, 2010) through two experiments. In Experiment 1, we examined the relationship between dispositional global/local bias and the AB using a highly reliable hierarchical shape task measure. In Experiment 2, we examined whether three distinct global/local measures could predict AB performance. In both experiments, performance on the global/local tasks predicted subsequent AB performance, such that individuals with a greater preference for the global information showed a reduced AB. This supports previous findings, as well as recent models which discuss the role of attentional breadth in selective attention.

Identifying individual changes in performance with composite quality indicators while accounting for regression to the mean.

PubMed

Gajewski, Byron J; Dunton, Nancy

2013-04-01

Almost a decade ago Morton and Torgerson indicated that perceived medical benefits could be due to "regression to the mean." Despite this caution, the regression to the mean "effects on the identification of changes in institutional performance do not seem to have been considered previously in any depth" (Jones and Spiegelhalter). As a response, Jones and Spiegelhalter provide a methodology to adjust for regression to the mean when modeling recent changes in institutional performance for one-variable quality indicators. Therefore, in our view, Jones and Spiegelhalter provide a breakthrough methodology for performance measures. At the same time, in the interests of parsimony, it is useful to aggregate individual quality indicators into a composite score. Our question is, can we develop and demonstrate a methodology that extends the "regression to the mean" literature to allow for composite quality indicators? Using a latent variable modeling approach, we extend the methodology to the composite indicator case. We demonstrate the approach on 4 indicators collected by the National Database of Nursing Quality Indicators. A simulation study further demonstrates its "proof of concept."

Evaluation of annual, global seismicity forecasts, including ensemble models

NASA Astrophysics Data System (ADS)

Taroni, Matteo; Zechar, Jeremy; Marzocchi, Warner

2013-04-01

In 2009, the Collaboratory for the Study of the Earthquake Predictability (CSEP) initiated a prototype global earthquake forecast experiment. Three models participated in this experiment for 2009, 2010 and 2011—each model forecast the number of earthquakes above magnitude 6 in 1x1 degree cells that span the globe. Here we use likelihood-based metrics to evaluate the consistency of the forecasts with the observed seismicity. We compare model performance with statistical tests and a new method based on the peer-to-peer gambling score. The results of the comparisons are used to build ensemble models that are a weighted combination of the individual models. Notably, in these experiments the ensemble model always performs significantly better than the single best-performing model. Our results indicate the following: i) time-varying forecasts, if not updated after each major shock, may not provide significant advantages with respect to time-invariant models in 1-year forecast experiments; ii) the spatial distribution seems to be the most important feature to characterize the different forecasting performances of the models; iii) the interpretation of consistency tests may be misleading because some good models may be rejected while trivial models may pass consistency tests; iv) a proper ensemble modeling seems to be a valuable procedure to get the best performing model for practical purposes.

"Put Myself Into Your Place": Embodied Simulation and Perspective Taking in Autism Spectrum Disorders.

PubMed

Conson, Massimiliano; Mazzarella, Elisabetta; Esposito, Dalila; Grossi, Dario; Marino, Nicoletta; Massagli, Angelo; Frolli, Alessandro

2015-08-01

Embodied cognition theories hold that cognitive processes are grounded in bodily states. Embodied processes in autism spectrum disorders (ASD) have classically been investigated in studies on imitation. Several observations suggested that unlike typical individuals who are able of copying the model's actions from the model's position, individuals with ASD tend to reenact the model's actions from their own egocentric perspective. Here, we performed two behavioral experiments to directly test the ability of ASD individuals to adopt another person's point of view. In Experiment 1, participants had to explicitly judge the left/right location of a target object in a scene from their own or the actor's point of view (visual perspective taking task). In Experiment 2, participants had to perform left/right judgments on front-facing or back-facing human body images (own body transformation task). Both tasks can be solved by mentally simulating one's own body motion to imagine oneself transforming into the position of another person (embodied simulation strategy), or by resorting to visual/spatial processes, such as mental object rotation (nonembodied strategy). Results of both experiments showed that individual with ASD solved the tasks mainly relying on a nonembodied strategy, whereas typical controls adopted an embodied strategy. Moreover, in the visual perspective taking task ASD participants had more difficulties than controls in inhibiting other-perspective when directed to keep one's own point of view. These findings suggested that, in social cognitive tasks, individuals with ASD do not resort to embodied simulation and have difficulties in cognitive control over self- and other-perspective. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

R-warfarin clearances from plasma associated with polymorphic cytochrome P450 2C19 and simulated by individual physiologically based pharmacokinetic models for 11 cynomolgus monkeys.

PubMed

Utoh, Masahiro; Kusama, Takashi; Miura, Tomonori; Mitsui, Marina; Kawano, Mirai; Hirano, Takahiro; Shimizu, Makiko; Uno, Yasuhiro; Yamazaki, Hiroshi

2018-02-01

1. Cynomolgus monkey cytochrome P450 2C19 (formerly known as P450 2C75), homologous to human P450 2C19, has been identified as R-warfarin 7-hydroxylase. In this study, simulations of R-warfarin clearance in individual cynomolgus monkeys genotyped for P450 2C19 p.[(Phe100Asn; Ala103Val; Ile112Leu)] were performed using individual simplified physiologically based pharmacokinetic (PBPK) modeling. 2. Pharmacokinetic parameters and absorption rate constants, volumes of the systemic circulation, and hepatic intrinsic clearances for individual PBPK models were estimated for eleven cynomolgus monkeys. 3. One-way ANOVA revealed significant effects of the genotype (p < 0.01) on the observed elimination half-lives and areas under the curves of R-warfarin among the homozygous mutant, heterozygous mutant, and wild-type groups. R-Warfarin clearances in individual cynomolgus monkeys genotyped for P450 2C19 were simulated by simplified PBPK modeling. The modeled hepatic intrinsic clearances were significantly associated with the P450 2C19 genotypes. The liver microsomal elimination rates of R-warfarin for individual animals after in vivo administration showed significant reductions associated with the genotype (p < 0.01). 4. This study provides important information to help simulate clearances of R-warfarin and related medicines associated with polymorphic P450 2C19 in individual cynomolgus monkeys, thereby facilitating calculation of the fraction of hepatic clearance.

Student perceptions of secondary science: A performance technology application

NASA Astrophysics Data System (ADS)

Small, Belinda Rusnak

The primary purpose of this study was to identify influences blocking or promoting science performance from the lived K-12 classroom experience. Human Performance Technology protocols were used to understand factors promoting or hindering science performance. The goal was to gain information from the individual students' perspective to enhance opportunities for stakeholders to improve the current state of performance in science education. Individual perspectives of 10 secondary science students were examined using grounded theory protocols. Findings include students' science learning behaviors are influenced by two major themes, environmental supports and individual learning behaviors. The three environmental support factors identified include the methods students receive instruction, students' opportunities to access informal help apart from formal instruction, and students' feelings of teacher likability. Additionally, findings include three major factors causing individual learners to generate knowledge in science. Factors reported include personalizing information to transform data into knowledge, customizing learning opportunities to maximize peak performance, and tapping motivational opportunities to persevere through complex concepts. The emergent theory postulated is that if a performance problem exists in an educational setting, then integrating student perspectives into the cause analysis opens opportunity to align interventions for influencing student performance outcomes. An adapted version of Gilbert's Behavioral Engineering Model is presented as an organizational tool to display the findings. The boundaries of this Performance Technology application do not extend to the identification, selection, design, or implementation of solutions to improved science performance. However, as stakeholders begin to understand learner perspectives then aligned decisions may be created to support learners of science in a direct, cost effective manner.

Numerical simulation of the distribution of individual gas bubbles in shaped sapphire crystals

NASA Astrophysics Data System (ADS)

Borodin, A. V.; Borodin, V. A.

2017-11-01

The simulation of the effective density of individual gas bubbles in a two-phase melt, consisting of a liquid and gas bubbles, is performed using the virtual model of the thermal unit. Based on the studies, for the first time the theoretically and experimentally grounded mechanism of individual gas bubbles formation in shaped sapphire is proposed. It is shown that the change of the melt flow pattern in crucible affects greatly the bubble density at the crystallization front, and in the crystal. The obtained results allowed reducing the number of individual gas bubbles in sapphire sheets.

Next day discharge rate has little use as a quality measure for individual physician performance.

PubMed

Inabnit, Christopher; Markwell, Stephen; Gruwell, Jack; Jaeger, Cassie; Millburg, Lance; Griffen, David

2018-06-18

Emergency Department (ED) physicians' next day discharge rate (NDDR), the percentage of patients who were admitted from the ED and subsequently discharged within the next calendar day was hypothesized as a potential measure for unnecessary admissions. The objective was to determine if NDDR has validity as a measure for quality of individual ED physician performance. Hospital admission data was obtained for thirty-six ED physicians for calendar year 2015. Funnel plots were used to identify NDDR outliers beyond 95% control limits. A mixed model logistic regression was built to investigate factors contributing to NDDR. To determine yearly variation, data from calendar years 2014 and 2016 were analyzed, again by funnel plots and logistic regression. Intraclass correlation coefficient was used to estimate the percent of total variation in NDDR attributable to individual ED physicians. NDDR varied significantly among ED physicians. Individual ED physician outliers in NDDR varied year to year. Individual ED physician contribution to NDDR variation was minimal, accounting for 1%. Years of experience in Emergency Medicine practice was not correlated with NDDR. NDDR does not appear to be a reliable independent quality measure for individual ED physician performance. The percent of variance attributable to the ED physician was 1%. Copyright © 2018. Published by Elsevier Inc.

A proposed case-control framework to probabilistically classify individual deaths as expected or excess during extreme hot weather events.

PubMed

Henderson, Sarah B; Gauld, Jillian S; Rauch, Stephen A; McLean, Kathleen E; Krstic, Nikolas; Hondula, David M; Kosatsky, Tom

2016-11-15

Most excess deaths that occur during extreme hot weather events do not have natural heat recorded as an underlying or contributing cause. This study aims to identify the specific individuals who died because of hot weather using only secondary data. A novel approach was developed in which the expected number of deaths was repeatedly sampled from all deaths that occurred during a hot weather event, and compared with deaths during a control period. The deaths were compared with respect to five factors known to be associated with hot weather mortality. Individuals were ranked by their presence in significant models over 100 trials of 10,000 repetitions. Those with the highest rankings were identified as probable excess deaths. Sensitivity analyses were performed on a range of model combinations. These methods were applied to a 2009 hot weather event in greater Vancouver, Canada. The excess deaths identified were sensitive to differences in model combinations, particularly between univariate and multivariate approaches. One multivariate and one univariate combination were chosen as the best models for further analyses. The individuals identified by multiple combinations suggest that marginalized populations in greater Vancouver are at higher risk of death during hot weather. This study proposes novel methods for classifying specific deaths as expected or excess during a hot weather event. Further work is needed to evaluate performance of the methods in simulation studies and against clinically identified cases. If confirmed, these methods could be applied to a wide range of populations and events of interest.

Tracking reading: dual task costs of oral reading for young versus older adults.

PubMed

Kemper, Susan; Bontempo, Daniel; Schmalzried, RaLynn; McKedy, Whitney; Tagliaferri, Bruno; Kieweg, Doug

2014-02-01

A digital pursuit rotor was used to monitor oral reading costs by time-locking tracking performance to the auditory wave form produced as young and older adults were reading out short paragraphs. Multilevel modeling was used to determine how paragraph-level predictors of length, grammatical complexity, and readability and person-level predictors such as speaker age or working memory capacity predicted reading and tracking performance. In addition, sentence-by-sentence variation in tracking performance was examined during the production of individual sentences and during the pauses before upcoming sentences. The results suggest that dual tasking has a greater impact on older adults' reading comprehension and tracking performance. At the level of individual sentences, young and older adults adopt different strategies to deal with grammatically complex and propositionally dense sentences.

Dual Task Costs of Oral Reading for Young versus Older Adults

PubMed Central

Kemper, Susan; Bontempo, Daniel; Schmalzried, RaLynn; McKedy, Whitney; Tagliaferri, Bruno; Kieweg, Doug

2013-01-01

A digital pursuit rotor was used to monitor oral reading costs by time-locking tracking performance to the auditory wave form produced as young and older adults were reading out short paragraphs. Multilevel modeling was used to determine how paragraph-level predictors of length, grammatical complexity, and readability and person-level predictors such as speaker age or working memory capacity predicted reading and tracking performance. In addition, sentence-by-sentence variation in tracking performance was examined during the production of individual sentences and during the pauses before upcoming sentences. The results suggest that dual tasking has a greater impact on older adults’ reading comprehension and tracking performance. At the level of individual sentences, young and older adults adopt different strategies to deal with grammatically complex and propositionally dense sentences. PMID:23463405

Individualized prediction of lung-function decline in chronic obstructive pulmonary disease

PubMed Central

Zafari, Zafar; Sin, Don D.; Postma, Dirkje S.; Löfdahl, Claes-Göran; Vonk, Judith; Bryan, Stirling; Lam, Stephen; Tammemagi, C. Martin; Khakban, Rahman; Man, S.F. Paul; Tashkin, Donald; Wise, Robert A.; Connett, John E.; McManus, Bruce; Ng, Raymond; Hollander, Zsuszanna; Sadatsafavi, Mohsen

2016-01-01

Background: The rate of lung-function decline in chronic obstructive pulmonary disease (COPD) varies substantially among individuals. We sought to develop and validate an individualized prediction model for forced expiratory volume at 1 second (FEV1) in current smokers with mild-to-moderate COPD. Methods: Using data from a large long-term clinical trial (the Lung Health Study), we derived mixed-effects regression models to predict future FEV1 values over 11 years according to clinical traits. We modelled heterogeneity by allowing regression coefficients to vary across individuals. Two independent cohorts with COPD were used for validating the equations. Results: We used data from 5594 patients (mean age 48.4 yr, 63% men, mean baseline FEV1 2.75 L) to create the individualized prediction equations. There was significant between-individual variability in the rate of FEV1 decline, with the interval for the annual rate of decline that contained 95% of individuals being −124 to −15 mL/yr for smokers and −83 to 15 mL/yr for sustained quitters. Clinical variables in the final model explained 88% of variation around follow-up FEV1. The C statistic for predicting severity grades was 0.90. Prediction equations performed robustly in the 2 external data sets. Interpretation: A substantial part of individual variation in FEV1 decline can be explained by easily measured clinical variables. The model developed in this work can be used for prediction of future lung health in patients with mild-to-moderate COPD. Trial registration: Lung Health Study — ClinicalTrials.gov, no. NCT00000568; Pan-Canadian Early Detection of Lung Cancer Study — ClinicalTrials.gov, no. NCT00751660 PMID:27486205

Methods for network meta-analysis of continuous outcomes using individual patient data: a case study in acupuncture for chronic pain.

PubMed

Saramago, Pedro; Woods, Beth; Weatherly, Helen; Manca, Andrea; Sculpher, Mark; Khan, Kamran; Vickers, Andrew J; MacPherson, Hugh

2016-10-06

Network meta-analysis methods, which are an extension of the standard pair-wise synthesis framework, allow for the simultaneous comparison of multiple interventions and consideration of the entire body of evidence in a single statistical model. There are well-established advantages to using individual patient data to perform network meta-analysis and methods for network meta-analysis of individual patient data have already been developed for dichotomous and time-to-event data. This paper describes appropriate methods for the network meta-analysis of individual patient data on continuous outcomes. This paper introduces and describes network meta-analysis of individual patient data models for continuous outcomes using the analysis of covariance framework. Comparisons are made between this approach and change score and final score only approaches, which are frequently used and have been proposed in the methodological literature. A motivating example on the effectiveness of acupuncture for chronic pain is used to demonstrate the methods. Individual patient data on 28 randomised controlled trials were synthesised. Consistency of endpoints across the evidence base was obtained through standardisation and mapping exercises. Individual patient data availability avoided the use of non-baseline-adjusted models, allowing instead for analysis of covariance models to be applied and thus improving the precision of treatment effect estimates while adjusting for baseline imbalance. The network meta-analysis of individual patient data using the analysis of covariance approach is advocated to be the most appropriate modelling approach for network meta-analysis of continuous outcomes, particularly in the presence of baseline imbalance. Further methods developments are required to address the challenge of analysing aggregate level data in the presence of baseline imbalance.

The Impact of Pictorial Display on Operator Learning and Performance. M.S. Thesis

NASA Technical Reports Server (NTRS)

Miller, R. A.; Messing, L. J.; Jagacinski, R. J.

1984-01-01

The effects of pictorially displayed information on human learning and performance of a simple control task were investigated. The controlled system was a harmonic oscillator and the system response was displayed to subjects as either an animated pendulum or a horizontally moving dot. Results indicated that the pendulum display did not effect performance scores but did significantly effect the learning processes of individual operators. The subjects with the pendulum display demonstrated more vertical internal models early in the experiment and the manner in which their internal models were tuned with practice showed increased variability between subjects.

Parametric System Model for a Stirling Radioisotope Generator

NASA Technical Reports Server (NTRS)

Schmitz, Paul C.

2015-01-01

A Parametric System Model (PSM) was created in order to explore conceptual designs, the impact of component changes and power level on the performance of the Stirling Radioisotope Generator (SRG). Using the General Purpose Heat Source (GPHS approximately 250 Wth) modules as the thermal building block from which a SRG is conceptualized, trade studies are performed to understand the importance of individual component scaling on isotope usage. Mathematical relationships based on heat and power throughput, temperature, mass, and volume were developed for each of the required subsystems. The PSM uses these relationships to perform component- and system-level trades.

Parametric System Model for a Stirling Radioisotope Generator

NASA Technical Reports Server (NTRS)

Schmitz, Paul C.

2014-01-01

A Parametric System Model (PSM) was created in order to explore conceptual designs, the impact of component changes and power level on the performance of Stirling Radioisotope Generator (SRG). Using the General Purpose Heat Source (GPHS approximately 250 watt thermal) modules as the thermal building block around which a SRG is conceptualized, trade studies are performed to understand the importance of individual component scaling on isotope usage. Mathematical relationships based on heat and power throughput, temperature, mass and volume were developed for each of the required subsystems. The PSM uses these relationships to perform component and system level trades.

Estimating effectiveness in HIV prevention trials with a Bayesian hierarchical compound Poisson frailty model

PubMed Central

Coley, Rebecca Yates; Browna, Elizabeth R.

2016-01-01

Inconsistent results in recent HIV prevention trials of pre-exposure prophylactic interventions may be due to heterogeneity in risk among study participants. Intervention effectiveness is most commonly estimated with the Cox model, which compares event times between populations. When heterogeneity is present, this population-level measure underestimates intervention effectiveness for individuals who are at risk. We propose a likelihood-based Bayesian hierarchical model that estimates the individual-level effectiveness of candidate interventions by accounting for heterogeneity in risk with a compound Poisson-distributed frailty term. This model reflects the mechanisms of HIV risk and allows that some participants are not exposed to HIV and, therefore, have no risk of seroconversion during the study. We assess model performance via simulation and apply the model to data from an HIV prevention trial. PMID:26869051

International challenge to model the long-range transport of radioxenon released from medical isotope production to six Comprehensive Nuclear-Test-Ban Treaty monitoring stations

DOE PAGES

Maurer, Christian; Baré, Jonathan; Kusmierczyk-Michulec, Jolanta; ...

2018-03-08

After performing a first multi-model exercise in 2015 a comprehensive and technically more demanding atmospheric transport modelling challenge was organized in 2016. Release data were provided by the Australian Nuclear Science and Technology Organization radiopharmaceutical facility in Sydney (Australia) for a one month period. Measured samples for the same time frame were gathered from six International Monitoring System stations in the Southern Hemisphere with distances to the source ranging between 680 (Melbourne) and about 17,000 km (Tristan da Cunha). Participants were prompted to work with unit emissions in pre-defined emission intervals (daily, half-daily, 3-hourly and hourly emission segment lengths) andmore » in order to perform a blind test actual emission values were not provided to them. Despite the quite different settings of the two atmospheric transport modelling challenges there is common evidence that for long-range atmospheric transport using temporally highly resolved emissions and highly space-resolved meteorological input fields has no significant advantage compared to using lower resolved ones. As well an uncertainty of up to 20% in the daily stack emission data turns out to be acceptable for the purpose of a study like this. Model performance at individual stations is quite diverse depending largely on successfully capturing boundary layer processes. No single model-meteorology combination performs best for all stations. Moreover, the stations statistics do not depend on the distance between the source and the individual stations. Finally, it became more evident how future exercises need to be designed. Set-up parameters like the meteorological driver or the output grid resolution should be pre-scribed in order to enhance diversity as well as comparability among model runs.« less

Ant colony optimization algorithm for interpretable Bayesian classifiers combination: application to medical predictions.

PubMed

Bouktif, Salah; Hanna, Eileen Marie; Zaki, Nazar; Abu Khousa, Eman

2014-01-01

Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models' interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions. The datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm.

International challenge to model the long-range transport of radioxenon released from medical isotope production to six Comprehensive Nuclear-Test-Ban Treaty monitoring stations

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

Maurer, Christian; Baré, Jonathan; Kusmierczyk-Michulec, Jolanta

After performing a first multi-model exercise in 2015 a comprehensive and technically more demanding atmospheric transport modelling challenge was organized in 2016. Release data were provided by the Australian Nuclear Science and Technology Organization radiopharmaceutical facility in Sydney (Australia) for a one month period. Measured samples for the same time frame were gathered from six International Monitoring System stations in the Southern Hemisphere with distances to the source ranging between 680 (Melbourne) and about 17,000 km (Tristan da Cunha). Participants were prompted to work with unit emissions in pre-defined emission intervals (daily, half-daily, 3-hourly and hourly emission segment lengths) andmore » in order to perform a blind test actual emission values were not provided to them. Despite the quite different settings of the two atmospheric transport modelling challenges there is common evidence that for long-range atmospheric transport using temporally highly resolved emissions and highly space-resolved meteorological input fields has no significant advantage compared to using lower resolved ones. As well an uncertainty of up to 20% in the daily stack emission data turns out to be acceptable for the purpose of a study like this. Model performance at individual stations is quite diverse depending largely on successfully capturing boundary layer processes. No single model-meteorology combination performs best for all stations. Moreover, the stations statistics do not depend on the distance between the source and the individual stations. Finally, it became more evident how future exercises need to be designed. Set-up parameters like the meteorological driver or the output grid resolution should be pre-scribed in order to enhance diversity as well as comparability among model runs.« less

Attachment anxiety benefits from security priming: Evidence from working memory performance

PubMed Central

2018-01-01

The present study investigates the relationship between the attachment dimensions (anxious vs. avoidance) and the cognitive performance of individuals, specifically whether the attachment dimensions would predict the working memory (WM) performance. In the n-back task, reflecting the WM capacity, both attachment related and non-attachment related words were used. Participants were randomly assigned into two groups that received either the secure or the neutral subliminal priming. In the secure priming condition, the aim was to induce sense of security by presenting secure attachment words prior to the n-back task performance. In neutral priming condition, neutral words that did not elicit sense of security were presented. Structural equation modeling revealed divergent patterns for attachment anxiety and avoidance dimensions under the different priming conditions. In neutral priming condition, WM performance declined in terms of capacity in the n-back task for individuals who rated higher levels of attachment anxiety. However in the secure priming condition, WM performance was boosted in the n-back task for individuals who rated higher levels of attachment anxiety. In other words, the subliminal priming of the security led to increased WM capacity of individuals who rated higher levels of attachment anxiety. This effect, however, was not observed for higher levels of attachment avoidance. Results are discussed along the lines of hyperactivation and deactivation strategies of the attachment system. PMID:29522549

Diversity Performance Analysis on Multiple HAP Networks.

PubMed

Dong, Feihong; Li, Min; Gong, Xiangwu; Li, Hongjun; Gao, Fengyue

2015-06-30

One of the main design challenges in wireless sensor networks (WSNs) is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP) is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO) techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO) model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV). In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) are derived. In addition, the average symbol error rate (ASER) with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI) and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques.

Too Exhausted to Perform at the Highest Level? On the Importance of Self-control Strength in Educational Settings

PubMed Central

Englert, Chris; Zavery, Alafia; Bertrams, Alex

2017-01-01

In order to perform at the highest level in educational settings (e.g., students in testing situations), individuals often have to control their impulses or desires (e.g., to study for an upcoming test or to prepare a course instead of spending time with the peer group). Previous research suggests that the ability to exert self-control is an important predictor of performance and behavior in educational contexts. According to the strength model, all self-control acts are based on one global energy pool whose capacity is assumed to be limited. After having performed a first act of self-control, this resource can become temporarily depleted which negatively affects subsequent self-control. In such a state of ego depletion, individuals tend to display impaired concentration and academic performance, fail to meet academic deadlines, or even disengage from their duties. In this mini-review, we report recent studies on ego depletion which have focused on children as well as adults in educational settings, derive practical implications for how to improve self-control strength in the realm of education and instruction, and discuss limitations regarding the assumptions of the strength model of self-control. PMID:28790963

Too Exhausted to Perform at the Highest Level? On the Importance of Self-control Strength in Educational Settings.

PubMed

Englert, Chris; Zavery, Alafia; Bertrams, Alex

2017-01-01

In order to perform at the highest level in educational settings (e.g., students in testing situations), individuals often have to control their impulses or desires (e.g., to study for an upcoming test or to prepare a course instead of spending time with the peer group). Previous research suggests that the ability to exert self-control is an important predictor of performance and behavior in educational contexts. According to the strength model, all self-control acts are based on one global energy pool whose capacity is assumed to be limited. After having performed a first act of self-control, this resource can become temporarily depleted which negatively affects subsequent self-control. In such a state of ego depletion, individuals tend to display impaired concentration and academic performance, fail to meet academic deadlines, or even disengage from their duties. In this mini-review, we report recent studies on ego depletion which have focused on children as well as adults in educational settings, derive practical implications for how to improve self-control strength in the realm of education and instruction, and discuss limitations regarding the assumptions of the strength model of self-control.

Individual cell lag time distributions of Cronobacter (Enterobacter sakazakii) and impact of pooling samples on its detection in powdered infant formula.

PubMed

Miled, Rabeb Bennour; Guillier, Laurent; Neves, Sandra; Augustin, Jean-Christophe; Colin, Pierre; Besse, Nathalie Gnanou

2011-06-01

Cells of six strains of Cronobacter were subjected to dry stress and stored for 2.5 months at ambient temperature. The individual cell lag time distributions of recovered cells were characterized at 25 °C and 37 °C in non-selective broth. The individual cell lag times were deduced from the times taken by cultures from individual cells to reach an optical density threshold. In parallel, growth curves for each strain at high contamination levels were determined in the same growth conditions. In general, the extreme value type II distribution with a shape parameter fixed to 5 (EVIIb) was the most effective at describing the 12 observed distributions of individual cell lag times. Recently, a model for characterizing individual cell lag time distribution from population growth parameters was developed for other food-borne pathogenic bacteria such as Listeria monocytogenes. We confirmed this model's applicability to Cronobacter by comparing the mean and the standard deviation of individual cell lag times to populational lag times observed with high initial concentration experiments. We also validated the model in realistic conditions by studying growth in powdered infant formula decimally diluted in Buffered Peptone Water, which represents the first enrichment step of the standard detection method for Cronobacter. Individual lag times and the pooling of samples significantly affect detection performances. Copyright © 2010 Elsevier Ltd. All rights reserved.

Automation in organizations: Eternal conflict

NASA Technical Reports Server (NTRS)

Dieterly, D. L.

1981-01-01

Some ideas on and insights into the problems associated with automation in organizations are presented with emphasis on the concept of automation, its relationship to the individual, and its impact on system performance. An analogy is drawn, based on an American folk hero, to emphasize the extent of the problems encountered when dealing with automation within an organization. A model is proposed to focus attention on a set of appropriate dimensions. The function allocation process becomes a prominent aspect of the model. The current state of automation research is mentioned in relation to the ideas introduced. Proposed directions for an improved understanding of automation's effect on the individual's efficiency are discussed. The importance of understanding the individual's perception of the system in terms of the degree of automation is highlighted.

Nonlinear model of epidemic spreading in a complex social network.

PubMed

Kosiński, Robert A; Grabowski, A

2007-10-01

The epidemic spreading in a human society is a complex process, which can be described on the basis of a nonlinear mathematical model. In such an approach the complex and hierarchical structure of social network (which has implications for the spreading of pathogens and can be treated as a complex network), can be taken into account. In our model each individual has one of the four permitted states: susceptible, infected, infective, unsusceptible or dead. This refers to the SEIR model used in epidemiology. The state of an individual changes in time, depending on the previous state and the interactions with other individuals. The description of the interpersonal contacts is based on the experimental observations of the social relations in the community. It includes spatial localization of the individuals and hierarchical structure of interpersonal interactions. Numerical simulations were performed for different types of epidemics, giving the progress of a spreading process and typical relationships (e.g. range of epidemic in time, the epidemic curve). The spreading process has a complex and spatially chaotic character. The time dependence of the number of infective individuals shows the nonlinear character of the spreading process. We investigate the influence of the preventive vaccinations on the spreading process. In particular, for a critical value of preventively vaccinated individuals the percolation threshold is observed and the epidemic is suppressed.

Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

NASA Technical Reports Server (NTRS)

Hun, Eunjin; Crow, Wade T.; Holmes, Thomas; Bolten, John

2014-01-01

Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model and a sequential data assimilation filter) against a series of linear models which perform the same function (i.e., have the same basic inputoutput structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross-correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moistureNDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which non-linearities andor complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model and an Ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.

A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation

PubMed Central

Chao, Lidia S.; Lu, Yi; Xing, Junwen

2014-01-01

Data selection has shown significant improvements in effective use of training data by extracting sentences from large general-domain corpora to adapt statistical machine translation (SMT) systems to in-domain data. This paper performs an in-depth analysis of three different sentence selection techniques. The first one is cosine tf-idf, which comes from the realm of information retrieval (IR). The second is perplexity-based approach, which can be found in the field of language modeling. These two data selection techniques applied to SMT have been already presented in the literature. However, edit distance for this task is proposed in this paper for the first time. After investigating the individual model, a combination of all three techniques is proposed at both corpus level and model level. Comparative experiments are conducted on Hong Kong law Chinese-English corpus and the results indicate the following: (i) the constraint degree of similarity measuring is not monotonically related to domain-specific translation quality; (ii) the individual selection models fail to perform effectively and robustly; but (iii) bilingual resources and combination methods are helpful to balance out-of-vocabulary (OOV) and irrelevant data; (iv) finally, our method achieves the goal to consistently boost the overall translation performance that can ensure optimal quality of a real-life SMT system. PMID:24683356

Detecting a Change in School Performance: A Bayesian Analysis for a Multilevel Join Point Problem. CSE Technical Report 542.

ERIC Educational Resources Information Center

Thum, Yeow Meng; Bhattacharya, Suman Kumar

To better describe individual behavior within a system, this paper uses a sample of longitudinal test scores from a large urban school system to consider hierarchical Bayes estimation of a multilevel linear regression model in which each individual regression slope of test score on time switches at some unknown point in time, "kj."…

The Use of Individual Growth and Developmental Indicators for Progress Monitoring and Intervention Decision Making in Early Education

ERIC Educational Resources Information Center

Walker, Dale; Carta, Judith J.; Greenwood, Charles R.; Buzhardt, Joseph F.

2008-01-01

Progress monitoring tools have been shown to be essential elements in current approaches to intervention problem-solving models. Such tools have been valuable not only in marking individual children's level of performance relative to peers but also in measuring change in skill level in a way that can be attributed to intervention and development.…

Key properties of expert movement systems in sport : an ecological dynamics perspective.

PubMed

Seifert, Ludovic; Button, Chris; Davids, Keith

2013-03-01

This paper identifies key properties of expertise in sport predicated on the performer-environment relationship. Weaknesses of traditional approaches to expert performance, which uniquely focus on the performer and the environment separately, are highlighted by an ecological dynamics perspective. Key properties of expert movement systems include 'multi- and meta-stability', 'adaptive variability', 'redundancy', 'degeneracy' and the 'attunement to affordances'. Empirical research on these expert system properties indicates that skill acquisition does not emerge from the internal representation of declarative and procedural knowledge, or the imitation of expert behaviours to linearly reduce a perceived 'gap' separating movements of beginners and a putative expert model. Rather, expert performance corresponds with the ongoing co-adaptation of an individual's behaviours to dynamically changing, interacting constraints, individually perceived and encountered. The functional role of adaptive movement variability is essential to expert performance in many different sports (involving individuals and teams; ball games and outdoor activities; land and aquatic environments). These key properties signify that, in sport performance, although basic movement patterns need to be acquired by developing athletes, there exists no ideal movement template towards which all learners should aspire, since relatively unique functional movement solutions emerge from the interaction of key constraints.

Measuring the usefulness of hidden units in Boltzmann machines with mutual information.

PubMed

Berglund, Mathias; Raiko, Tapani; Cho, Kyunghyun

2015-04-01

Restricted Boltzmann machines (RBMs) and deep Boltzmann machines (DBMs) are important models in deep learning, but it is often difficult to measure their performance in general, or measure the importance of individual hidden units in specific. We propose to use mutual information to measure the usefulness of individual hidden units in Boltzmann machines. The measure is fast to compute, and serves as an upper bound for the information the neuron can pass on, enabling detection of a particular kind of poor training results. We confirm experimentally that the proposed measure indicates how much the performance of the model drops when some of the units of an RBM are pruned away. We demonstrate the usefulness of the measure for early detection of poor training in DBMs. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Framework for Cognitive Interventions Targeting Everyday Memory Performance and Memory Self-efficacy

PubMed Central

McDougall, Graham J.

2009-01-01

The human brain has the potential for self-renewal through adult neurogenesis, which is the birth of new neurons. Neural plasticity implies that the nervous system can change and grow. This understanding has created new possibilities for cognitive enhancement and rehabilitation. However, as individuals age, they have decreased confidence, or memory self-efficacy, which is directly related to their everyday memory performance. In this article, a developmental account of studies about memory self-efficacy and nonpharmacologic cognitive intervention models is presented and a cognitive intervention model, called the cognitive behavioral model of everyday memory, is proposed. PMID:19065089

A model for the transfer of perceptual-motor skill learning in human behaviors.

PubMed

Rosalie, Simon M; Müller, Sean

2012-09-01

This paper presents a preliminary model that outlines the mechanisms underlying the transfer of perceptual-motor skill learning in sport and everyday tasks. Perceptual-motor behavior is motivated by performance demands and evolves over time to increase the probability of success through adaptation. Performance demands at the time of an event create a unique transfer domain that specifies a range of potentially successful actions. Transfer comprises anticipatory subconscious and conscious mechanisms. The model also outlines how transfer occurs across a continuum, which depends on the individual's expertise and contextual variables occurring at the incidence of transfer

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

NASA Technical Reports Server (NTRS)

Gorinevsky, Dimitry; Matthews, Bryan L.; Martin, Rodney

2012-01-01

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Individual Differences in Response to Automation: The Five Factor Model of Personality

ERIC Educational Resources Information Center

Szalma, James L.; Taylor, Grant S.

2011-01-01

This study examined the relationship of operator personality (Five Factor Model) and characteristics of the task and of adaptive automation (reliability and adaptiveness--whether the automation was well-matched to changes in task demand) to operator performance, workload, stress, and coping. This represents the first investigation of how the Five…

Evaluating Curriculum-Based Measurement from a Behavioral Assessment Perspective

ERIC Educational Resources Information Center

Ardoin, Scott P.; Roof, Claire M.; Klubnick, Cynthia; Carfolite, Jessica

2008-01-01

Curriculum-based measurement Reading (CBM-R) is an assessment procedure used to evaluate students' relative performance compared to peers and to evaluate their growth in reading. Within the response to intervention (RtI) model, CBM-R data are plotted in time series fashion as a means modeling individual students' response to varying levels of…

An Integrative Social-Cognitive Developmental Model of Supervision for Substance Abuse Counselors-in-Training

ERIC Educational Resources Information Center

Sias, Shari M.; Lambie, Glenn W.

2008-01-01

Substance abuse counselors (SACs) at higher levels of social-cognitive maturity manage complex situations and perform counselor-related tasks more effectively than individuals at lower levels of development. This article presents an integrative clinical supervision model designed to promote the social-cognitive maturity (ego development;…

Key algorithms used in GR02: A computer simulation model for predicting tree and stand growth

Treesearch

Garrett A. Hughes; Paul E. Sendak; Paul E. Sendak

1985-01-01

GR02 is an individual tree, distance-independent simulation model for predicting tree and stand growth over time. It performs five major functions during each run: (1) updates diameter at breast height, (2) updates total height, (3) estimates mortality, (4) determines regeneration, and (5) updates crown class.

Exploring an Ecological Model of Perceived Usability within a Multi-Tiered Vocabulary Intervention

ERIC Educational Resources Information Center

Neugebauer, Sabina R.; Chafouleas, Sandra M.; Coyne, Michael D.; McCoach, D. Betsy; Briesch, Amy M.

2016-01-01

The present study examines an ecological model for intervention use to explain student vocabulary performance in a multi-tiered intervention setting. A teacher self-report measure composed of factors hypothesized to influence intervention use at multiple levels (i.e., individual, intervention, and system level) was administered to 54 teachers and…

Modeling methodology for supply chain synthesis and disruption analysis

NASA Astrophysics Data System (ADS)

Wu, Teresa; Blackhurst, Jennifer

2004-11-01

The concept of an integrated or synthesized supply chain is a strategy for managing today's globalized and customer driven supply chains in order to better meet customer demands. Synthesizing individual entities into an integrated supply chain can be a challenging task due to a variety of factors including conflicting objectives, mismatched incentives and constraints of the individual entities. Furthermore, understanding the effects of disruptions occurring at any point in the system is difficult when working toward synthesizing supply chain operations. Therefore, the goal of this research is to present a modeling methodology to manage the synthesis of a supply chain by linking hierarchical levels of the system and to model and analyze disruptions in the integrated supply chain. The contribution of this research is threefold: (1) supply chain systems can be modeled hierarchically (2) the performance of synthesized supply chain system can be evaluated quantitatively (3) reachability analysis is used to evaluate the system performance and verify whether a specific state is reachable, allowing the user to understand the extent of effects of a disruption.

Chimpanzees demonstrate individual differences in social information use.

PubMed

Watson, Stuart K; Vale, Gillian L; Hopper, Lydia M; Dean, Lewis G; Kendal, Rachel L; Price, Elizabeth E; Wood, Lara A; Davis, Sarah J; Schapiro, Steven J; Lambeth, Susan P; Whiten, Andrew

2018-06-19

Studies of transmission biases in social learning have greatly informed our understanding of how behaviour patterns may diffuse through animal populations, yet within-species inter-individual variation in social information use has received little attention and remains poorly understood. We have addressed this question by examining individual performances across multiple experiments with the same population of primates. We compiled a dataset spanning 16 social learning studies (26 experimental conditions) carried out at the same study site over a 12-year period, incorporating a total of 167 chimpanzees. We applied a binary scoring system to code each participant's performance in each study according to whether they demonstrated evidence of using social information from conspecifics to solve the experimental task or not (Social Information Score-'SIS'). Bayesian binomial mixed effects models were then used to estimate the extent to which individual differences influenced SIS, together with any effects of sex, rearing history, age, prior involvement in research and task type on SIS. An estimate of repeatability found that approximately half of the variance in SIS was accounted for by individual identity, indicating that individual differences play a critical role in the social learning behaviour of chimpanzees. According to the model that best fit the data, females were, depending on their rearing history, 15-24% more likely to use social information to solve experimental tasks than males. However, there was no strong evidence of an effect of age or research experience, and pedigree records indicated that SIS was not a strongly heritable trait. Our study offers a novel, transferable method for the study of individual differences in social learning.

Strategies to intervene on causal systems are adaptively selected.

PubMed

Coenen, Anna; Rehder, Bob; Gureckis, Todd M

2015-06-01

How do people choose interventions to learn about causal systems? Here, we considered two possibilities. First, we test an information sampling model, information gain, which values interventions that can discriminate between a learner's hypotheses (i.e. possible causal structures). We compare this discriminatory model to a positive testing strategy that instead aims to confirm individual hypotheses. Experiment 1 shows that individual behavior is described best by a mixture of these two alternatives. In Experiment 2 we find that people are able to adaptively alter their behavior and adopt the discriminatory model more often after experiencing that the confirmatory strategy leads to a subjective performance decrement. In Experiment 3, time pressure leads to the opposite effect of inducing a change towards the simpler positive testing strategy. These findings suggest that there is no single strategy that describes how intervention decisions are made. Instead, people select strategies in an adaptive fashion that trades off their expected performance and cognitive effort. Copyright © 2015 Elsevier Inc. All rights reserved.

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

PubMed

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

2018-06-01

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

Development and application of a backscatter lidar forward operator for quantitative validation of aerosol dispersion models and future data assimilation

NASA Astrophysics Data System (ADS)

Geisinger, Armin; Behrendt, Andreas; Wulfmeyer, Volker; Strohbach, Jens; Förstner, Jochen; Potthast, Roland

2017-12-01

A new backscatter lidar forward operator was developed which is based on the distinct calculation of the aerosols' backscatter and extinction properties. The forward operator was adapted to the COSMO-ART ash dispersion simulation of the Eyjafjallajökull eruption in 2010. While the particle number concentration was provided as a model output variable, the scattering properties of each individual particle type were determined by dedicated scattering calculations. Sensitivity studies were performed to estimate the uncertainties related to the assumed particle properties. Scattering calculations for several types of non-spherical particles required the usage of T-matrix routines. Due to the distinct calculation of the backscatter and extinction properties of the models' volcanic ash size classes, the sensitivity studies could be made for each size class individually, which is not the case for forward models based on a fixed lidar ratio. Finally, the forward-modeled lidar profiles have been compared to automated ceilometer lidar (ACL) measurements both qualitatively and quantitatively while the attenuated backscatter coefficient was chosen as a suitable physical quantity. As the ACL measurements were not calibrated automatically, their calibration had to be performed using satellite lidar and ground-based Raman lidar measurements. A slight overestimation of the model-predicted volcanic ash number density was observed. Major requirements for future data assimilation of data from ACL have been identified, namely, the availability of calibrated lidar measurement data, a scattering database for atmospheric aerosols, a better representation and coverage of aerosols by the ash dispersion model, and more investigation in backscatter lidar forward operators which calculate the backscatter coefficient directly for each individual aerosol type. The introduced forward operator offers the flexibility to be adapted to a multitude of model systems and measurement setups.

Performance Optimization of Alternative Lower Global Warming Potential Refrigerants in Mini-Split Room Air Conditioners

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

Shen, Bo; Abdelaziz, Omar; Shrestha, Som S

Oak Ridge National laboratory (ORNL) recently conducted extensive laboratory, drop-in investigations for lower Global Warming Potential (GWP) refrigerants to replace R-22 and R-410A. ORNL studied propane, DR-3, ARM-20B, N-20B and R-444B as lower GWP refrigerant replacement for R-22 in a mini-split room air conditioner (RAC) originally designed for R-22; and, R-32, DR-55, ARM-71A, and L41-2, in a mini-split RAC designed for R-410A. We obtained laboratory testing results with very good energy balance and nominal measurement uncertainty. Drop-in studies are not enough to judge the overall performance of the alternative refrigerants since their thermodynamic and transport properties might favor different heatmore » exchanger configurations, e.g. cross-flow, counter flow, etc. This study compares optimized performances of individual refrigerants using a physics-based system model tools. The DOE/ORNL Heat Pump Design Model (HPDM) was used to model the mini-split RACs by inputting detailed heat exchangers geometries, compressor displacement and efficiencies as well as other relevant system components. The RAC models were calibrated against the lab data for each individual refrigerant. The calibrated models were then used to conduct a design optimization for the cooling performance by varying the compressor displacement to match the required capacity, and changing the number of circuits, refrigerant flow direction, tube diameters, air flow rates in the condenser and evaporator at 100% and 50% cooling capacities. This paper compares the optimized performance results for all alternative refrigerants and highlights best candidates for R-22 and R-410A replacement.« less

Modelling temporal networks of human face-to-face contacts with public activity and individual reachability

NASA Astrophysics Data System (ADS)

Zhang, Yi-Qing; Cui, Jing; Zhang, Shu-Min; Zhang, Qi; Li, Xiang

2016-02-01

Modelling temporal networks of human face-to-face contacts is vital both for understanding the spread of airborne pathogens and word-of-mouth spreading of information. Although many efforts have been devoted to model these temporal networks, there are still two important social features, public activity and individual reachability, have been ignored in these models. Here we present a simple model that captures these two features and other typical properties of empirical face-to-face contact networks. The model describes agents which are characterized by an attractiveness to slow down the motion of nearby people, have event-triggered active probability and perform an activity-dependent biased random walk in a square box with periodic boundary. The model quantitatively reproduces two empirical temporal networks of human face-to-face contacts which are testified by their network properties and the epidemic spread dynamics on them.

Comparison of statistical models for writer verification

NASA Astrophysics Data System (ADS)

Srihari, Sargur; Ball, Gregory R.

2009-01-01

A novel statistical model for determining whether a pair of documents, a known and a questioned, were written by the same individual is proposed. The goal of this formulation is to learn the specific uniqueness of style in a particular author's writing, given the known document. Since there are often insufficient samples to extrapolate a generalized model of an writer's handwriting based solely on the document, we instead generalize over the differences between the author and a large population of known different writers. This is in contrast to an earlier model proposed whereby probability distributions were a priori without learning. We show the performance of the model along with a comparison in performance to the non-learning, older model, which shows significant improvement.

An International Evaluation of Cognitive Reserve and Memory Changes in Early Old Age in 10 European Countries.

PubMed

Cadar, Dorina; Robitaille, Annie; Clouston, Sean; Hofer, Scott M; Piccinin, Andrea M; Muniz-Terrera, Graciela

2017-01-01

Cognitive reserve was postulated to explain individual differences in susceptibility to ageing, offering apparent protection to those with higher education. We investigated the association between education and change in memory in early old age. Immediate and delayed memory scores from over 10,000 individuals aged 65 years and older, from 10 countries of the Survey of Health, Ageing and Retirement in Europe, were modeled as a function of time in the study over an 8-year period, fitting independent latent growth models. Education was used as a marker of cognitive reserve and evaluated in association with memory performance and rate of change, while accounting for income, general health, smoking, body mass index, gender, and baseline age. In most countries, more educated individuals performed better on both memory tests at baseline, compared to those less educated. However, education was not protective against faster decline, except for in Spain for both immediate and delayed recall (0.007 [SE = 0.003] and 0.006 [SE = 0.002]), and Switzerland for immediate recall (0.006 [SE = 0.003]). Interestingly, highly educated Italian respondents had slightly faster declines in immediate recall (-0.006 [SE = 0.003]). We found weak evidence of a protective effect of education on memory change in most European samples, although there was a positive association with memory performance at individuals' baseline assessment. © 2017 The Author(s) Published by S. Karger AG, Basel.

Evaluating Temporal Factors in Combined Interventions of Workforce Shift and School Closure for Mitigating the Spread of Influenza

PubMed Central

Zhang, Tianyou; Fu, Xiuju; Ma, Stefan; Xiao, Gaoxi; Wong, Limsoon; Kwoh, Chee Keong; Lees, Michael; Lee, Gary Kee Khoon; Hung, Terence

2012-01-01

Background It is believed that combined interventions may be more effective than individual interventions in mitigating epidemic. However there is a lack of quantitative studies on performance of the combination of individual interventions under different temporal settings. Methodology/Principal Findings To better understand the problem, we develop an individual-based simulation model running on top of contact networks based on real-life contact data in Singapore. We model and evaluate the spread of influenza epidemic with intervention strategies of workforce shift and its combination with school closure, and examine the impacts of temporal factors, namely the trigger threshold and the duration of an intervention. By comparing simulation results for intervention scenarios with different temporal factors, we find that combined interventions do not always outperform individual interventions and are more effective only when the duration is longer than 6 weeks or school closure is triggered at the 5% threshold; combined interventions may be more effective if school closure starts first when the duration is less than 4 weeks or workforce shift starts first when the duration is longer than 4 weeks. Conclusions/Significance We therefore conclude that identifying the appropriate timing configuration is crucial for achieving optimal or near optimal performance in mitigating the spread of influenza epidemic. The results of this study are useful to policy makers in deliberating and planning individual and combined interventions. PMID:22403634

Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks.

PubMed

Coiera, Enrico; Wang, Ying; Magrabi, Farah; Concha, Oscar Perez; Gallego, Blanca; Runciman, William

2014-05-21

Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models. Patients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria. There was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known 'weekend-effect' where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94). Risks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual's hospitalization. Combining disease specific models with such time varying- estimates appears to result in robust predictive performance. Such risk exposure models should find utility both in enhancing standard prognostic models as well as estimating the risk of continuation of hospitalization.

Accommodating the ecological fallacy in disease mapping in the absence of individual exposures.

PubMed

Wang, Feifei; Wang, Jian; Gelfand, Alan; Li, Fan

2017-12-30

In health exposure modeling, in particular, disease mapping, the ecological fallacy arises because the relationship between aggregated disease incidence on areal units and average exposure on those units differs from the relationship between the event of individual incidence and the associated individual exposure. This article presents a novel modeling approach to address the ecological fallacy in the least informative data setting. We assume the known population at risk with an observed incidence for a collection of areal units and, separately, environmental exposure recorded during the period of incidence at a collection of monitoring stations. We do not assume any partial individual level information or random allocation of individuals to observed exposures. We specify a conceptual incidence surface over the study region as a function of an exposure surface resulting in a stochastic integral of the block average disease incidence. The true block level incidence is an unavailable Monte Carlo integration for this stochastic integral. We propose an alternative manageable Monte Carlo integration for the integral. Modeling in this setting is immediately hierarchical, and we fit our model within a Bayesian framework. To alleviate the resulting computational burden, we offer 2 strategies for efficient model fitting: one is through modularization, the other is through sparse or dimension-reduced Gaussian processes. We illustrate the performance of our model with simulations based on a heat-related mortality dataset in Ohio and then analyze associated real data. Copyright © 2017 John Wiley & Sons, Ltd.

Development of PBPK Models for Gasoline in Adult and ...

EPA Pesticide Factsheets

Concern for potential developmental effects of exposure to gasoline-ethanol blends has grown along with their increased use in the US fuel supply. Physiologically-based pharmacokinetic (PBPK) models for these complex mixtures were developed to address dosimetric issues related to selection of exposure concentrations for in vivo toxicity studies. Sub-models for individual hydrocarbon (HC) constituents were first developed and calibrated with published literature or QSAR-derived data where available. Successfully calibrated sub-models for individual HCs were combined, assuming competitive metabolic inhibition in the liver, and a priori simulations of mixture interactions were performed. Blood HC concentration data were collected from exposed adult non-pregnant (NP) rats (9K ppm total HC vapor, 6h/day) to evaluate performance of the NP mixture model. This model was then converted to a pregnant (PG) rat mixture model using gestational growth equations that enabled a priori estimation of life-stage specific kinetic differences. To address the impact of changing relevant physiological parameters from NP to PG, the PG mixture model was first calibrated against the NP data. The PG mixture model was then evaluated against data from PG rats that were subsequently exposed (9K ppm/6.33h gestation days (GD) 9-20). Overall, the mixture models adequately simulated concentrations of HCs in blood from single (NP) or repeated (PG) exposures (within ~2-3 fold of measured values of

The relation between social anxiety and audience perception: Examining Clark and Wells’ (1995) model among adolescents

PubMed Central

Blöte, Anke W.; Miers, Anne C.; Heyne, David A.; Clark, David M.; Westenberg, P. Michiel

2016-01-01

Background Clark and Wells’ (1995; Clark, 2001) cognitive model of social anxiety proposes that socially anxious individuals have negative expectations of performance prior to a social event, focus their attention predominantly on themselves and on their negative self-evaluations during an event, and use this negative self processing to infer that other people are judging them harshly. Aims The present study tested these propositions. Method The study used a community sample of 161 adolescents aged 14-18 years. The participants gave a speech in front of a pre-recorded audience acting neutrally, and participants were aware that the projected audience was pre-recorded. Results As expected, participants with higher levels of social anxiety had more negative performance expectations, higher self-focused attention, and more negative perceptions of the audience. Negative performance expectations and self-focused attention were found to mediate the relationship between social anxiety and audience perception. Conclusion The findings support Clark and Wells’ cognitive model of social anxiety which poses that socially anxious individuals have distorted perceptions of the responses of other people because their perceptions are colored by their negative thoughts and feelings. PMID:23635882

Can We Predict Cognitive Performance Decrements Due to Sleep Loss and the Recuperative Effects of Caffeine

DTIC Science & Technology

2015-10-14

such as timely short naps and caffeine, are often used to mitigate the effects of sleep loss on performance. However, the timing, duration, and dosage...loss and the restorative effects of different dosages of caffeine on a specific individual’s performance. When used as a decision-aid tool, this model...provides the means to maximize Warfighter cognitive performance, resulting in peak alertness and prolonged alertness at the desired times

Closed loop models for analyzing engineering requirements for simulators

NASA Technical Reports Server (NTRS)

Baron, S.; Muralidharan, R.; Kleinman, D.

1980-01-01

A closed loop analytic model, incorporating a model for the human pilot, (namely, the optimal control model) that would allow certain simulation design tradeoffs to be evaluated quantitatively was developed. This model was applied to a realistic flight control problem. The resulting model is used to analyze both overall simulation effects and the effects of individual elements. The results show that, as compared to an ideal continuous simulation, the discrete simulation can result in significant performance and/or workload penalties.

Influence of children's oral health-related quality of life on school performance and school absenteeism.

PubMed

Piovesan, Chaiana; Antunes, José Leopoldo Ferreira; Mendes, Fausto Medeiros; Guedes, Renata Saraiva; Ardenghi, Thiago Machado

2012-01-01

This study assessed the relation of child oral health-related quality of life with school performance and school absenteeism. We followed a cross-sectional design with a multistage random sample of 312 12-year-old schoolchildren living in Brazil. The participants completed the child perceptions questionnaire (CPQ(11-14) ) that provides information about psychological factors, while their parents or guardians answered questions on their socioeconomic status measured by parents' education level and household income. A dental examination of each child provided information on the prevalence of caries and dental trauma. Data on school performance, which included the results of baseline Brazilian language (Portuguese) tests, and school absenteeism (school days missed) were obtained from the school register. Multilevel linear regression was used to investigate the association among psychological and socioeconomic status and children's school performance. In the multiple model, after adjusting for individual covariates, being a girl was associated with higher school performance (P < 0.05), whereas low household income (P < 0.05), higher mean of CPQ(11-14) (P < 0.05), and higher school days missed (P < 0.001) were identified as individual determinants of lower school performance. When the school-level covariates were included in the model, the association between subjects' level characteristics and school performance still persisted. Children's school performance and absence were influenced by psychological and socioeconomic conditions. © 2012 American Association of Public Health Dentistry.

3D Face Modeling Using the Multi-Deformable Method

PubMed Central

Hwang, Jinkyu; Yu, Sunjin; Kim, Joongrock; Lee, Sangyoun

2012-01-01

In this paper, we focus on the problem of the accuracy performance of 3D face modeling techniques using corresponding features in multiple views, which is quite sensitive to feature extraction errors. To solve the problem, we adopt a statistical model-based 3D face modeling approach in a mirror system consisting of two mirrors and a camera. The overall procedure of our 3D facial modeling method has two primary steps: 3D facial shape estimation using a multiple 3D face deformable model and texture mapping using seamless cloning that is a type of gradient-domain blending. To evaluate our method's performance, we generate 3D faces of 30 individuals and then carry out two tests: accuracy test and robustness test. Our method shows not only highly accurate 3D face shape results when compared with the ground truth, but also robustness to feature extraction errors. Moreover, 3D face rendering results intuitively show that our method is more robust to feature extraction errors than other 3D face modeling methods. An additional contribution of our method is that a wide range of face textures can be acquired by the mirror system. By using this texture map, we generate realistic 3D face for individuals at the end of the paper. PMID:23201976

Organizational and Market Influences on Physician Performance on Patient Experience Measures

PubMed Central

Rodriguez, Hector P; von Glahn, Ted; Rogers, William H; Safran, Dana Gelb

2009-01-01

Objective To examine the extent to which medical group and market factors are related to individual primary care physician (PCP) performance on patient experience measures. Data Sources This study employs Clinician and Group CAHPS survey data (n=105,663) from 2,099 adult PCPs belonging to 34 diverse medical groups across California. Medical group directors were interviewed to assess the magnitude and nature of financial incentives directed at individual physicians and the adoption of patient experience improvement strategies. Primary care services area (PCSA) data were used to characterize the market environment of physician practices. Study Design We used multilevel models to estimate the relationship between medical group and market factors and physician performance on each Clinician and Group CAHPS measure. Models statistically controlled for respondent characteristics and accounted for the clustering of respondents within physicians, physicians within medical groups, and medical groups within PCSAs using random effects. Principal Findings Compared with physicians belonging to independent practice associations, physicians belonging to integrated medical groups had better performance on the communication (p=.007) and care coordination (p=.03) measures. Physicians belonging to medical groups with greater numbers of PCPs had better performance on all measures. The use of patient experience improvement strategies was not associated with performance. Greater emphasis on productivity and efficiency criteria in individual physician financial incentive formulae was associated with worse access to care (p=.04). Physicians located in PCSAs with higher area-level deprivation had worse performance on the access to care (p=.04) and care coordination (p<.001) measures. Conclusions Physicians from integrated medical groups and groups with greater numbers of PCPs performed better on several patient experience measures, suggesting that organized care processes adopted by these groups may enhance patients' experiences. Physicians practicing in markets with high concentrations of vulnerable populations may be disadvantaged by constraints that affect performance. Future studies should clarify the extent to which performance deficits associated with area-level deprivation are modifiable. PMID:19674429

Organizational and market influences on physician performance on patient experience measures.

PubMed

Rodriguez, Hector P; von Glahn, Ted; Rogers, William H; Safran, Dana Gelb

2009-06-01

To examine the extent to which medical group and market factors are related to individual primary care physician (PCP) performance on patient experience measures. This study employs Clinician and Group CAHPS survey data (n=105,663) from 2,099 adult PCPs belonging to 34 diverse medical groups across California. Medical group directors were interviewed to assess the magnitude and nature of financial incentives directed at individual physicians and the adoption of patient experience improvement strategies. Primary care services area (PCSA) data were used to characterize the market environment of physician practices. We used multilevel models to estimate the relationship between medical group and market factors and physician performance on each Clinician and Group CAHPS measure. Models statistically controlled for respondent characteristics and accounted for the clustering of respondents within physicians, physicians within medical groups, and medical groups within PCSAs using random effects. Compared with physicians belonging to independent practice associations, physicians belonging to integrated medical groups had better performance on the communication ( p=.007) and care coordination ( p=.03) measures. Physicians belonging to medical groups with greater numbers of PCPs had better performance on all measures. The use of patient experience improvement strategies was not associated with performance. Greater emphasis on productivity and efficiency criteria in individual physician financial incentive formulae was associated with worse access to care ( p=.04). Physicians located in PCSAs with higher area-level deprivation had worse performance on the access to care ( p=.04) and care coordination ( p<.001) measures. Physicians from integrated medical groups and groups with greater numbers of PCPs performed better on several patient experience measures, suggesting that organized care processes adopted by these groups may enhance patients' experiences. Physicians practicing in markets with high concentrations of vulnerable populations may be disadvantaged by constraints that affect performance. Future studies should clarify the extent to which performance deficits associated with area-level deprivation are modifiable.

External validation of the Garvan nomograms for predicting absolute fracture risk: the Tromsø study.

PubMed

Ahmed, Luai A; Nguyen, Nguyen D; Bjørnerem, Åshild; Joakimsen, Ragnar M; Jørgensen, Lone; Størmer, Jan; Bliuc, Dana; Center, Jacqueline R; Eisman, John A; Nguyen, Tuan V; Emaus, Nina

2014-01-01

Absolute risk estimation is a preferred approach for assessing fracture risk and treatment decision making. This study aimed to evaluate and validate the predictive performance of the Garvan Fracture Risk Calculator in a Norwegian cohort. The analysis included 1637 women and 1355 aged 60+ years from the Tromsø study. All incident fragility fractures between 2001 and 2009 were registered. The predicted probabilities of non-vertebral osteoporotic and hip fractures were determined using models with and without BMD. The discrimination and calibration of the models were assessed. Reclassification analysis was used to compare the models performance. The incidence of osteoporotic and hip fracture was 31.5 and 8.6 per 1000 population in women, respectively; in men the corresponding incidence was 12.2 and 5.1. The predicted 5-year and 10-year probability of fractures was consistently higher in the fracture group than the non-fracture group for all models. The 10-year predicted probabilities of hip fracture in those with fracture was 2.8 (women) to 3.1 times (men) higher than those without fracture. There was a close agreement between predicted and observed risk in both sexes and up to the fifth quintile. Among those in the highest quintile of risk, the models over-estimated the risk of fracture. Models with BMD performed better than models with body weight in correct classification of risk in individuals with and without fracture. The overall net decrease in reclassification of the model with weight compared to the model with BMD was 10.6% (p = 0.008) in women and 17.2% (p = 0.001) in men for osteoporotic fractures, and 13.3% (p = 0.07) in women and 17.5% (p = 0.09) in men for hip fracture. The Garvan Fracture Risk Calculator is valid and clinically useful in identifying individuals at high risk of fracture. The models with BMD performed better than those with body weight in fracture risk prediction.

Emergent collective decision-making: Control, model and behavior

NASA Astrophysics Data System (ADS)

Shen, Tian

In this dissertation we study emergent collective decision-making in social groups with time-varying interactions and heterogeneously informed individuals. First we analyze a nonlinear dynamical systems model motivated by animal collective motion with heterogeneously informed subpopulations, to examine the role of uninformed individuals. We find through formal analysis that adding uninformed individuals in a group increases the likelihood of a collective decision. Secondly, we propose a model for human shared decision-making with continuous-time feedback and where individuals have little information about the true preferences of other group members. We study model equilibria using bifurcation analysis to understand how the model predicts decisions based on the critical threshold parameters that represent an individual's tradeoff between social and environmental influences. Thirdly, we analyze continuous-time data of pairs of human subjects performing an experimental shared tracking task using our second proposed model in order to understand transient behavior and the decision-making process. We fit the model to data and show that it reproduces a wide range of human behaviors surprisingly well, suggesting that the model may have captured the mechanisms of observed behaviors. Finally, we study human behavior from a game-theoretic perspective by modeling the aforementioned tracking task as a repeated game with incomplete information. We show that the majority of the players are able to converge to playing Nash equilibrium strategies. We then suggest with simulations that the mean field evolution of strategies in the population resemble replicator dynamics, indicating that the individual strategies may be myopic. Decisions form the basis of control and problems involving deciding collectively between alternatives are ubiquitous in nature and in engineering. Understanding how multi-agent systems make decisions among alternatives also provides insight for designing decentralized control laws for engineering applications from mobile sensor networks for environmental monitoring to collective construction robots. With this dissertation we hope to provide additional methodology and mathematical models for understanding the behavior and control of collective decision-making in multi-agent systems.

Unraveling mysteries of personal performance style; biomechanics of left-hand position changes (shifting) in violin performance.

PubMed

Visentin, Peter; Li, Shiming; Tardif, Guillaume; Shan, Gongbing

2015-01-01

Instrumental music performance ranks among the most complex of learned human behaviors, requiring development of highly nuanced powers of sensory and neural discrimination, intricate motor skills, and adaptive abilities in a temporal activity. Teaching, learning and performing on the violin generally occur within musico-cultural parameters most often transmitted through aural traditions that include both verbal instruction and performance modeling. In most parts of the world, violin is taught in a manner virtually indistinguishable from that used 200 years ago. The current study uses methods from movement science to examine the "how" and "what" of left-hand position changes (shifting), a movement skill essential during violin performance. In doing so, it begins a discussion of artistic individualization in terms of anthropometry, the performer-instrument interface, and the strategic use of motor behaviors. Results based on 540 shifting samples, a case series of 6 professional-level violinists, showed that some elements of the skill were individualized in surprising ways while others were explainable by anthropometry, ergonomics and entrainment. Remarkably, results demonstrated each violinist to have developed an individualized pacing for shifts, a feature that should influence timing effects and prove foundational to aesthetic outcomes during performance. Such results underpin the potential for scientific methodologies to unravel mysteries of performance that are associated with a performer's personal artistic style.

INDIVIDUAL DIFFERENCES IN TASK-SPECIFIC PAIRED ASSOCIATES LEARNING IN OLDER ADULTS: THE ROLE OF PROCESSING SPEED AND WORKING MEMORY

PubMed Central

Kurtz, Tanja; Mogle, Jacqueline; Sliwinski, Martin J.; Hofer, Scott M.

2013-01-01

Background The role of processing speed and working memory was investigated in terms of individual differences in task-specific paired associates learning in a sample of older adults. Task-specific learning, as distinct from content-oriented item-specific learning, refers to gains in performance due to repeated practice on a learning task in which the to-be-learned material changes over trials. Methods Learning trajectories were modeled within an intensive repeated-measures design based on participants obtained from an opt-in internet-based sampling service (Mage = 65.3, SD = 4.81). Participants completed an eight-item paired associates task daily over a seven-day period. Results Results indicated that a three-parameter hyperbolic model (i.e., initial level, learning rate, and asymptotic performance) best described learning trajectory. After controlling for age-related effects, both higher working memory and higher processing speed had a positive effect on all three learning parameters. Conclusion These results emphasize the role of cognitive abilities for individual differences in task-specific learning of older adults. PMID:24151913

Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies.

PubMed

Huang, Yangxin; Lu, Xiaosun; Chen, Jiaqing; Liang, Juan; Zangmeister, Miriam

2017-10-27

Longitudinal and time-to-event data are often observed together. Finite mixture models are currently used to analyze nonlinear heterogeneous longitudinal data, which, by releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, can cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, and be associated with clinically important time-to-event data. This article develops a joint modeling approach to a finite mixture of NLME models for longitudinal data and proportional hazard Cox model for time-to-event data, linked by individual latent class indicators, under a Bayesian framework. The proposed joint models and method are applied to a real AIDS clinical trial data set, followed by simulation studies to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and Cox model are fitted separately.

Performance Analysis of Hospital Managers Using Fuzzy AHP and Fuzzy TOPSIS: Iranian Experience.

PubMed

Shafii, Milad; Hosseini, Seyed Mostafa; Arab, Mohammad; Asgharizadeh, Ezzatollah; Farzianpour, Fereshteh

2015-06-12

Hospitals are complex organizations that require strong and effective management. The success of such organizations depends on the performance of managers. This study provides a comprehensive set of indicators to assess the performance of hospital managers in Iranian Ministry of Health owned hospitals. This research was a cross-sectional study. First, reviewing the literature and using experts' viewpoints and convening a panel of experts, the dimensions of performance have been selected and came in the form of a performance model. Then, using Fuzzy Analytic Hierarchy Process (FAHP), the chosen dimensions were weighted. Finally, based on the weighted performance dimensions, a questionnaire was designed and after confirming the reliability and validity, through a census, 407 senior and middle managers from 10 hospitals in Yazd, Iran completed it and performance of CEOs in these hospitals was evaluated using the Fuzzy Technique for Order Preference by Similarity Ideal Solution (FTOPSIS). To measure the performance of hospital managers, a performance assessment model consisted of 19 sub-dimensions in 5 main dimensions (Functional, Professional, Organizational, Individual and Human) was developed. The functional area had the most weight and the individual area had the least weight, as well. The hospital managers had different performance levels in each category and sub-dimensions. In terms of overall performance, the hospital managers C and H had the best and the worst performance, respectively. The use of appropriate dimensions for performance, prioritizing them and evaluating the performance of hospital managers using appropriate techniques, can play an effective role in the selection of qualified managers, identifying strengths and weaknesses in performance and continuous improvement of them.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

PubMed

Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

2016-10-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

PubMed Central

Staras, Kevin

2016-01-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture. PMID:27760125

Mining Twitter Data to Improve Detection of Schizophrenia

PubMed Central

McManus, Kimberly; Mallory, Emily K.; Goldfeder, Rachel L.; Haynes, Winston A.; Tatum, Jonathan D.

2015-01-01

Individuals who suffer from schizophrenia comprise I percent of the United States population and are four times more likely to die of suicide than the general US population. Identification of at-risk individuals with schizophrenia is challenging when they do not seek treatment. Microblogging platforms allow users to share their thoughts and emotions with the world in short snippets of text. In this work, we leveraged the large corpus of Twitter posts and machine-learning methodologies to detect individuals with schizophrenia. Using features from tweets such as emoticon use, posting time of day, and dictionary terms, we trained, built, and validated several machine learning models. Our support vector machine model achieved the best performance with 92% precision and 71% recall on the held-out test set. Additionally, we built a web application that dynamically displays summary statistics between cohorts. This enables outreach to undiagnosed individuals, improved physician diagnoses, and destigmatization of schizophrenia. PMID:26306253

How the government's punishment and individual's sensitivity affect the rumor spreading in online social networks

NASA Astrophysics Data System (ADS)

Li, Dandan; Ma, Jing

2017-03-01

We explore the impact of punishment of governments and sensitivity of individuals on the rumor spreading in this paper. Considering the facts that some rumors that relate to the hot events could be disseminated repeatedly, however, some other rumors will never be disseminated after they have been popular for some time. Therefore, we investigate two types (SIS and SIR) of rumor spreading models in which the punishment of government and sensitivity of individuals are considered. Based on the mean-field method, we have calculated the spreading threshold of SIS and SIR model, respectively. Furthermore, we perform the rumor spreading process in the Facebook and POK social networks, and achieve that there is an excellent agreement between the theoretical and numerical results of spreading threshold. The results indicate that improving the punishment of government and increasing the sensitivity of individuals could control the spreading of rumor effectively.

What distinguishes individual stocks from the index?

NASA Astrophysics Data System (ADS)

Wagner, F.; Milaković, M.; Alfarano, S.

2010-01-01

Stochastic volatility models decompose the time series of financial returns into the product of a volatility factor and an iid noise factor. Assuming a slow dynamic for the volatility factor, we show via nonparametric tests that both the index as well as its individual stocks share a common volatility factor. While the noise component is Gaussian for the index, individual stock returns turn out to require a leptokurtic noise. Thus we propose a two-component model for stocks, given by the sum of Gaussian noise, which reflects market-wide fluctuations, and Laplacian noise, which incorporates firm-specific factors such as firm profitability or growth performance, both of which are known to be Laplacian distributed. In the case of purely Gaussian noise, the chi-squared probability for the density of individual stock returns is typically on the order of 10-20, while it increases to values of O(1) by adding the Laplace component.

Magnitude and Temporal Variability of Inter-stimulus EEG Modulate the Linear Relationship Between Laser-Evoked Potentials and Fast-Pain Perception

PubMed Central

Li, Linling; Huang, Gan; Lin, Qianqian; Liu, Jia; Zhang, Shengli; Zhang, Zhiguo

2018-01-01

The level of pain perception is correlated with the magnitude of pain-evoked brain responses, such as laser-evoked potentials (LEP), across trials. The positive LEP-pain relationship lays the foundation for pain prediction based on single-trial LEP, but cross-individual pain prediction does not have a good performance because the LEP-pain relationship exhibits substantial cross-individual difference. In this study, we aim to explain the cross-individual difference in the LEP-pain relationship using inter-stimulus EEG (isEEG) features. The isEEG features (root mean square as magnitude and mean square successive difference as temporal variability) were estimated from isEEG data (at full band and five frequency bands) recorded between painful stimuli. A linear model was fitted to investigate the relationship between pain ratings and LEP response for fast-pain trials on a trial-by-trial basis. Then the correlation between isEEG features and the parameters of LEP-pain model (slope and intercept) was evaluated. We found that the magnitude and temporal variability of isEEG could modulate the parameters of an individual's linear LEP-pain model for fast-pain trials. Based on this, we further developed a new individualized fast-pain prediction scheme, which only used training individuals with similar isEEG features as the test individual to train the fast-pain prediction model, and obtained improved accuracy in cross-individual fast-pain prediction. The findings could help elucidate the neural mechanism of cross-individual difference in pain experience and the proposed fast-pain prediction scheme could be potentially used as a practical and feasible pain prediction method in clinical practice. PMID:29904336

Performance of the herb Verbascum thapsus along environmental gradients in its native and non-native ranges

Treesearch

Tim Seipel; Jake M. Alexander; Curtis C. Daehler; Lisa J. Rew; Peter J. Edwards; Pervaiz A. Dar; Keith McDougall; Bridgett Naylor; Catherine Parks; Fredric W. Pollnac; Zafar A. Reshi; Mel Schroder; Christoph Kueffer; Peter Pearman

2014-01-01

Aim We evaluated whether the performance of individuals and populations of the invasive plant Verbascum thapsus differs between its native and non-native ranges, across climate gradients, and in response to its position in a global- scaled niche model.Location India (Kashmir) and Switzerland (native range) and Australia and USA (Hawaii,...

Perceived Control and Hedonic Tone Dynamics during Performance in Elite Shooters

ERIC Educational Resources Information Center

Robazza, Claudio; Bertollo, Maurizio; Filho, Edson; Hanin, Yuri; Bortoli, Laura

2016-01-01

Purpose: The purpose of the study was to investigate the individuals' dynamics of perceived control and hedonic tone over time, with respect to the 4 performance states as conceptualized within the multiaction plan (MAP) model. We expected to find idiosyncratic and differentiated trends over time in the scores of perceived control and hedonic…

High capacity demonstration of honeycomb panel heat pipes

NASA Technical Reports Server (NTRS)

Tanzer, H. J.

1989-01-01

The feasibility of performance enhancing the sandwich panel heat pipe was investigated for moderate temperature range heat rejection radiators on future-high-power spacecraft. The hardware development program consisted of performance prediction modeling, fabrication, ground test, and data correlation. Using available sandwich panel materials, a series of subscale test panels were augumented with high-capacity sideflow and temperature control variable conductance features, and test evaluated for correlation with performance prediction codes. Using the correlated prediction model, a 50-kW full size radiator was defined using methanol working fluid and closely spaced sideflows. A new concept called the hybrid radiator individually optimizes heat pipe components. A 2.44-m long hybrid test vehicle demonstrated proof-of-principle performance.

Expert performance in sport and the dynamics of talent development.

PubMed

Phillips, Elissa; Davids, Keith; Renshaw, Ian; Portus, Marc

2010-04-01

Research on expertise, talent identification and development has tended to be mono-disciplinary, typically adopting genocentric or environmentalist positions, with an overriding focus on operational issues. In this paper, the validity of dualist positions on sport expertise is evaluated. It is argued that, to advance understanding of expertise and talent development, a shift towards a multidisciplinary and integrative science focus is necessary, along with the development of a comprehensive multidisciplinary theoretical rationale. Here we elucidate dynamical systems theory as a multidisciplinary theoretical rationale for capturing how multiple interacting constraints can shape the development of expert performers. This approach suggests that talent development programmes should eschew the notion of common optimal performance models, emphasize the individual nature of pathways to expertise, and identify the range of interacting constraints that impinge on performance potential of individual athletes, rather than evaluating current performance on physical tests referenced to group norms.

Variations in clinical nurse leaders' confidence with performing the core role functions.

PubMed

Gilmartin, Mattia J

2014-01-01

Clinical nurse leader (CNL) practice, by definition, requires individuals to make career transitions. CNLs must adjust to their new work role and responsibilities and doing so also entails individual adjustment. Prior work has not examined the role of individual-level factors in effective CNL role transition. This study contributes to CNL implementation efforts by developing understanding of personal and contextual factors that explain variation in individuals' levels of self-confidence with performing the key functions of the CNL role. Data were gathered using a cross-sectional survey from a national sample of registered nurses (RNs) certified as CNLs. Respondents' perceptions of their confidence in performing CNL role competencies were measured with the Clinical Nurse Leader Self-Efficacy Scale (CNLSES; Gilmartin MJ, Nokes, K. (in press). The Clinical Nurse Leader Self Efficacy Scale: Results of a pilot study. Nursing Economic$). The CNLSES is a 35-item state-specific self-efficacy scale with established measurement properties that assesses nurses' perceptions of their ability to function effectively as a CNL. Demographic data were also collected. Data were analyzed using a general linear regression model. One hundred forty-seven certified CNLs participated in the survey. Results indicate that respondents vary in their confidence with performing the nine role competencies associated with CNL practice. Results from regression analyses also show that respondents' confidence in their abilities to carry out the core functions associated with the CNL role varied significantly across geographic region, organizational type, and by CNL graduate program model. The results of this study show important differences in CNLs' levels of self-confidence with the core competencies of their role. As a result, it may be important to develop targeted career transition interventions to gain the full benefit of CNL practice. Copyright © 2014 Elsevier Inc. All rights reserved.

Bayesian adjustment for measurement error in continuous exposures in an individually matched case-control study.

PubMed

Espino-Hernandez, Gabriela; Gustafson, Paul; Burstyn, Igor

2011-05-14

In epidemiological studies explanatory variables are frequently subject to measurement error. The aim of this paper is to develop a Bayesian method to correct for measurement error in multiple continuous exposures in individually matched case-control studies. This is a topic that has not been widely investigated. The new method is illustrated using data from an individually matched case-control study of the association between thyroid hormone levels during pregnancy and exposure to perfluorinated acids. The objective of the motivating study was to examine the risk of maternal hypothyroxinemia due to exposure to three perfluorinated acids measured on a continuous scale. Results from the proposed method are compared with those obtained from a naive analysis. Using a Bayesian approach, the developed method considers a classical measurement error model for the exposures, as well as the conditional logistic regression likelihood as the disease model, together with a random-effect exposure model. Proper and diffuse prior distributions are assigned, and results from a quality control experiment are used to estimate the perfluorinated acids' measurement error variability. As a result, posterior distributions and 95% credible intervals of the odds ratios are computed. A sensitivity analysis of method's performance in this particular application with different measurement error variability was performed. The proposed Bayesian method to correct for measurement error is feasible and can be implemented using statistical software. For the study on perfluorinated acids, a comparison of the inferences which are corrected for measurement error to those which ignore it indicates that little adjustment is manifested for the level of measurement error actually exhibited in the exposures. Nevertheless, a sensitivity analysis shows that more substantial adjustments arise if larger measurement errors are assumed. In individually matched case-control studies, the use of conditional logistic regression likelihood as a disease model in the presence of measurement error in multiple continuous exposures can be justified by having a random-effect exposure model. The proposed method can be successfully implemented in WinBUGS to correct individually matched case-control studies for several mismeasured continuous exposures under a classical measurement error model.

Collective-efficacy as a mediator of the relationship of leaders' personality traits and team performance: A cross-level analysis.

PubMed

Li, Xiaoshan; Zhou, Mingjie; Zhao, Na; Zhang, Shanshan; Zhang, Jianxin

2015-06-01

The relationship between a leader's personality and his team's performance has been established in organisational research, but the underlying process and mechanism responsible for this effect have not been fully explored. Both the traditional multiple linear regression and the multilevel structural equation model approaches were used in this study to test a proposed mediating model of subordinates' perception of collective efficacy between leader personality and team performance. The results show that the team leader's extraversion and conscientiousness personality traits were related positively to both the team-average (individual) perception of collective efficacy and team performance, and the collective efficacy mediated the relationship of the leader's personality traits and team performance. This study also discusses how Chinese cultural elements play a role in such a mediating model. © 2014 International Union of Psychological Science.

Barriers to using eHealth data for clinical performance feedback in Malawi: A case study.

PubMed

Landis-Lewis, Zach; Manjomo, Ronald; Gadabu, Oliver J; Kam, Matthew; Simwaka, Bertha N; Zickmund, Susan L; Chimbwandira, Frank; Douglas, Gerald P; Jacobson, Rebecca S

2015-10-01

Sub-optimal performance of healthcare providers in low-income countries is a critical and persistent global problem. The use of electronic health information technology (eHealth) in these settings is creating large-scale opportunities to automate performance measurement and provision of feedback to individual healthcare providers, to support clinical learning and behavior change. An electronic medical record system (EMR) deployed in 66 antiretroviral therapy clinics in Malawi collects data that supervisors use to provide quarterly, clinic-level performance feedback. Understanding barriers to provision of eHealth-based performance feedback for individual healthcare providers in this setting could present a relatively low-cost opportunity to significantly improve the quality of care. The aims of this study were to identify and describe barriers to using EMR data for individualized audit and feedback for healthcare providers in Malawi and to consider how to design technology to overcome these barriers. We conducted a qualitative study using interviews, observations, and informant feedback in eight public hospitals in Malawi where an EMR system is used. We interviewed 32 healthcare providers and conducted seven hours of observation of system use. We identified four key barriers to the use of EMR data for clinical performance feedback: provider rotations, disruptions to care processes, user acceptance of eHealth, and performance indicator lifespan. Each of these factors varied across sites and affected the quality of EMR data that could be used for the purpose of generating performance feedback for individual healthcare providers. Using routinely collected eHealth data to generate individualized performance feedback shows potential at large-scale for improving clinical performance in low-resource settings. However, technology used for this purpose must accommodate ongoing changes in barriers to eHealth data use. Understanding the clinical setting as a complex adaptive system (CAS) may enable designers of technology to effectively model change processes to mitigate these barriers. Copyright © 2015. Published by Elsevier Ireland Ltd.

Barriers to using eHealth data for clinical performance feedback in Malawi: A case study

PubMed Central

Landis-Lewis, Zach; Manjomo, Ronald; Gadabu, Oliver J; Kam, Matthew; Simwaka, Bertha N; Zickmund, Susan L; Chimbwandira, Frank; Douglas, Gerald P; Jacobson, Rebecca S

2016-01-01

Introduction Sub-optimal performance of healthcare providers in low-income countries is a critical and persistent global problem. The use of electronic health information technology (eHealth) in these settings is creating large-scale opportunities to automate performance measurement and provision of feedback to individual healthcare providers, to support clinical learning and behavior change. An electronic medical record system (EMR) deployed in 66 antiretroviral therapy clinics in Malawi collects data that supervisors use to provide quarterly, clinic-level performance feedback. Understanding barriers to provision of eHealth-based performance feedback for individual healthcare providers in this setting could present a relatively low-cost opportunity to significantly improve the quality of care. Objective The aims of this study were to identify and describe barriers to using EMR data for individualized audit and feedback for healthcare providers in Malawi and to consider how to design technology to overcome these barriers. Methods We conducted a qualitative study using interviews, observations, and informant feedback in eight public hospitals in Malawi where an EMR is used. We interviewed 32 healthcare providers and conducted seven hours of observation of system use. Results We identified four key barriers to the use of EMR data for clinical performance feedback: provider rotations, disruptions to care processes, user acceptance of eHealth, and performance indicator lifespan. Each of these factors varied across sites and affected the quality of EMR data that could be used for the purpose of generating performance feedback for individual healthcare providers. Conclusion Using routinely collected eHealth data to generate individualized performance feedback shows potential at large-scale for improving clinical performance in low-resource settings. However, technology used for this purpose must accommodate ongoing changes in barriers to eHealth data use. Understanding the clinical setting as a complex adaptive system (CAS) may enable designers of technology to effectively model change processes to mitigate these barriers. PMID:26238704

Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms.

PubMed

Schädler, Marc R; Warzybok, Anna; Kollmeier, Birger

2018-01-01

The simulation framework for auditory discrimination experiments (FADE) was adopted and validated to predict the individual speech-in-noise recognition performance of listeners with normal and impaired hearing with and without a given hearing-aid algorithm. FADE uses a simple automatic speech recognizer (ASR) to estimate the lowest achievable speech reception thresholds (SRTs) from simulated speech recognition experiments in an objective way, independent from any empirical reference data. Empirical data from the literature were used to evaluate the model in terms of predicted SRTs and benefits in SRT with the German matrix sentence recognition test when using eight single- and multichannel binaural noise-reduction algorithms. To allow individual predictions of SRTs in binaural conditions, the model was extended with a simple better ear approach and individualized by taking audiograms into account. In a realistic binaural cafeteria condition, FADE explained about 90% of the variance of the empirical SRTs for a group of normal-hearing listeners and predicted the corresponding benefits with a root-mean-square prediction error of 0.6 dB. This highlights the potential of the approach for the objective assessment of benefits in SRT without prior knowledge about the empirical data. The predictions for the group of listeners with impaired hearing explained 75% of the empirical variance, while the individual predictions explained less than 25%. Possibly, additional individual factors should be considered for more accurate predictions with impaired hearing. A competing talker condition clearly showed one limitation of current ASR technology, as the empirical performance with SRTs lower than -20 dB could not be predicted.

Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms

PubMed Central

Schädler, Marc R.; Warzybok, Anna; Kollmeier, Birger

2018-01-01

The simulation framework for auditory discrimination experiments (FADE) was adopted and validated to predict the individual speech-in-noise recognition performance of listeners with normal and impaired hearing with and without a given hearing-aid algorithm. FADE uses a simple automatic speech recognizer (ASR) to estimate the lowest achievable speech reception thresholds (SRTs) from simulated speech recognition experiments in an objective way, independent from any empirical reference data. Empirical data from the literature were used to evaluate the model in terms of predicted SRTs and benefits in SRT with the German matrix sentence recognition test when using eight single- and multichannel binaural noise-reduction algorithms. To allow individual predictions of SRTs in binaural conditions, the model was extended with a simple better ear approach and individualized by taking audiograms into account. In a realistic binaural cafeteria condition, FADE explained about 90% of the variance of the empirical SRTs for a group of normal-hearing listeners and predicted the corresponding benefits with a root-mean-square prediction error of 0.6 dB. This highlights the potential of the approach for the objective assessment of benefits in SRT without prior knowledge about the empirical data. The predictions for the group of listeners with impaired hearing explained 75% of the empirical variance, while the individual predictions explained less than 25%. Possibly, additional individual factors should be considered for more accurate predictions with impaired hearing. A competing talker condition clearly showed one limitation of current ASR technology, as the empirical performance with SRTs lower than −20 dB could not be predicted. PMID:29692200

Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values.

PubMed

Óskarsdóttir, María; Baesens, Bart; Vanthienen, Jan

2018-03-01

The goal of customer retention campaigns, by design, is to add value and enhance the operational efficiency of businesses. For organizations that strive to retain their customers in saturated, and sometimes fast moving, markets such as the telecommunication and banking industries, implementing customer churn prediction models that perform well and in accordance with the business goals is vital. The expected maximum profit (EMP) measure is tailored toward this problem by taking into account the costs and benefits of a retention campaign and estimating its worth for the organization. Unfortunately, the measure assumes fixed and equal customer lifetime value (CLV) for all customers, which has been shown to not correspond well with reality. In this article, we extend the EMP measure to take into account the variability in the lifetime values of customers, thereby basing it on individual characteristics. We demonstrate how to incorporate the heterogeneity of CLVs when CLVs are known, when their prior distribution is known, and when neither is known. By taking into account individual CLVs, our proposed approach of measuring model performance gives novel insights when deciding on a customer retention campaign. The method is dependent on the characteristics of the customer base as is compliant with modern business analytics and accommodates the data-driven culture that has manifested itself within organizations.

The role of cognitive reserve and memory self-efficacy in compensatory strategy use: A structural equation approach.

PubMed

Simon, Christa; Schmitter-Edgecombe, Maureen

2016-08-01

The use of compensatory strategies plays an important role in the ability of older adults to adapt to late-life memory changes. Even with the benefits associated with compensatory strategy use, little research has explored specific mechanisms associated with memory performance and compensatory strategies. Rather than an individual's objective memory performance directly predicting their use of compensatory strategies, it is possible that some other variables are indirectly influencing that relationship. The purpose of this study was to: (a) examine the moderating effects of cognitive reserve (CR) and (b) evaluate the potential mediating effects of memory self-efficacy on the relationship between objective memory performance and compensatory strategy use. Two structural equation models (SEM) were used to evaluate CR (latent moderator model) and memory self-efficacy (mediator model) in a sample of 155 community-dwelling older adults over the age of 55. The latent variable moderator model indicated that CR was not substantiated as a moderator variable in this sample (p = .861). However, memory self-efficacy significantly mediated the association between objective memory performance and compensatory strategy use (β = .22, 95% confidence interval, CI [.002, .437]). More specifically, better objective memory was associated with lower compensatory strategy use because of its relation to higher memory self-efficacy. These findings provide initial support for an explanatory framework of the relation between objective memory and compensatory strategy use in a healthy older adult population by identifying the importance of an individual's memory perceptions.

An individual-based model for biofilm formation at liquid surfaces

NASA Astrophysics Data System (ADS)

Ardré, Maxime; Henry, Hervé; Douarche, Carine; Plapp, Mathis

2015-12-01

The bacterium Bacillus subtilis frequently forms biofilms at the interface between the culture medium and the air. We present a mathematical model that couples a description of bacteria as individual discrete objects to the standard advection-diffusion equations for the environment. The model takes into account two different bacterial phenotypes. In the motile state, bacteria swim and perform a run-and-tumble motion that is biased toward regions of high oxygen concentration (aerotaxis). In the matrix-producer state they excrete extracellular polymers, which allows them to connect to other bacteria and to form a biofilm. Bacteria are also advected by the fluid, and can trigger bioconvection. Numerical simulations of the model reproduce all the stages of biofilm formation observed in laboratory experiments. Finally, we study the influence of various model parameters on the dynamics and morphology of biofilms.

Cognitive predictors of balance in Parkinson's disease.

PubMed

Fernandes, Ângela; Mendes, Andreia; Rocha, Nuno; Tavares, João Manuel R S

2016-06-01

Postural instability is one of the most incapacitating symptoms of Parkinson's disease (PD) and appears to be related to cognitive deficits. This study aims to determine the cognitive factors that can predict deficits in static and dynamic balance in individuals with PD. A sociodemographic questionnaire characterized 52 individuals with PD for this work. The Trail Making Test, Rule Shift Cards Test, and Digit Span Test assessed the executive functions. The static balance was assessed using a plantar pressure platform, and dynamic balance was based on the Timed Up and Go Test. The results were statistically analysed using SPSS Statistics software through linear regression analysis. The results show that a statistically significant model based on cognitive outcomes was able to explain the variance of motor variables. Also, the explanatory value of the model tended to increase with the addition of individual and clinical variables, although the resulting model was not statistically significant The model explained 25-29% of the variability of the Timed Up and Go Test, while for the anteroposterior displacement it was 23-34%, and for the mediolateral displacement it was 24-39%. From the findings, we conclude that the cognitive performance, especially the executive functions, is a predictor of balance deficit in individuals with PD.

Modeling error in assessment of mammographic image features for improved computer-aided mammography training: initial experience

NASA Astrophysics Data System (ADS)

Mazurowski, Maciej A.; Tourassi, Georgia D.

2011-03-01

In this study we investigate the hypothesis that there exist patterns in erroneous assessment of BI-RADS image features among radiology trainees when performing diagnostic interpretation of mammograms. We also investigate whether these error making patterns can be captured by individual user models. To test our hypothesis we propose a user modeling algorithm that uses the previous readings of a trainee to identify whether certain BI-RADS feature values (e.g. "spiculated" value for "margin" feature) are associated with higher than usual likelihood that the feature will be assessed incorrectly. In our experiments we used readings of 3 radiology residents and 7 breast imaging experts for 33 breast masses for the following BI-RADS features: parenchyma density, mass margin, mass shape and mass density. The expert readings were considered as the gold standard. Rule-based individual user models were developed and tested using the leave one-one-out crossvalidation scheme. Our experimental evaluation showed that the individual user models are accurate in identifying cases for which errors are more likely to be made. The user models captured regularities in error making for all 3 residents. This finding supports our hypothesis about existence of individual error making patterns in assessment of mammographic image features using the BI-RADS lexicon. Explicit user models identifying the weaknesses of each resident could be of great use when developing and adapting a personalized training plan to meet the resident's individual needs. Such approach fits well with the framework of adaptive computer-aided educational systems in mammography we have proposed before.

Research on odor interaction between aldehyde compounds via a partial differential equation (PDE) model.

PubMed

Yan, Luchun; Liu, Jiemin; Qu, Chen; Gu, Xingye; Zhao, Xia

2015-01-28

In order to explore the odor interaction of binary odor mixtures, a series of odor intensity evaluation tests were performed using both individual components and binary mixtures of aldehydes. Based on the linear relation between the logarithm of odor activity value and odor intensity of individual substances, the relationship between concentrations of individual constituents and their joint odor intensity was investigated by employing a partial differential equation (PDE) model. The obtained results showed that the binary odor interaction was mainly influenced by the mixing ratio of two constituents, but not the concentration level of an odor sample. Besides, an extended PDE model was also proposed on the basis of the above experiments. Through a series of odor intensity matching tests for several different binary odor mixtures, the extended PDE model was proved effective at odor intensity prediction. Furthermore, odorants of the same chemical group and similar odor type exhibited similar characteristics in the binary odor interaction. The overall results suggested that the PDE model is a more interpretable way of demonstrating the odor interactions of binary odor mixtures.

Objective Fidelity Evaluation in Multisensory Virtual Environments: Auditory Cue Fidelity in Flight Simulation

PubMed Central

Meyer, Georg F.; Wong, Li Ting; Timson, Emma; Perfect, Philip; White, Mark D.

2012-01-01

We argue that objective fidelity evaluation of virtual environments, such as flight simulation, should be human-performance-centred and task-specific rather than measure the match between simulation and physical reality. We show how principled experimental paradigms and behavioural models to quantify human performance in simulated environments that have emerged from research in multisensory perception provide a framework for the objective evaluation of the contribution of individual cues to human performance measures of fidelity. We present three examples in a flight simulation environment as a case study: Experiment 1: Detection and categorisation of auditory and kinematic motion cues; Experiment 2: Performance evaluation in a target-tracking task; Experiment 3: Transferrable learning of auditory motion cues. We show how the contribution of individual cues to human performance can be robustly evaluated for each task and that the contribution is highly task dependent. The same auditory cues that can be discriminated and are optimally integrated in experiment 1, do not contribute to target-tracking performance in an in-flight refuelling simulation without training, experiment 2. In experiment 3, however, we demonstrate that the auditory cue leads to significant, transferrable, performance improvements with training. We conclude that objective fidelity evaluation requires a task-specific analysis of the contribution of individual cues. PMID:22957068

Changes in Brain Network Efficiency and Working Memory Performance in Aging

PubMed Central

Stanley, Matthew L.; Simpson, Sean L.; Dagenbach, Dale; Lyday, Robert G.; Burdette, Jonathan H.; Laurienti, Paul J.

2015-01-01

Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory. PMID:25875001

Changes in brain network efficiency and working memory performance in aging.

PubMed

Stanley, Matthew L; Simpson, Sean L; Dagenbach, Dale; Lyday, Robert G; Burdette, Jonathan H; Laurienti, Paul J

2015-01-01

Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory.

Modeling the behavior of Listeria monocytogenes during enrichment in half Fraser broth; impact of pooling and the duration of enrichment on the detection of L. monocytogenes in food.

PubMed

Augustin, Jean-Christophe; Kalmokoff, Martin; Ells, Timothy; Favret, Sandra; Desreumaux, Jennifer; Decourseulles Brasseur, Emilie; Gnanou Besse, Nathalie

2016-12-01

A stochastic model describing the growth of Listeria monocytogenes during enrichment in half Fraser was developed for the purpose of estimating the effects of modifications to the first enrichment step of the EN ISO 11290-1 detection method. Information pertaining to the variability of growth rates, physiological state of the cell, and the behavior of individual cells contaminating the food were obtained from previously published studies. We used this model to investigate the impact of pooling enrichment broths (wet pooling) on the performance of the standard method. For validation of the model, the numbers of L. monocytogenes occurring in 88 naturally contaminated foods following pre-enrichment were compared to model-simulated microbial counts. The model was then used to perform simulations representative of the natural contamination observed for smoked salmon in the European baseline survey of 2010-2011. The model-estimated L. monocytogenes levels following individual enrichment or following the pooling of five broths where only one would be contaminated were compared. The model indicated a 10% loss of method sensitivity resulting from wet pooling. The model also predicted a 5% decrease in the sensitivity of the method when the duration of the enrichment was reduced from 24 to 22 h. Copyright © 2016 Elsevier Ltd. All rights reserved.

Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling

NASA Astrophysics Data System (ADS)

Chen, Jie; Li, Chao; Brissette, François P.; Chen, Hua; Wang, Mingna; Essou, Gilles R. C.

2018-05-01

Bias correction is usually implemented prior to using climate model outputs for impact studies. However, bias correction methods that are commonly used treat climate variables independently and often ignore inter-variable dependencies. The effects of ignoring such dependencies on impact studies need to be investigated. This study aims to assess the impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. To this end, a joint bias correction (JBC) method which corrects the joint distribution of two variables as a whole is compared with an independent bias correction (IBC) method; this is considered in terms of correcting simulations of precipitation and temperature from 26 climate models for hydrological modeling over 12 watersheds located in various climate regimes. The results show that the simulated precipitation and temperature are considerably biased not only in the individual distributions, but also in their correlations, which in turn result in biased hydrological simulations. In addition to reducing the biases of the individual characteristics of precipitation and temperature, the JBC method can also reduce the bias in precipitation-temperature (P-T) correlations. In terms of hydrological modeling, the JBC method performs significantly better than the IBC method for 11 out of the 12 watersheds over the calibration period. For the validation period, the advantages of the JBC method are greatly reduced as the performance becomes dependent on the watershed, GCM and hydrological metric considered. For arid/tropical and snowfall-rainfall-mixed watersheds, JBC performs better than IBC. For snowfall- or rainfall-dominated watersheds, however, the two methods behave similarly, with IBC performing somewhat better than JBC. Overall, the results emphasize the advantages of correcting the P-T correlation when using climate model-simulated precipitation and temperature to assess the impact of climate change on watershed hydrology. However, a thorough validation and a comparison with other methods are recommended before using the JBC method, since it may perform worse than the IBC method for some cases due to bias nonstationarity of climate model outputs.

Social capital and sense of insecurity in the neighbourhood: a population-based multilevel analysis in Malmö, Sweden.

PubMed

Lindström, Martin; Merlo, Juan; Ostergren, Per Olof

2003-03-01

The aim of this study was to investigate the influence of social capital on self-reported sense of insecurity in the neighbourhood. The public health survey in Malmö, Sweden in 1994 was a cross-sectional study. A total of 5600 individuals aged 20-80 years were asked to answer a postal questionnaire. The participation rate was 71%. A multilevel logistic regression model, with individuals at the first level and neighbourhoods at the second, was performed. We analysed the effect (intra-area correlation, cross-level modification and odds ratios) of individual (social participation) and neighbourhood social capital (electoral participation in the 1994 municipal election) on sense of insecurity after adjustment for compositional factors. Neighbourhood factors accounted for 7.2% of the total variance in individual insecurity. This effect was marginally reduced when the individual factors were included in the model. In contrast, it was reduced by 70% by the introduction of the contextual variable. This study suggests that social capital, measured as electoral participation, may partly explain the individual's sense of insecurity in the neighbourhood.

Development and Validation of a Model to Predict Absolute Vascular Risk Reduction by Moderate-Intensity Statin Therapy in Individual Patients With Type 2 Diabetes Mellitus: The Anglo Scandinavian Cardiac Outcomes Trial, Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial, and Collaborative Atorvastatin Diabetes Study.

PubMed

Kaasenbrood, Lotte; Poulter, Neil R; Sever, Peter S; Colhoun, Helen M; Livingstone, Shona J; Boekholdt, S Matthijs; Pressel, Sara L; Davis, Barry R; van der Graaf, Yolanda; Visseren, Frank L J

2016-05-01

In this study, we aimed to translate the average relative effect of statin therapy from trial data to the individual patient with type 2 diabetes mellitus by developing and validating a model to predict individualized absolute risk reductions (ARR) of cardiovascular events. Data of 2725 patients with type 2 diabetes mellitus from the Lipid Lowering Arm of the Anglo Scandinavian Cardiac Outcomes Trial (ASCOT-LLA) study (atorvastatin 10 mg versus placebo) were used for model derivation. The model was based on 8 clinical predictors including treatment allocation (statin/placebo). Ten-year individualized ARR on major cardiovascular events by statin therapy were calculated for each patient by subtracting the estimated on-treatment risk from the estimated off-treatment risk. Predicted 10-year ARR by statin therapy was <2% for 13% of the patients. About 30% had an ARR of >4% (median ARR, 3.2%; interquartile range, 2.5%-4.3%; 95% confidence interval for 3.2% ARR, -1.4% to 6.8%). Addition of treatment interactions did not improve model performance. Therefore, the wide distribution in ARR was a consequence of the underlying distribution in cardiovascular risk enrolled in these trials. External validation of the model was performed in data from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT-LLT; pravastatin 40 mg versus usual care) and Collaborative Atorvastatin Diabetes Study (CARDS; atorvastatin 10 mg versus placebo) of 3878 and 2838 patients with type 2 diabetes mellitus, respectively. Model calibration was adequate in both external data sets, discrimination was moderate (ALLHAT-LLT: c-statistics, 0.64 [95% confidence interval, 0.61-0.67] and CARDS: 0.68 [95% confidence interval, 0.64-0.72]). ARRs of major cardiovascular events by statin therapy can be accurately estimated for individual patients with type 2 diabetes mellitus using a model based on routinely available patient characteristics. There is a wide distribution in ARR that may complement informed decision making. URL: http://www.clinicaltrials.gov. Unique identifier: NCT00327418 (CARDS) and NCT00000542 (ALLHAT). © 2016 American Heart Association, Inc.

A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance.

PubMed

Meads, Catherine; Ahmed, Ikhlaaq; Riley, Richard D

2012-04-01

A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the 'Gail 2' model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the 'Gail 2' model showed the average C statistic was 0.63 (95% CI 0.59-0.67), and the expected/observed ratio of events varied considerably across studies (95% prediction interval for E/O ratio when the model was applied in practice was 0.75-1.19). There is a need for models with better predictive performance but, given the large amount of work already conducted, further improvement of existing models based on conventional risk factors is perhaps unlikely. Research to identify new risk factors with large additionally predictive ability is therefore needed, alongside clearer reporting and continual validation of new models as they develop.

Optimal combinations of control strategies and cost-effective analysis for visceral leishmaniasis disease transmission.

PubMed

Biswas, Santanu; Subramanian, Abhishek; ELMojtaba, Ibrahim M; Chattopadhyay, Joydev; Sarkar, Ram Rup

2017-01-01

Visceral leishmaniasis (VL) is a deadly neglected tropical disease that poses a serious problem in various countries all over the world. Implementation of various intervention strategies fail in controlling the spread of this disease due to issues of parasite drug resistance and resistance of sandfly vectors to insecticide sprays. Due to this, policy makers need to develop novel strategies or resort to a combination of multiple intervention strategies to control the spread of the disease. To address this issue, we propose an extensive SIR-type model for anthroponotic visceral leishmaniasis transmission with seasonal fluctuations modeled in the form of periodic sandfly biting rate. Fitting the model for real data reported in South Sudan, we estimate the model parameters and compare the model predictions with known VL cases. Using optimal control theory, we study the effects of popular control strategies namely, drug-based treatment of symptomatic and PKDL-infected individuals, insecticide treated bednets and spray of insecticides on the dynamics of infected human and vector populations. We propose that the strategies remain ineffective in curbing the disease individually, as opposed to the use of optimal combinations of the mentioned strategies. Testing the model for different optimal combinations while considering periodic seasonal fluctuations, we find that the optimal combination of treatment of individuals and insecticide sprays perform well in controlling the disease for the time period of intervention introduced. Performing a cost-effective analysis we identify that the same strategy also proves to be efficacious and cost-effective. Finally, we suggest that our model would be helpful for policy makers to predict the best intervention strategies for specific time periods and their appropriate implementation for elimination of visceral leishmaniasis.

[Financing, organization, costs and services performance of the Argentinean health sub-systems.

PubMed

Yavich, Natalia; Báscolo, Ernesto Pablo; Haggerty, Jeannie

2016-01-01

To analyze the relationship between health system financing and services organization models with costs and health services performance in each of Rosario's health sub-systems. The financing and organization models were characterized using secondary data. Costs were calculated using the WHO/SHA methodology. Healthcare quality was measured by a household survey (n=822). Public subsystem:Vertically integrated funding and primary healthcare as a leading strategy to provide services produced low costs and individual-oriented healthcare but with weak accessibility conditions and comprehensiveness. Private subsystem: Contractual integration and weak regulatory and coordination mechanisms produced effects opposed to those of the public sub-system. Social security: Contractual integration and strong regulatory and coordination mechanisms contributed to intermediate costs and overall high performance. Each subsystem financing and services organization model had a strong and heterogeneous influence on costs and health services performance.

Unraveling mysteries of personal performance style; biomechanics of left-hand position changes (shifting) in violin performance

PubMed Central

Visentin, Peter; Li, Shiming; Tardif, Guillaume

2015-01-01

Instrumental music performance ranks among the most complex of learned human behaviors, requiring development of highly nuanced powers of sensory and neural discrimination, intricate motor skills, and adaptive abilities in a temporal activity. Teaching, learning and performing on the violin generally occur within musico-cultural parameters most often transmitted through aural traditions that include both verbal instruction and performance modeling. In most parts of the world, violin is taught in a manner virtually indistinguishable from that used 200 years ago. The current study uses methods from movement science to examine the “how” and “what” of left-hand position changes (shifting), a movement skill essential during violin performance. In doing so, it begins a discussion of artistic individualization in terms of anthropometry, the performer-instrument interface, and the strategic use of motor behaviors. Results based on 540 shifting samples, a case series of 6 professional-level violinists, showed that some elements of the skill were individualized in surprising ways while others were explainable by anthropometry, ergonomics and entrainment. Remarkably, results demonstrated each violinist to have developed an individualized pacing for shifts, a feature that should influence timing effects and prove foundational to aesthetic outcomes during performance. Such results underpin the potential for scientific methodologies to unravel mysteries of performance that are associated with a performer’s personal artistic style. PMID:26557431

Multi-objective optimization for generating a weighted multi-model ensemble

NASA Astrophysics Data System (ADS)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic ensemble mean and may provide reliable future projections.

Transformational leadership and employee safety performance: a within-person, between-jobs design.

PubMed

Inness, Michelle; Turner, Nick; Barling, Julian; Stride, Chris B

2010-07-01

We investigated the extent to which the safety performance (i.e., self-reported safety compliance and safety participation) of employees with 2 jobs was predicted by their respective supervisors' transformational leadership behaviors. We compared 2 within-person models: a context-specific model (i.e., transformational leadership experienced by employees in 1 context related to those same employees' safety performance only in that context) and a context-spillover model (i.e., transformational leadership experienced by employees in 1 context related to those same employees' safety performance in the same and other contexts). Our sample comprised 159 "moonlighters" (73 men, 86 women): employees who simultaneously hold 2 different jobs, each with a different supervisor, providing within-person data on the influence of different supervisors on employee safety performance across 2 job contexts. Having controlled for individual differences (negative affectivity and conscientiousness) and work characteristics (e.g., hours worked and length of relationship with supervisor), the context-specific model provided the best fit to the data among alternative nested models. Implications for the role of transformational leadership in promoting workplace safety are discussed.

Using a web-based, iterative education model to enhance clinical clerkships.

PubMed

Alexander, Erik K; Bloom, Nurit; Falchuk, Kenneth H; Parker, Michael

2006-10-01

Although most clinical clerkship curricula are designed to provide all students consistent exposure to defined course objectives, it is clear that individual students are diverse in their backgrounds and baseline knowledge. Ideally, the learning process should be individualized towards the strengths and weakness of each student, but, until recently, this has proved prohibitively time-consuming. The authors describe a program to develop and evaluate an iterative, Web-based educational model assessing medical students' knowledge deficits and allowing targeted teaching shortly after their identification. Beginning in 2002, a new educational model was created, validated, and applied in a prospective fashion to medical students during an internal medicine clerkship at Harvard Medical School. Using a Web-based platform, five validated questions were delivered weekly and a specific knowledge deficiency identified. Teaching targeted to the deficiency was provided to an intervention cohort of five to seven students in each clerkship, though not to controls (the remaining 7-10 students). Effectiveness of this model was assessed by performance on the following week's posttest question. Specific deficiencies were readily identified weekly using this model. Throughout the year, however, deficiencies varied unpredictably. Teaching targeted to deficiencies resulted in significantly better performance on follow-up questioning compared to the performance of those who did not receive this intervention. This model was easily applied in an additive fashion to the current curriculum, and student acceptance was high. The authors conclude that a Web-based, iterative assessment model can effectively target specific curricular needs unique to each group; focus teaching in a rapid, formative, and highly efficient manner; and may improve the efficiency of traditional clerkship teaching.

Bridging the Gap between Theory and Model: A Reflection on the Balance Scale Task.

ERIC Educational Resources Information Center

Turner, Geoffrey F. W.; Thomas, Hoben

2002-01-01

Focuses on individual strengths of articles by Jensen and van der Maas, and Halford et al., and the power of their combined perspectives. Suggests a performance model that can both evaluate specific theoretical claims and reveal important data features that had been previously obscured using conventional statistical analyses. Maintains that the…

Facilitating Social Initiations of Preschoolers with Autism Spectrum Disorders Using Video Self-Modeling

ERIC Educational Resources Information Center

Buggey, Tom; Hoomes, Grace; Sherberger, Mary Elizabeth; Williams, Sarah

2011-01-01

Video self-modeling (VSM) has accumulated a relatively impressive track record in the research literature across behaviors, ages, and types of disabilities. Using only positive imagery, VSM gives individuals the opportunity to view themselves performing a task just beyond their present functioning level via creative editing of videos using VCRs or…

A Collective Locus of Leadership: Exploring Leadership in Higher Education through a Paradigm of Collaborative Effort

ERIC Educational Resources Information Center

Harris, Kendra E.

2010-01-01

This single-case qualitative study examines leadership in an institution of higher education using the Responsible Leadership for Performance (RLP) model (Lynham & Chermack, 2006) as a framework. The study explores how using a paradigm of collective leadership as an alternative to models of individual leadership could inform understanding of…

Practical guidelines for workload assessment

NASA Technical Reports Server (NTRS)

Tattersall, Andrew J.

1994-01-01

The practical problems that might be encountered in carrying out workload evaluations in work settings have been outlined. Different approaches have been distinguished that may determine the type of research design used and provide assistance in the difficult choice between workload assessment techniques. One approach to workload assessment is to examine the short-term consequences of combining various tasks. Theoretical models of attention allocation will underpin specific studies of interference and the consequences of task demand and task conflict for performance. A further approach with a different temporal orientation may lead us to a better understanding of the relationships between work demands and strain through the analysis of individual differences in cognitive control processes. The application of these processes may depend on individual differences in long term styles and short term strategies, but may be used to prevent decrements in work performance under difficult conditions. However, control may attract costs as well as benefits in terms of changes in effective state and physiological activity. Thus, strain associated with work demands may only be measurable in the form of tradeoffs between performance and other domains of individual activity. The methodological implications are to identify patterns of adjustment to workload variations using repeated measures and longitudinal sampling of performance as well as subjective and physiological measures. Possible enhancements to workplace design must take into account these human factors considerations of workload in order to avoid potential decrements in individual performance and associated organizational problems.

Effect of Precede-Proceed Model on Preventive Behaviors for Type 2 Diabetes Mellitus in High-Risk Individuals.

PubMed

Moshki, Mahdi; Dehnoalian, Atefeh; Alami, Ali

2017-04-01

This study sought to assess the effect of precede-proceed model on preventive behaviors for type 2 diabetes mellitus (DM) in high-risk individuals. In this semi-experimental study, 164 high-risk individuals for type 2 DM were selected and were randomly divided into two groups of intervention and control ( n = 85). Educational intervention was performed as a single session face-to-face instruction for 1.5 hr for the intervention group participants. Data were collected before (baseline) and immediately and 1 month after the intervention in the two groups. The mean score of predisposing (knowledge) factors ( p = .001), reinforcing factors ( p = .001), and enabling factors ( p = .02) were significantly different at baseline and 1 month after the intervention in the intervention group compared with the control group ( p < .05). A significant improvement occurred in the nutritional habits of high-risk participants in the intervention group at 1 month after the intervention compared with controls ( p = .001). The precede-proceed model can be effective for promoting the preventive behaviors for type 2 DM in high-risk individuals.

Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability.

PubMed

Beda, Alessandro; Simpson, David M; Faes, Luca

2017-01-01

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits' width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications in biomedical signal processing and beyond, providing a powerful new tool for assessing the confidence limits of indexes estimated from individual recordings.