WorldWideScience

Sample records for modeling work predicts

  1. Required Collaborative Work in Online Courses: A Predictive Modeling Approach

    Science.gov (United States)

    Smith, Marlene A.; Kellogg, Deborah L.

    2015-01-01

    This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…

  2. Predicting Sustainable Work Behavior

    DEFF Research Database (Denmark)

    Hald, Kim Sundtoft

    2013-01-01

    Sustainable work behavior is an important issue for operations managers – it has implications for most outcomes of OM. This research explores the antecedents of sustainable work behavior. It revisits and extends the sociotechnical model developed by Brown et al. (2000) on predicting safe behavior....... Employee characteristics and general attitudes towards safety and work condition are included in the extended model. A survey was handed out to 654 employees in Chinese factories. This research contributes by demonstrating how employee- characteristics and general attitudes towards safety and work...... condition influence their sustainable work behavior. A new definition of sustainable work behavior is proposed....

  3. Prediction of Critical Power and W′ in Hypoxia: Application to Work-Balance Modelling

    Science.gov (United States)

    Townsend, Nathan E.; Nichols, David S.; Skiba, Philip F.; Racinais, Sebastien; Périard, Julien D.

    2017-01-01

    Purpose: Develop a prediction equation for critical power (CP) and work above CP (W′) in hypoxia for use in the work-balance (WBAL′) model. Methods: Nine trained male cyclists completed cycling time trials (TT; 12, 7, and 3 min) to determine CP and W′ at five altitudes (250, 1,250, 2,250, 3,250, and 4,250 m). Least squares regression was used to predict CP and W′ at altitude. A high-intensity intermittent test (HIIT) was performed at 250 and 2,250 m. Actual and predicted CP and W′ were used to compute W′ during HIIT using differential (WBALdiff′) and integral (WBALint′) forms of the WBAL′ model. Results: CP decreased at altitude (P hypoxia. This enables the application of WBAL′ modelling to training prescription and competition analysis at altitude. PMID:28386237

  4. Predicting Freeway Work Zone Delays and Costs with a Hybrid Machine-Learning Model

    Directory of Open Access Journals (Sweden)

    Bo Du

    2017-01-01

    Full Text Available A hybrid machine-learning model, integrating an artificial neural network (ANN and a support vector machine (SVM model, is developed to predict spatiotemporal delays, subject to road geometry, number of lane closures, and work zone duration in different periods of a day and in the days of a week. The model is very user friendly, allowing the least inputs from the users. With that the delays caused by a work zone on any location of a New Jersey freeway can be predicted. To this end, tremendous amounts of data from different sources were collected to establish the relationship between the model inputs and outputs. A comparative analysis was conducted, and results indicate that the proposed model outperforms others in terms of the least root mean square error (RMSE. The proposed hybrid model can be used to calculate contractor penalty in terms of cost overruns as well as incentive reward schedule in case of early work competition. Additionally, it can assist work zone planners in determining the best start and end times of a work zone for developing and evaluating traffic mitigation and management plans.

  5. Comprehensive contour prediction model of work rolls in hot wide strip mill

    Institute of Scientific and Technical Information of China (English)

    Xiaodong Wang; Quan Yang; Anrui He; Renzhong Wang

    2007-01-01

    The predictive calculation of comprehensive contour of work rolls in the on-line strip shape control model during hot rolling consists of two important parts of wear contour calculation and thermal contour calculation, which have a direct influence on the accuracy of shape control. A statistical wear model and a finite difference thermal contour model of work rolls were described. The comprehensive contour is the equivalence treatment of the sum of grinding, wear, and thermal contours. This comprehensive contour calculation model has been applied successfully in the real on-line strip shape control model. Its high precision has been proved through the large amounts of actual roll profile measurements and theoretical analyses. The hit rates (percent of shape index satisfying requirement) of crown and head flatness of the strips rolled, by using the shape control model, which includes the comprehensive contour calculation model, have about 16% and 10% increase respectively, compared to those of strips rolled by using manual operation.

  6. Prediction of Critical Power and W' in Hypoxia: Application to Work-Balance Modelling.

    Science.gov (United States)

    Townsend, Nathan E; Nichols, David S; Skiba, Philip F; Racinais, Sebastien; Périard, Julien D

    2017-01-01

    Purpose: Develop a prediction equation for critical power (CP) and work above CP (W') in hypoxia for use in the work-balance ([Formula: see text]) model. Methods: Nine trained male cyclists completed cycling time trials (TT; 12, 7, and 3 min) to determine CP and W' at five altitudes (250, 1,250, 2,250, 3,250, and 4,250 m). Least squares regression was used to predict CP and W' at altitude. A high-intensity intermittent test (HIIT) was performed at 250 and 2,250 m. Actual and predicted CP and W' were used to compute W' during HIIT using differential ([Formula: see text]) and integral ([Formula: see text]) forms of the [Formula: see text] model. Results: CP decreased at altitude (P CP and W') on modelled [Formula: see text] at 2,250 m (P = 0.24). [Formula: see text] returned higher values than [Formula: see text] throughout HIIT (P CP and W' developed in this study are suitable for use with the [Formula: see text] model in acute hypoxia. This enables the application of [Formula: see text] modelling to training prescription and competition analysis at altitude.

  7. Work roll thermal contour prediction model of nonoriented electrical steel sheets in hot strip mills

    Institute of Scientific and Technical Information of China (English)

    Ningtao Zhao; Jianguo Cao; Jie Zhang; Yi Su; Tanli Yan; Kefeng Rao

    2008-01-01

    The demands for profile and flatness of nonoriented electrical steels are becoming more and more severe. The temperature field and thermal contour of work rolls are the key factors that affect the profile and flatness control in the finishing trains of the hot rolling. A theoretic mathematical model was built by a two-dimensional finite difference to calculate the temperature field and thermal contour at any time within the entire rolling campaign in the hot rolling process. To improve the calculating speed and precision,some special solutions were introduced, including the development of this model, the simplification of boundary conditions, the computation of heat transfer coefficient, and the narrower mesh along the edge of the strip. The effects of rolling pace and work roll shifting on the temperature field and thermal contour of work rolls in the hot rolling process were demonstrated. The calculated results of the prediction model are in good agreement with the measured ones and can be applied to guiding profde and flatness control of nonoriented electrical steel sheets in hot strip mills.

  8. Verification of a Predictive Model of Psychological Health at Work in Canada and France

    Directory of Open Access Journals (Sweden)

    Jean-Sébastien Boudrias

    2014-01-01

    Full Text Available The purpose of this study was to test the invariance of a predictive model of psychological health at work (PHW in Canada and France. The model a defines PHW as an integrative second-order variable (low distress, high well-being and b includes three categories of PHW inductors (job demands, personal resources and social-organizational resources and one psychological intermediate variable (needs satisfaction that were found to be directly or indirectly related to PHW in a previous study on a sample of French teachers (Boudrias, Desrumaux, Gaudreau, Nelson, Savoie and Brunet, 2011. To test if this model is invariant across countries, these data from French teachers ('N' = 391 were reanalyzed and compared with data from a sample of Canadian teachers ('N' = 480 who completed the same set of questionnaires. Results from structural equation modeling analyses indicated that the model is completely invariant across the two samples. Therefore, pathways to PHW appeared to generalize across these samples of teachers without the addition of other cultural variables. This PHW model suggests that personal resources exert considerable influence directly and indirectly on psychological health through multiple mediators. Research implications and study limitations are discussed.

  9. Serotonergic modulation of spatial working memory: predictions from a computational network model

    Directory of Open Access Journals (Sweden)

    Maria eCano-Colino

    2013-09-01

    Full Text Available Serotonin (5-HT receptors of types 1A and 2A are massively expressed in prefrontal cortex (PFC neurons, an area associated with cognitive function. Hence, 5-HT could be effective in modulating prefrontal-dependent cognitive functions, such as spatial working memory (SWM. However, a direct association between 5-HT and SWM has proved elusive in psycho-pharmacological studies. Recently, a computational network model of the PFC microcircuit was used to explore the relationship between 5‑HT and SWM (Cano-Colino et al. 2013. This study found that both excessive and insufficient 5-HT levels lead to impaired SWM performance in the network, and it concluded that analyzing behavioral responses based on confidence reports could facilitate the experimental identification of SWM behavioral effects of 5‑HT neuromodulation. Such analyses may have confounds based on our limited understanding of metacognitive processes. Here, we extend these results by deriving three additional predictions from the model that do not rely on confidence reports. Firstly, only excessive levels of 5-HT should result in SWM deficits that increase with delay duration. Secondly, excessive 5-HT baseline concentration makes the network vulnerable to distractors at distances that were robust to distraction in control conditions, while the network still ignores distractors efficiently for low 5‑HT levels that impair SWM. Finally, 5-HT modulates neuronal memory fields in neurophysiological experiments: Neurons should be better tuned to the cued stimulus than to the behavioral report for excessive 5-HT levels, while the reverse should happen for low 5-HT concentrations. In all our simulations agonists of 5-HT1A receptors and antagonists of 5-HT2A receptors produced behavioral and physiological effects in line with global 5-HT level increases. Our model makes specific predictions to be tested experimentally and advance our understanding of the neural basis of SWM and its neuromodulation

  10. Natural selection at work: an accelerated evolutionary computing approach to predictive model selection

    Directory of Open Access Journals (Sweden)

    Olcay Akman

    2010-07-01

    Full Text Available We implement genetic algorithm based predictive model building as an alternative to the traditional stepwise regression. We then employ the Information Complexity Measure (ICOMP as a measure of model fitness instead of the commonly used measure of R-square. Furthermore, we propose some modifications to the genetic algorithm to increase the overall efficiency.

  11. Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

    NARCIS (Netherlands)

    Roelen, Corne A. M.; Stapelfeldt, Christina M.; Heymans, Martijn W.; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V.; Bultmann, Ute; Jensen, Chris

    2015-01-01

    Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. Methods 2,562 municipal eldercare w

  12. Predicting medical specialists' working (long) hours: Testing a contemporary career model

    NARCIS (Netherlands)

    Pas, B.R.; Eisinga, R.N.; Doorewaard, J.A.C.M.

    2016-01-01

    With the feminization (in numbers) of several professions, changing gender role prescriptions regarding parenthood and an increased attention for work-life balance, career theorists recently addressed the need for a more contemporary career model taking a work-home perspective. In this study, we tes

  13. Predicting medical specialists' working (long) hours: Testing a contemporary career model

    NARCIS (Netherlands)

    Pas, B.R.; Eisinga, R.N.; Doorewaard, J.A.C.M.

    2016-01-01

    With the feminization (in numbers) of several professions, changing gender role prescriptions regarding parenthood and an increased attention for work-life balance, career theorists recently addressed the need for a more contemporary career model taking a work-home perspective. In this study, we

  14. An FE Based On-line Model for the Prediction of Work Roll Thermal Profile in Hot Strip Rolling

    Science.gov (United States)

    Choi, Ji Won; Lee, Jung Hyeung; Sun, Cheng Gang; Hwang, Sang Moo

    2010-06-01

    Prediction and control of the thermal deformation of the work roll is vital for enhancing product quality in hot strip and plate rolling. In this paper, we present an on-line model for the prediction of the work roll thermal profile. The model is developed on the basis of an integrated finite element model for the coupled analysis of heat transfer and deformation occurring at the bite zone, to rigorously take into account the effect of various rolling parameters on the thermal behavior of the work roll. The validity of the model is demonstrated through comparison with measurements made in an industrial hot strip mill. Also, an emphasis is given to the examination the effect of some selected rolling parameters in an actual production environment.

  15. Predicting medical specialists’ working (long) hours: testing a contemporary career model

    NARCIS (Netherlands)

    Pas, B.R.; Eisinga, R.; Doorewaard, J.A.C.M.

    2016-01-01

    With the feminization (in numbers) of several professions, changing gender role prescriptions regarding parenthood and an increased attention for work-life balance, career theorists recently addressed the need for a more contemporary career model taking a work–home perspective. In this study, we tes

  16. Predicting Preschoolers' Attachment Security from Fathers' Involvement, Internal Working Models, and Use of Social Support

    Science.gov (United States)

    Newland, Lisa A.; Coyl, Diana D.; Freeman, Harry

    2008-01-01

    Associations between preschoolers' attachment security, fathers' involvement (i.e. parenting behaviors and consistency) and fathering context (i.e. fathers' internal working models (IWMs) and use of social support) were examined in a subsample of 102 fathers, taken from a larger sample of 235 culturally diverse US families. The authors predicted…

  17. Predicting medical specialists’ working (long) hours: Testing a contemporary career model

    NARCIS (Netherlands)

    Pas, B.R.; Eisinga, R.; Doorewaard, J.A.C.M.

    2016-01-01

    With the feminization (in numbers) of several professions, changing gender role prescriptions regarding parenthood and an increased attention for work-life balance, career theorists recently addressed the need for a more contemporary career model taking a work–home perspective. In this study, we

  18. Medication Reconciliation: Work Domain Ontology, prototype development, and a predictive model.

    Science.gov (United States)

    Markowitz, Eliz; Bernstam, Elmer V; Herskovic, Jorge; Zhang, Jiajie; Shneiderman, Ben; Plaisant, Catherine; Johnson, Todd R

    2011-01-01

    Medication errors can result from administration inaccuracies at any point of care and are a major cause for concern. To develop a successful Medication Reconciliation (MR) tool, we believe it necessary to build a Work Domain Ontology (WDO) for the MR process. A WDO defines the explicit, abstract, implementation-independent description of the task by separating the task from work context, application technology, and cognitive architecture. We developed a prototype based upon the WDO and designed to adhere to standard principles of interface design. The prototype was compared to Legacy Health System's and Pre-Admission Medication List Builder MR tools via a Keystroke-Level Model analysis for three MR tasks. The analysis found the prototype requires the fewest mental operations, completes tasks in the fewest steps, and completes tasks in the least amount of time. Accordingly, we believe that developing a MR tool, based upon the WDO and user interface guidelines, improves user efficiency and reduces cognitive load.

  19. Working postures: prediction and evaluation

    NARCIS (Netherlands)

    Delleman, N.J.

    1999-01-01

    To date, workstation designers cannot see the effects of a design on working posture before a mock-up/prototype is available. At that moment, usually the margin for creating the conditions required for adopting favourable working postures is still very limited. Posture prediction at an early design

  20. Comparing different approach and avoidance models of learning and personality in the prediction of work, university, and leadership outcomes.

    Science.gov (United States)

    Jackson, Chris J; Hobman, Elizabeth V; Jimmieson, Nerina L; Martin, Robin

    2009-05-01

    Jackson (2005) developed a hybrid model of personality and learning, known as the learning styles profiler (LSP) which was designed to span biological, socio-cognitive, and experiential research foci of personality and learning research. The hybrid model argues that functional and dysfunctional learning outcomes can be best understood in terms of how cognitions and experiences control, discipline, and re-express the biologically based scale of sensation-seeking. In two studies with part-time workers undertaking tertiary education (N = 137 and 58), established models of approach and avoidance from each of the three different research foci were compared with Jackson's hybrid model in their predictiveness of leadership, work, and university outcomes using self-report and supervisor ratings. Results showed that the hybrid model was generally optimal and, as hypothesized, that goal orientation was a mediator of sensation-seeking on outcomes (work performance, university performance, leader behaviours, and counterproductive work behaviour). Our studies suggest that the hybrid model has considerable promise as a predictor of work and educational outcomes as well as dysfunctional outcomes.

  1. Development of a risk prediction model for incident hypertension in a working-age Japanese male population.

    Science.gov (United States)

    Otsuka, Toshiaki; Kachi, Yuko; Takada, Hirotaka; Kato, Katsuhito; Kodani, Eitaro; Ibuki, Chikao; Kusama, Yoshiki; Kawada, Tomoyuki

    2015-06-01

    The aim of this study was to develop a risk prediction model for incident hypertension in a Japanese male population. Study participants included 15,025 nonhypertensive Japanese male workers (mean age, 38.8±8.9 years) who underwent an annual medical checkup at a company. The participants were followed-up for a median of 4.0 years to determine new-onset hypertension, defined as a systolic blood pressure (BP) ⩾140 mm Hg, a diastolic BP ⩾90 mm Hg, or the initiation of antihypertensive medication. Participants were divided into the following two cohorts for subsequent analyses: the derivation cohort (n=12,020, 80% of the study population) and the validation cohort (n=3005, the remaining 20% of the study population). In the derivation cohort, a multivariate Cox proportional hazards model demonstrated that age, body mass index, systolic and diastolic BP, current smoking status, excessive alcohol intake and parental history of hypertension were independent predictors of incident hypertension. Using these variables, a risk prediction model was constructed to estimate the 4-year risk of incident hypertension. In the validation cohort, the risk prediction model demonstrated high discrimination ability and acceptable calibration, with a C-statistic of 0.861 (95% confidence interval 0.844, 0.877) and a modified Hosmer-Lemeshow χ2 statistic of 15.2 (P=0.085). A risk score sheet was constructed to enable the simple calculation of the approximate 4-year probability of incident hypertension. In conclusion, a practical risk prediction model for incident hypertension was successfully developed in a working-age Japanese male population.

  2. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

  3. Redesigning the work of case management: testing a predictive model for readmission.

    Science.gov (United States)

    Gilbert, Penny; Rutland, Michael D; Brockopp, Dorothy

    2013-11-01

    The rising cost of healthcare along with pay-for-performance and bundled-payment initiatives have affirmed the importance of case management in today's healthcare market. Case managers have historically functioned as gatekeepers regarding patient length of stay (LOS) and cost per case. While LOS and cost of care remain important components of the case manager's responsibilities, at present they have evolved to a much broader role that includes prevention of readmissions and successful transition through the continuum of care. Medicare beneficiaries readmitted to the hospital within 30 days of discharge are thought to cost the healthcare system $17.4 billion annually. In today's hospitals, case managers are being asked to address this issue with systems and processes developed only as discharge facilitation models. Case managers at one acute care organization recognized the need to move beyond the traditional case management roles and activities related to discharge planning, utilization review, and LOS management. Effective transition from hospital to home or supportive agency is a major component of this case management model.

  4. Landscaping analyses of the ROC predictions of discrete-slots and signal-detection models of visual working memory.

    Science.gov (United States)

    Donkin, Chris; Tran, Sophia Chi; Nosofsky, Robert

    2014-10-01

    A fundamental issue concerning visual working memory is whether its capacity limits are better characterized in terms of a limited number of discrete slots (DSs) or a limited amount of a shared continuous resource. Rouder et al. (2008) found that a mixed-attention, fixed-capacity, DS model provided the best explanation of behavior in a change detection task, outperforming alternative continuous signal detection theory (SDT) models. Here, we extend their analysis in two ways: first, with experiments aimed at better distinguishing between the predictions of the DS and SDT models, and second, using a model-based analysis technique called landscaping, in which the functional-form complexity of the models is taken into account. We find that the balance of evidence supports a DS account of behavior in change detection tasks but that the SDT model is best when the visual displays always consist of the same number of items. In our General Discussion section, we outline, but ultimately reject, a number of potential explanations for the observed pattern of results. We finish by describing future research that is needed to pinpoint the basis for this observed pattern of results.

  5. Predictive Models of Work-Related Musculoskeletal Disorders (WMSDs Among Sewing Machine Operators in the Garments Industry

    Directory of Open Access Journals (Sweden)

    Carlos Ignacio P. Lugay

    2015-02-01

    Full Text Available The Philippine garments industry has been a driving force in the country’s economy, with apparel manufacturing firms catering to the local and global markets and providing employment opportunities for skilled Filipinos. Tight competition from neighboring Asian countries however, has made the industry’s situation difficult to flourish, especially in the wake of the Association of Southeast Asian Nations (ASEAN 2015 Integration. To assist the industry, this research examined one of the more common problems among sewing machine operators, termed as Work-related Musculoskeletal Disorders (WMSDs. These disorders are reflective in the frequency and severity of the pain experienced by the sewers while accomplishing their tasks. The causes of these disorders were identified and were correlated with the frequency and severity of pain in various body areas of the operator. To forecast pain from WMSDs among the operators, mathematical models were developed to predict the combined frequency and severity of the pain from WMSDs. Loss time or “unofficial breaktimes” due to pain from WMSDs was likewise forecasted to determine its effects on the firm’s production capacity. Both these predictive models were developed in order to assist garment companies in anticipating better the effects of WMSDs and loss time in their operations. Moreover, ergonomic interventions were suggested to minimize pain from WMSDs, with the expectation of increased productivity of the operators and improved quality of their outputs.

  6. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...

  7. The Prediction of Consumer Buying Intentions: A Comparative Study of the Predictive Efficacy of Two Attitudinal Models. Faculty Working Paper No. 234.

    Science.gov (United States)

    Bhagat, Rabi S.; And Others

    The role of attitudes in the conduct of buyer behavior is examined in the context of two competitive models of attitude structure and attitude-behavior relationship. Specifically, the objectives of the study were to compare the Fishbein and Sheth models on the criteria of predictive as well as cross validities. Data on both the models were…

  8. An Assessment of the Model of Concentration Addition for Predicting the Estrogenic Activity of Chemical Mixtures in Wastewater Treatment Works Effluents

    OpenAIRE

    Thorpe, Karen L.; Gross-Sorokin, Melanie; Johnson, Ian; Brighty, Geoff; Tyler, Charles R.

    2005-01-01

    The effects of simple mixtures of chemicals, with similar mechanisms of action, can be predicted using the concentration addition model (CA). The ability of this model to predict the estrogenic effects of more complex mixtures such as effluent discharges, however, has yet to be established. Effluents from 43 U.K. wastewater treatment works were analyzed for the presence of the principal estrogenic chemical contaminants, estradiol, estrone, ethinylestradiol, and nonylphenol. The measured conce...

  9. Working memory load strengthens reward prediction errors.

    Science.gov (United States)

    Collins, Anne G E; Ciullo, Brittany; Frank, Michael J; Badre, David

    2017-03-20

    Reinforcement learning in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we asked how working memory and incremental reinforcement learning processes interact to guide human learning. Working memory load was manipulated by varying the number of stimuli to be learned across blocks. Behavioral results and computational modeling confirmed that learning was best explained as a mixture of two mechanisms: a fast, capacity-limited, and delay-sensitive working memory process together with slower reinforcement learning. Model-based analysis of fMRI data showed that striatum and lateral prefrontal cortex were sensitive to reward prediction error, as shown previously, but critically, these signals were reduced when the learning problem was within capacity of working memory. The degree of this neural interaction related to individual differences in the use of working memory to guide behavioral learning. These results indicate that the two systems do not process information independently, but rather interact during learning.SIGNIFICANCE STATEMENTReinforcement learning theory has been remarkably productive at improving our understanding of instrumental learning as well as dopaminergic and striatal network function across many mammalian species. However, this neural network is only one contributor to human learning, and other mechanisms such as prefrontal cortex working memory, also play a key role. Our results show in addition that these other players interact with the dopaminergic RL system, interfering with its key computation of reward predictions errors.

  10. Objectively-Measured Impulsivity and Attention-Deficit/Hyperactivity Disorder (ADHD): Testing Competing Predictions from the Working Memory and Behavioral Inhibition Models of ADHD

    Science.gov (United States)

    Raiker, Joseph S.; Rapport, Mark D.; Kofler, Michael J.; Sarver, Dustin E.

    2012-01-01

    Impulsivity is a hallmark of two of the three DSM-IV ADHD subtypes and is associated with myriad adverse outcomes. Limited research, however, is available concerning the mechanisms and processes that contribute to impulsive responding by children with ADHD. The current study tested predictions from two competing models of ADHD--working memory (WM)…

  11. CVC工作辊非对称磨损分析与预报模型建立%Asymmetric wear and wear prediction model of CVC work roll

    Institute of Scientific and Technical Information of China (English)

    2016-01-01

    The CVC work roll is widely used because of its strong crown control ability in hot strip rolling. However, CVC work roll can't uniform the roll wear. So the CVC work roll wear is serious and its wear form is asymmetric. The characterization method of asymmetric wear for CVC work roll is proposed in the current study. With this method,the downstream CVC work roll wears of the 1800 mm CSP production line are analyzed. It is found that the CVC work roll wears of downstream stands are always asymmetric and there is a relationship between the CVC work roll contour and the CVC work roll wear contour. Based on the analysis,the influence coefficient of roll diameter for the CVC work roll wear and the influence coefficient of roll diameter for the rolling pressure are proposed. The asymmetric wear prediction model for CVC work roll is built,the parameters of the model are optimized by using genetic algorithm. The asymmet-ric wear prediction model has been verified with measured data,the accuracy of the improved wear prediction model is improved by about 35%compared with the conventional wear prediction model.%CVC辊形以其较强的凸度控制能力在热连轧中有着广泛的应用,但是CVC辊形不具有均匀磨损的能力,其磨损往往比较严重,且呈现出非对称性.针对该特点提出CVC工作辊非对称磨损的表征方法,利用该方法对某1800 mm CSP生产线下游机架CVC工作辊非对称磨损情况进行分析.统计结果表明,下游机架工作辊磨损多为非对称形式,并与CVC辊形呈现出一定的对应性.在此基础上,提出辊径对整体磨损影响系数及辊径对轧制力影响系数2个新的磨损模型参数,并建立针对CVC轧辊的非对称磨损预报模型,利用遗传算法对模型参数进行优化,并利用实测数据进行验证,改进后磨损模型精度比常规磨损模型精度平均提高了约35%.

  12. 148 Predictive Model for Return to Work After Elective Surgery for Lumbar Degenerative Disease: An Analysis From National Neurosurgery Quality Outcomes Database Registry.

    Science.gov (United States)

    Asher, Anthony L; Chotai, Silky; Devin, Clinton J; Archer-Swygert, Kristen; Parker, Scott L; Bydon, Mohamad; Hui, Nian; Harrell, Frank; Speroff, Theodore; Dittus, Robert; Philips, Sharon; Shaffrey, Christopher I; Foley, Kevin T; McGirt, Matthew J

    2016-08-01

    The current costs associated with spine care are unsustainable. The productivity loss and time away from work in gainfully employed patients contributes greatly to the financial burden. Therefore, it is vital to identify the factors associated with returning to work after lumbar spine surgery. We present a predictive model of ability to return to work (RTW) after lumbar spine surgery for degenerative spine disease. Total 4694 patients undergoing elective spine surgery for degenerative lumbar disease who were employed were entered into a prospective multicenter registry (N2QOD). Baseline and 3-month postoperative patient-reported outcomes: Oswestry Disability Index (ODI), EQ-5D, NRS back and leg pain were recorded. The time to RTW was defined as the period between operation time and date of returning to work. A multivariable Cox proportional hazards regression model, including an array of preoperative factors, was fitted for RTW. The model performance was measured by the c-index. Eighty-two percent of patients (n = 3855) returned to work within 3 -months postoperatively. The risk-adjusted predictors of lower likelihood of RTW were preoperatively employed but not working at the time of presentation, those occupied with manual labor, on worker's compensation, on liability insurance, baseline ODI and NRS-BP scores, female sex, African American race, history of diabetes mellitus, and higher ASA grades. The likelihood of RTW within 3 months was higher in patients with higher education level compared with those with less than high school level education. The c-index of our model performance was 0.71. We present a novel predictive model for probability of RTW after lumbar spine surgery. Spine care providers can use this model to educate patients and encourage them in shared decision making regarding the RTW outcome. This will result in better communication between patients and clinicians and improve recovery expectations, which will ultimately increase the likelihood of a

  13. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  14. Working memory management and predicted utility.

    Science.gov (United States)

    Chatham, Christopher H; Badre, David

    2013-01-01

    Given the limited capacity of working memory (WM), its resources should be allocated strategically. One strategy is filtering, whereby access to WM is granted preferentially to items with the greatest utility. However, reallocation of WM resources might be required if the utility of maintained information subsequently declines. Here, we present behavioral, computational, and neuroimaging evidence that human participants track changes in the predicted utility of information in WM. First, participants demonstrated behavioral costs when the utility of items already maintained in WM declined and resources should be reallocated. An adapted Q-learning model indicated that these costs scaled with the historical utility of individual items. Finally, model-based neuroimaging demonstrated that frontal cortex tracked the utility of items to be maintained in WM, whereas ventral striatum tracked changes in the utility of items maintained in WM to the degree that these items are no longer useful. Our findings suggest that frontostriatal mechanisms track the utility of information in WM, and that these dynamics may predict delays in the removal of information from WM.

  15. Predicting Satisfaction with Group Work Assignments

    Science.gov (United States)

    Burdett, Jane; Hastie, Brianne

    2009-01-01

    Universities are increasingly using group based assessment tasks; however, as with work-place teams, such tasks often elicit mixed feelings from participants. This study investigated factors that may predict student satisfaction with group work at university. Final-year business students completed a questionnaire addressing experiences of group…

  16. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...

  17. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  18. An analysis from the Quality Outcomes Database, Part 2. Predictive model for return to work after elective surgery for lumbar degenerative disease.

    Science.gov (United States)

    Asher, Anthony L; Devin, Clinton J; Archer, Kristin R; Chotai, Silky; Parker, Scott L; Bydon, Mohamad; Nian, Hui; Harrell, Frank E; Speroff, Theodore; Dittus, Robert S; Philips, Sharon E; Shaffrey, Christopher I; Foley, Kevin T; McGirt, Matthew J

    2017-10-01

    OBJECTIVE Current costs associated with spine care are unsustainable. Productivity loss and time away from work for patients who were once gainfully employed contributes greatly to the financial burden experienced by individuals and, more broadly, society. Therefore, it is vital to identify the factors associated with return to work (RTW) after lumbar spine surgery. In this analysis, the authors used data from a national prospective outcomes registry to create a predictive model of patients' ability to RTW after undergoing lumbar spine surgery for degenerative spine disease. METHODS Data from 4694 patients who underwent elective spine surgery for degenerative lumbar disease, who had been employed preoperatively, and who had completed a 3-month follow-up evaluation, were entered into a prospective, multicenter registry. Patient-reported outcomes-Oswestry Disability Index (ODI), numeric rating scale (NRS) for back pain (BP) and leg pain (LP), and EQ-5D scores-were recorded at baseline and at 3 months postoperatively. The time to RTW was defined as the period between operation and date of returning to work. A multivariable Cox proportional hazards regression model, including an array of preoperative factors, was fitted for RTW. The model performance was measured using the concordance index (c-index). RESULTS Eighty-two percent of patients (n = 3855) returned to work within 3 months postoperatively. The risk-adjusted predictors of a lower likelihood of RTW were being preoperatively employed but not working at the time of presentation, manual labor as an occupation, worker's compensation, liability insurance for disability, higher preoperative ODI score, higher preoperative NRS-BP score, and demographic factors such as female sex, African American race, history of diabetes, and higher American Society of Anesthesiologists score. The likelihood of a RTW within 3 months was higher in patients with higher education level than in those with less than high school

  19. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production......The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... and HIRLAM predictions. The statistical models belong to the class of conditional parametric models. The models are estimated using local polynomial regression, but the estimation method is here extended to be adaptive in order to allow for slow changes in the system e.g. caused by the annual variations...

  20. Armodafinil and modafinil in patients with excessive sleepiness associated with shift work disorder: a pharmacokinetic/pharmacodynamic model for predicting and comparing their concentration-effect relationships.

    Science.gov (United States)

    Darwish, Mona; Bond, Mary; Ezzet, Farkad

    2012-09-01

    Armodafinil, the longer lasting R-isomer of racemic modafinil, improves wakefulness in patients with excessive sleepiness associated with shift work disorder (SWD). Pharmacokinetic studies suggest that armodafinil achieves higher plasma concentrations than modafinil late in a dose interval following equal oral doses. Pooled Multiple Sleep Latency Test (MSLT) data from 2 randomized, double-blind, placebo-controlled trials in 463 patients with SWD, 1 with armodafinil 150 mg/d and 1 with modafinil 200 mg/d (both administered around 2200 h before night shifts), were used to build a pharmacokinetic/pharmacodynamic model. Predicted plasma drug concentrations were obtained by developing and applying a population pharmacokinetic model using nonlinear mixed-effects modeling. Armodafinil 200 mg produced a plasma concentration above the EC(50) (4.6 µg/mL) for 9 hours, whereas modafinil 200 mg did not exceed the EC(50). Consequently, armodafinil produced greater increases in predicted placebo-subtracted MSLT times of 0.5-1 minute (up to 10 hours after dosing) compared with modafinil. On a milligram-to-milligram basis, armodafinil 200 mg consistently increased wakefulness more than modafinil 200 mg, including times late in the 8-hour shift.

  1. An assessment of the model of concentration addition for predicting the estrogenic activity of chemical mixtures in wastewater treatment works effluents.

    Science.gov (United States)

    Thorpe, Karen L; Gross-Sorokin, Melanie; Johnson, Ian; Brighty, Geoff; Tyler, Charles R

    2006-04-01

    The effects of simple mixtures of chemicals, with similar mechanisms of action, can be predicted using the concentration addition model (CA). The ability of this model to predict the estrogenic effects of more complex mixtures such as effluent discharges, however, has yet to be established. Effluents from 43 U.K. wastewater treatment works were analyzed for the presence of the principal estrogenic chemical contaminants, estradiol, estrone, ethinylestradiol, and nonylphenol. The measured concentrations were used to predict the estrogenic activity of each effluent, employing the model of CA, based on the relative potencies of the individual chemicals in an in vitro recombinant yeast estrogen screen (rYES) and a short-term (14-day) in vivo rainbow trout vitellogenin induction assay. Based on the measured concentrations of the four chemicals in the effluents and their relative potencies in each assay, the calculated in vitro and in vivo responses compared well and ranged between 3.5 and 87 ng/L of estradiol equivalents (E2 EQ) for the different effluents. In the rYES, however, the measured E2 EQ concentrations in the effluents ranged between 0.65 and 43 ng E2 EQ/L, and they varied against those predicted by the CA model. Deviations in the estimation of the estrogenic potency of the effluents by the CA model, compared with the measured responses in the rYES, are likely to have resulted from inaccuracies associated with the measurement of the chemicals in the extracts derived from the complex effluents. Such deviations could also result as a consequence of interactions between chemicals present in the extracts that disrupted the activation of the estrogen response elements in the rYES. E2 EQ concentrations derived from the vitellogenic response in fathead minnows exposed to a series of effluent dilutions were highly comparable with the E2 EQ concentrations derived from assessments of the estrogenic potency of these dilutions in the rYES. Together these data support the

  2. Predictive models in urology.

    Science.gov (United States)

    Cestari, Andrea

    2013-01-01

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

  3. The Level of Quality of Work Life to Predict Work Alienation

    Science.gov (United States)

    Erdem, Mustafa

    2014-01-01

    The current research aims to determine the level of elementary school teachers' quality of work life (QWL) to predict work alienation. The study was designed using the relational survey model. The research population consisted of 1096 teachers employed at 25 elementary schools within the city of Van in the academic year 2010- 2011, and 346…

  4. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... paper, we will present an introduction to the theory and application of MPC with Matlab codes written to ... model predictive control, linear systems, discrete-time systems, ... and then compute very rapidly for this open-loop con-.

  5. Nominal model predictive control

    OpenAIRE

    Grüne, Lars

    2013-01-01

    5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...

  6. Nominal Model Predictive Control

    OpenAIRE

    Grüne, Lars

    2014-01-01

    5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...

  7. Melanoma risk prediction models

    Directory of Open Access Journals (Sweden)

    Nikolić Jelena

    2014-01-01

    Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were

  8. Predicting work Performance through selection interview ratings and Psychological assessment

    Directory of Open Access Journals (Sweden)

    Liziwe Nzama

    2008-12-01

    Full Text Available The aim of the study was to establish whether selection interviews used in conjunction with psychological assessments of personality traits and cognitive functioning contribute to predicting work performance. The sample consisted of 102 managers who were appointed recently in a retail organisation. The independent variables were selection interview ratings obtained on the basis of structured competency-based interview schedules by interviewing panels, fve broad dimensions of personality defned by the Five Factor Model as measured by the 15 Factor Questionnaire (15FQ+, and cognitive processing variables (current level of work, potential level of work, and 12 processing competencies measured by the Cognitive Process Profle (CPP. Work performance was measured through annual performance ratings that focused on measurable outputs of performance objectives. Only two predictor variables correlated statistically signifcantly with the criterion variable, namely interview ratings (r = 0.31 and CPP Verbal Abstraction (r = 0.34. Following multiple regression, only these variables contributed signifcantly to predicting work performance, but only 17.8% of the variance of the criterion was accounted for.

  9. Predicting flow at work: investigating the activities and job characteristics that predict flow states at work.

    Science.gov (United States)

    Nielsen, Karina; Cleal, Bryan

    2010-04-01

    Flow (a state of consciousness where people become totally immersed in an activity and enjoy it intensely) has been identified as a desirable state with positive effects for employee well-being and innovation at work. Flow has been studied using both questionnaires and Experience Sampling Method (ESM). In this study, we used a newly developed 9-item flow scale in an ESM study combined with a questionnaire to examine the predictors of flow at two levels: the activities (brainstorming, planning, problem solving and evaluation) associated with transient flow states and the more stable job characteristics (role clarity, influence and cognitive demands). Participants were 58 line managers from two companies in Denmark; a private accountancy firm and a public elder care organization. We found that line managers in elder care experienced flow more often than accountancy line managers, and activities such as planning, problem solving, and evaluation predicted transient flow states. The more stable job characteristics included in this study were not, however, found to predict flow at work.

  10. A Predictive Model for MSSW Student Success

    Science.gov (United States)

    Napier, Angela Michele

    2011-01-01

    This study tested a hypothetical model for predicting both graduate GPA and graduation of University of Louisville Kent School of Social Work Master of Science in Social Work (MSSW) students entering the program during the 2001-2005 school years. The preexisting characteristics of demographics, academic preparedness and culture shock along with…

  11. Predictive Models for Music

    OpenAIRE

    Paiement, Jean-François; Grandvalet, Yves; Bengio, Samy

    2008-01-01

    Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce generative models for melodies. We decompose melodic modeling into two subtasks. We first propose a rhythm model based on the distributions of distances between subsequences. Then, we define a generative model for melodies given chords and rhythms based on modeling sequences of Narmour featur...

  12. Developing predictive models for return to work using the Military Power, Performance and Prevention (MP3) musculoskeletal injury risk algorithm: a study protocol for an injury risk assessment programme.

    Science.gov (United States)

    Rhon, Daniel I; Teyhen, Deydre S; Shaffer, Scott W; Goffar, Stephen L; Kiesel, Kyle; Plisky, Phil P

    2016-11-24

    Musculoskeletal injuries are a primary source of disability in the US Military, and low back pain and lower extremity injuries account for over 44% of limited work days annually. History of prior musculoskeletal injury increases the risk for future injury. This study aims to determine the risk of injury after returning to work from a previous injury. The objective is to identify criteria that can help predict likelihood for future injury or re-injury. There will be 480 active duty soldiers recruited from across four medical centres. These will be patients who have sustained a musculoskeletal injury in the lower extremity or lumbar/thoracic spine, and have now been cleared to return back to work without any limitations. Subjects will undergo a battery of physical performance tests and fill out sociodemographic surveys. They will be followed for a year to identify any musculoskeletal injuries that occur. Prediction algorithms will be derived using regression analysis from performance and sociodemographic variables found to be significantly different between injured and non-injured subjects. Due to the high rates of injuries, injury prevention and prediction initiatives are growing. This is the first study looking at predicting re-injury rates after an initial musculoskeletal injury. In addition, multivariate prediction models appear to have move value than models based on only one variable. This approach aims to validate a multivariate model used in healthy non-injured individuals to help improve variables that best predict the ability to return to work with lower risk of injury, after a recent musculoskeletal injury. NCT02776930. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  13. Determinants of work ability and its predictive value for disability

    NARCIS (Netherlands)

    S.M. Alavinia; A.G.E.M. de Boer; J.C. van Duivenbooden; M.H.W. Frings-Dresen; A. Burdorf

    2009-01-01

    Background Maintaining the ability of workers to cope with physical and psychosocial demands at work becomes increasingly important in prolonging working life. Aims To analyse the effects of work-related factors and individual characteristics on work ability and to determine the predictive value of

  14. Zephyr - the prediction models

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg

    2001-01-01

    This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Dani...

  15. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    modelling strategy is applied to different training sets. For each modelling strategy we estimate a confidence score based on the same repeated bootstraps. A new decomposition of the expected Brier score is obtained, as well as the estimates of population average confidence scores. The latter can be used...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...

  16. A Minority Report for Social Work? The Predictive Risk Model (PRM) and the Tuituia Assessment Framework in addressing the needs of New Zealand's Vulnerable Children.

    Science.gov (United States)

    Oak, Eileen

    2016-07-01

    This article examines the viability of the Risk Predictor Model (RPM) and its counterpart the actuarial risk assessment (ARA) tool in the form of the Tuituia Assessment Framework to address child vulnerability in New Zealand. In doing so, it suggests that these types of risk-assessment tools fail to address issues of contingency and complexity at the heart of the relationship-based nature of social work practice. Such developments have considerable implications for the capacity to enhance critical reflexive practice skills, whilst the introduction of these risk tools is occurring at a time when the reflexive space is being eroded as a result of the increased regulation of practice and supervision. It is further asserted that the primary aim of such instruments is not so much to detect risk, but rather to foster professional conformity with these managerialist risk-management systems so prevalent in contemporary Western societies.

  17. Modelling, controlling, predicting blackouts

    CERN Document Server

    Wang, Chengwei; Baptista, Murilo S

    2016-01-01

    The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids, and another one for smart grids. The control strategie...

  18. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Teachers' Perceptions of Their Working Conditions: How Predictive of Policy-Relevant Outcomes? Working Paper 33

    Science.gov (United States)

    Ladd, Helen F.

    2009-01-01

    This quantitative study uses data from North Carolina to examine the extent to which survey based perceptions of working conditions are predictive of policy-relevant outcomes, independent of other school characteristics such as the demographic mix of the school's students. Working conditions emerge as highly predictive of teachers' stated…

  20. Predictive models of forest dynamics.

    Science.gov (United States)

    Purves, Drew; Pacala, Stephen

    2008-06-13

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

  1. The predictive mind and the experience of visual art work

    Science.gov (United States)

    Kesner, Ladislav

    2014-01-01

    Among the main challenges of the predictive brain/mind concept is how to link prediction at the neural level to prediction at the cognitive-psychological level and finding conceptually robust and empirically verifiable ways to harness this theoretical framework toward explaining higher-order mental and cognitive phenomena, including the subjective experience of aesthetic and symbolic forms. Building on the tentative prediction error account of visual art, this article extends the application of the predictive coding framework to the visual arts. It does so by linking this theoretical discussion to a subjective, phenomenological account of how a work of art is experienced. In order to engage more deeply with a work of art, viewers must be able to tune or adapt their prediction mechanism to recognize art as a specific class of objects whose ontological nature defies predictability, and they must be able to sustain a productive flow of predictions from low-level sensory, recognitional to abstract semantic, conceptual, and affective inferences. The affective component of the process of predictive error optimization that occurs when a viewer enters into dialog with a painting is constituted both by activating the affective affordances within the image and by the affective consequences of prediction error minimization itself. The predictive coding framework also has implications for the problem of the culturality of vision. A person’s mindset, which determines what top–down expectations and predictions are generated, is co-constituted by culture-relative skills and knowledge, which form hyperpriors that operate in the perception of art. PMID:25566111

  2. Working on What Works: A New Model for Collaboration

    Science.gov (United States)

    Berzin, Stephanie; O'Brien, Kimberly; Tohn, Susan

    2012-01-01

    As the classroom is an influential setting for students, new models for collaboration between teachers and school social workers are essential for meeting student needs. This article discusses the literature and model for one such intervention, classroom solutions: working on what works. Through pilot data presented from nine classrooms and two…

  3. Does musculoskeletal discomfort at work predict future musculoskeletal pain?

    NARCIS (Netherlands)

    Hamberg - Reenen, H.H. van; Beek, A.J. van der; Blatter, B.; Grinten, M.P. van der; Mechelen, W. van; Bongers, P.M.

    2008-01-01

    The objective of this prospective cohort study was to evaluate if peak or cumulative musculoskeletal discomfort may predict future low-back, neck or shoulder pain among symptom-free workers. At baseline, discomfort per body region was rated on a 10-point scale six times during a working day. Questio

  4. Predicting College Women's Career Plans: Instrumentality, Work, and Family

    Science.gov (United States)

    Savela, Alexandra E.; O'Brien, Karen M.

    2016-01-01

    This study examined how college women's instrumentality and expectations about combining work and family predicted early career development variables. Specifically, 177 undergraduate women completed measures of instrumentality (i.e., traits such as ambition, assertiveness, and risk taking), willingness to compromise career for family, anticipated…

  5. High Working Memory Capacity Predicts Less Retrieval Induced Forgetting

    NARCIS (Netherlands)

    Mall, Jonathan T.; Morey, Candice C.

    2013-01-01

    Background : Working Memory Capacity (WMC) is thought to be related to executive control and focused memory search abilities. These two hypotheses make contrasting predictions regarding the effects of retrieval on forgetting. Executive control during memory retrieval is believed to lead to retrieval

  6. Does musculoskeletal discomfort at work predict future musculoskeletal pain?

    NARCIS (Netherlands)

    Hamberg - Reenen, H.H. van; Beek, A.J. van der; Blatter, B.; Grinten, M.P. van der; Mechelen, W. van; Bongers, P.M.

    2008-01-01

    The objective of this prospective cohort study was to evaluate if peak or cumulative musculoskeletal discomfort may predict future low-back, neck or shoulder pain among symptom-free workers. At baseline, discomfort per body region was rated on a 10-point scale six times during a working day. Questio

  7. Global Solar Dynamo Models: Simulations and Predictions

    Indian Academy of Sciences (India)

    Mausumi Dikpati; Peter A. Gilman

    2008-03-01

    Flux-transport type solar dynamos have achieved considerable success in correctly simulating many solar cycle features, and are now being used for prediction of solar cycle timing and amplitude.We first define flux-transport dynamos and demonstrate how they work. The essential added ingredient in this class of models is meridional circulation, which governs the dynamo period and also plays a crucial role in determining the Sun’s memory about its past magnetic fields.We show that flux-transport dynamo models can explain many key features of solar cycles. Then we show that a predictive tool can be built from this class of dynamo that can be used to predict mean solar cycle features by assimilating magnetic field data from previous cycles.

  8. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...

  9. Calibrated predictions for multivariate competing risks models.

    Science.gov (United States)

    Gorfine, Malka; Hsu, Li; Zucker, David M; Parmigiani, Giovanni

    2014-04-01

    Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.

  10. The Culture-Work-Health Model and Work Stress.

    Science.gov (United States)

    Peterson, Michael; Wilson, John F.

    2002-01-01

    Examines the role of organizational culture in the etiology of workplace stress through the framework of the Culture-Work- Health model. A review of relevant business and health literature indicates that culture is an important component of work stress and may be a key to creating effective organizational stress interventions. (SM)

  11. The Culture-Work-Health Model and Work Stress.

    Science.gov (United States)

    Peterson, Michael; Wilson, John F.

    2002-01-01

    Examines the role of organizational culture in the etiology of workplace stress through the framework of the Culture-Work- Health model. A review of relevant business and health literature indicates that culture is an important component of work stress and may be a key to creating effective organizational stress interventions. (SM)

  12. Characterizing Attention with Predictive Network Models.

    Science.gov (United States)

    Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

    2017-04-01

    Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Perceived versus used workplace flexibility in Singapore: predicting work-family fit.

    Science.gov (United States)

    Jones, Blake L; Scoville, D Phillip; Hill, E Jeffrey; Childs, Geniel; Leishman, Joan M; Nally, Kathryn S

    2008-10-01

    This study examined the relationship of 2 types of workplace flexibility to work-family fit and work, personal, and marriage-family outcomes using data (N = 1,601) representative of employed persons in Singapore. We hypothesized that perceived and used workplace flexibility would be positively related to the study variables. Results derived from structural equation modeling revealed that perceived flexibility predicted work-family fit; however, used flexibility did not. Work-family fit related positively to each work, personal, and marriage-family outcome; however, workplace flexibility only predicted work and personal outcomes. Findings suggest work-family fit may be an important facilitating factor in the interface between work and family life, relating directly to marital satisfaction and satisfaction in other family relationships. Implications of these findings are discussed. Copyright 2008 APA, all rights reserved.

  14. Distributional Analysis for Model Predictive Deferrable Load Control

    OpenAIRE

    Chen, Niangjun; Gan, Lingwen; Low, Steven H.; Wierman, Adam

    2014-01-01

    Deferrable load control is essential for handling the uncertainties associated with the increasing penetration of renewable generation. Model predictive control has emerged as an effective approach for deferrable load control, and has received considerable attention. In particular, previous work has analyzed the average-case performance of model predictive deferrable load control. However, to this point, distributional analysis of model predictive deferrable load control has been elusive. In ...

  15. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  16. PREDICT : model for prediction of survival in localized prostate cancer

    NARCIS (Netherlands)

    Kerkmeijer, Linda G W; Monninkhof, Evelyn M.; van Oort, Inge M.; van der Poel, Henk G.; de Meerleer, Gert; van Vulpen, Marco

    2016-01-01

    Purpose: Current models for prediction of prostate cancer-specific survival do not incorporate all present-day interventions. In the present study, a pre-treatment prediction model for patients with localized prostate cancer was developed.Methods: From 1989 to 2008, 3383 patients were treated with I

  17. Functional MRI in Awake Dogs Predicts Suitability for Assistance Work

    Science.gov (United States)

    Berns, Gregory S.; Brooks, Andrew M.; Spivak, Mark; Levy, Kerinne

    2017-03-01

    The overall goal of this work was to measure the efficacy of fMRI for predicting whether a dog would be a successful service dog. The training and imaging were performed in 49 dogs entering service training at 17–21 months of age. 33 dogs completed service training and were matched with a person, while 10 were released for behavioral reasons (4 were selected as breeders and 2 were released for medical reasons.) After 2 months of training, fMRI responses were measured while each dog observed hand signals indicating either reward or no reward and given by both a familiar handler and a stranger. Using anatomically defined ROIs in the caudate, amygdala, and visual cortex, we developed a classifier based on the dogs’ subsequent training outcomes. The classifier had a positive predictive value of 94% and a negative predictive value of 67%. The area under the ROC curve was 0.91 (0.80 with 4-fold cross-validation, P = 0.01), indicating a significant predictive capability. The magnitude of response in the caudate was positively correlated with a successful outcome, while the response in the amygdala depended on the interaction with the visual cortex during the stranger condition and was negatively correlated with outcome (higher being associated with failure). These results suggest that, as indexed by caudate activity, successful service dogs generalize associations to hand signals regardless who gives them but without excessive arousal as measured in the amygdala.

  18. Functional MRI in Awake Dogs Predicts Suitability for Assistance Work

    Science.gov (United States)

    Berns, Gregory S.; Brooks, Andrew M.; Spivak, Mark; Levy, Kerinne

    2017-01-01

    The overall goal of this work was to measure the efficacy of fMRI for predicting whether a dog would be a successful service dog. The training and imaging were performed in 49 dogs entering service training at 17–21 months of age. 33 dogs completed service training and were matched with a person, while 10 were released for behavioral reasons (4 were selected as breeders and 2 were released for medical reasons.) After 2 months of training, fMRI responses were measured while each dog observed hand signals indicating either reward or no reward and given by both a familiar handler and a stranger. Using anatomically defined ROIs in the caudate, amygdala, and visual cortex, we developed a classifier based on the dogs’ subsequent training outcomes. The classifier had a positive predictive value of 94% and a negative predictive value of 67%. The area under the ROC curve was 0.91 (0.80 with 4-fold cross-validation, P = 0.01), indicating a significant predictive capability. The magnitude of response in the caudate was positively correlated with a successful outcome, while the response in the amygdala depended on the interaction with the visual cortex during the stranger condition and was negatively correlated with outcome (higher being associated with failure). These results suggest that, as indexed by caudate activity, successful service dogs generalize associations to hand signals regardless who gives them but without excessive arousal as measured in the amygdala. PMID:28266550

  19. The affective shift model of work engagement.

    Science.gov (United States)

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

    2011-11-01

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

  20. Predicting quality of work life on nurses' intention to leave.

    Science.gov (United States)

    Lee, Ya-Wen; Dai, Yu-Tzu; Park, Chang-Gi; McCreary, Linda L

    2013-06-01

    The purpose of this study was to explore the relationship between quality of work life (QWL) and nurses' intention to leave their organization (ITLorg). A descriptive cross-sectional survey design was conducted using purposive sampling of 1,283 nurses at seven hospitals in Taiwan. Data were collected from March to June 2012. Three questionnaires, including the Chinese version of the Quality of Nursing Work Life scale (C-QNWL), a questionnaire of intention to leave the organization, and a demographic questionnaire, with two informed consent forms were delivered to the nurses at their workplaces. Descriptive data, Pearson's correlations, and the ordinal regression model were analyzed. Over half (52.5%) of nurses had ITLorg. Seven QWL dimensions were significantly negatively correlated with ITLorg (r = -0.17 to -0.37, p working in a nonteaching hospital. Four of the QWL dimensions--supportive milieu with job security and professional recognition, work arrangement and workload, work or home life balance, and nursing staffing and patient care--were also predictors of ITLorg. Three QWL dimensions were not predictors of ITLorg. This study showed that individual-related variables (being single, having a diploma or lower educational level), a work-related variable (working at a nonteaching hospital), and the four QWL dimensions play a significant role in nurses' ITLorg. After the QWL dimensions were added to the regression, the variance explained by the model more than doubled. To reduce nurses' ITLorg, nursing administrators may offer more focused interventions to improve the supportive milieu with job security and professional recognition, work arrangement and workload, work or home life balance, and nursing staffing and patient care. © 2013 Sigma Theta Tau International.

  1. Predictive Modeling of Cardiac Ischemia

    Science.gov (United States)

    Anderson, Gary T.

    1996-01-01

    The goal of the Contextual Alarms Management System (CALMS) project is to develop sophisticated models to predict the onset of clinical cardiac ischemia before it occurs. The system will continuously monitor cardiac patients and set off an alarm when they appear about to suffer an ischemic episode. The models take as inputs information from patient history and combine it with continuously updated information extracted from blood pressure, oxygen saturation and ECG lines. Expert system, statistical, neural network and rough set methodologies are then used to forecast the onset of clinical ischemia before it transpires, thus allowing early intervention aimed at preventing morbid complications from occurring. The models will differ from previous attempts by including combinations of continuous and discrete inputs. A commercial medical instrumentation and software company has invested funds in the project with a goal of commercialization of the technology. The end product will be a system that analyzes physiologic parameters and produces an alarm when myocardial ischemia is present. If proven feasible, a CALMS-based system will be added to existing heart monitoring hardware.

  2. Numerical weather prediction model tuning via ensemble prediction system

    Science.gov (United States)

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

    2011-12-01

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

  3. Working memory dysfunctions predict social problem solving skills in schizophrenia.

    Science.gov (United States)

    Huang, Jia; Tan, Shu-ping; Walsh, Sarah C; Spriggens, Lauren K; Neumann, David L; Shum, David H K; Chan, Raymond C K

    2014-12-15

    The current study aimed to examine the contribution of neurocognition and social cognition to components of social problem solving. Sixty-seven inpatients with schizophrenia and 31 healthy controls were administrated batteries of neurocognitive tests, emotion perception tests, and the Chinese Assessment of Interpersonal Problem Solving Skills (CAIPSS). MANOVAs were conducted to investigate the domains in which patients with schizophrenia showed impairments. Correlations were used to determine which impaired domains were associated with social problem solving, and multiple regression analyses were conducted to compare the relative contribution of neurocognitive and social cognitive functioning to components of social problem solving. Compared with healthy controls, patients with schizophrenia performed significantly worse in sustained attention, working memory, negative emotion, intention identification and all components of the CAIPSS. Specifically, sustained attention, working memory and negative emotion identification were found to correlate with social problem solving and 1-back accuracy significantly predicted the poor performance in social problem solving. Among the dysfunctions in schizophrenia, working memory contributed most to deficits in social problem solving in patients with schizophrenia. This finding provides support for targeting working memory in the development of future social problem solving rehabilitation interventions.

  4. Disease Prediction Models and Operational Readiness

    Energy Technology Data Exchange (ETDEWEB)

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-03-19

    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  5. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...

  6. Predictive Model Assessment for Count Data

    Science.gov (United States)

    2007-09-05

    critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts...the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. We consider a recent suggestion by Baker and...Figure 5. Boxplots for various scores for patent data count regressions. 11 Table 1 Four predictive models for larynx cancer counts in Germany, 1998–2002

  7. Ground Motion Prediction Models for Caucasus Region

    Science.gov (United States)

    Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino

    2016-04-01

    Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.

  8. A NEW MODEL FOR WORK STRESS PATTERNS

    OpenAIRE

    Belal Barhem; Samsinar Md Sidin; Iskandar Abdullah; Syed Kadir Alsagoff

    2004-01-01

    This study tests a new work stress model by evaluating the major work stress sources and work stress coping strategies experienced by the Malaysian and Jordanian Customs Department employees. It further ranks the sources and coping strategies of work stress, and evaluates the relationships between stress patterns. The sample consists of 216 Malaysian Customs employees and 248 Jordanian Customs officers, from which correlation, means, path analysis and frequencies were computed. The major find...

  9. Prediction Models of Free-Field Vibrations from Railway Traffic

    DEFF Research Database (Denmark)

    Malmborg, Jens; Persson, Kent; Persson, Peter

    2017-01-01

    and railways close to where people work and live. Annoyance from traffic-induced vibrations and noise is expected to be a growing issue. To predict the level of vibration and noise in buildings caused by railway and road traffic, calculation models are needed. In the present paper, a simplified prediction...

  10. Making eco logic and models work

    NARCIS (Netherlands)

    Kuiper, Jan Jurjen

    2016-01-01

    Dynamical ecosystem models are important tools that can help ecologists understand complex systems, and turn understanding into predictions of how these systems respond to external changes. This thesis revolves around PCLake, an integrated ecosystem model of shallow lakes that is used by both

  11. Making eco logic and models work

    NARCIS (Netherlands)

    Kuiper, Jan Jurjen

    2016-01-01

    Dynamical ecosystem models are important tools that can help ecologists understand complex systems, and turn understanding into predictions of how these systems respond to external changes. This thesis revolves around PCLake, an integrated ecosystem model of shallow lakes that is used by both scient

  12. Making eco logic and models work

    NARCIS (Netherlands)

    Kuiper, Jan Jurjen

    2016-01-01

    Dynamical ecosystem models are important tools that can help ecologists understand complex systems, and turn understanding into predictions of how these systems respond to external changes. This thesis revolves around PCLake, an integrated ecosystem model of shallow lakes that is used by both scient

  13. Testing a Model of Work Performance in an Academic Environment

    Directory of Open Access Journals (Sweden)

    B. Charles Tatum

    2012-04-01

    Full Text Available In modern society, people both work and study. The intersection between organizational and educational research suggests that a common model should apply to both academic and job performance. The purpose of this study was to apply a model of work and job performance (based on general expectancy theory to a classroom setting, and test the predicted relationships using a causal/path model methodology. The findings revealed that motivation and ability predicted student expectations and self-efficacy, and that expectations and efficacy predicted class performance. Limitations, implications, and future research directions are discussed. This study showed how the research in industrial and organizational psychology is relevant to education. It was concluded that greater effort should be made to integrate knowledge across a wider set of domains.

  14. Nonlinear chaotic model for predicting storm surges

    Directory of Open Access Journals (Sweden)

    M. Siek

    2010-09-01

    Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

  15. Nonlinear chaotic model for predicting storm surges

    NARCIS (Netherlands)

    Siek, M.; Solomatine, D.P.

    This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables.

  16. Predictive modelling of ferroelectric tunnel junctions

    Science.gov (United States)

    Velev, Julian P.; Burton, John D.; Zhuravlev, Mikhail Ye; Tsymbal, Evgeny Y.

    2016-05-01

    Ferroelectric tunnel junctions combine the phenomena of quantum-mechanical tunnelling and switchable spontaneous polarisation of a nanometre-thick ferroelectric film into novel device functionality. Switching the ferroelectric barrier polarisation direction produces a sizable change in resistance of the junction—a phenomenon known as the tunnelling electroresistance effect. From a fundamental perspective, ferroelectric tunnel junctions and their version with ferromagnetic electrodes, i.e., multiferroic tunnel junctions, are testbeds for studying the underlying mechanisms of tunnelling electroresistance as well as the interplay between electric and magnetic degrees of freedom and their effect on transport. From a practical perspective, ferroelectric tunnel junctions hold promise for disruptive device applications. In a very short time, they have traversed the path from basic model predictions to prototypes for novel non-volatile ferroelectric random access memories with non-destructive readout. This remarkable progress is to a large extent driven by a productive cycle of predictive modelling and innovative experimental effort. In this review article, we outline the development of the ferroelectric tunnel junction concept and the role of theoretical modelling in guiding experimental work. We discuss a wide range of physical phenomena that control the functional properties of ferroelectric tunnel junctions and summarise the state-of-the-art achievements in the field.

  17. MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION

    Directory of Open Access Journals (Sweden)

    Priyanka H U

    2016-09-01

    Full Text Available Developing predictive modelling solutions for risk estimation is extremely challenging in health-care informatics. Risk estimation involves integration of heterogeneous clinical sources having different representation from different health-care provider making the task increasingly complex. Such sources are typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel computing tools collectively termed big data tools are in need which can synthesize and assist the physician to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel approach for combining the predictive ability of multiple models for better prediction accuracy. We demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study. Results show that the proposed multi-model predictive architecture is able to provide better accuracy than best model approach. By modelling the error of predictive models we are able to choose sub set of models which yields accurate results. More information was modelled into system by multi-level mining which has resulted in enhanced predictive accuracy.

  18. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

    Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.

    2014-01-01

    The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain,...

  19. How to Establish Clinical Prediction Models

    Directory of Open Access Journals (Sweden)

    Yong-ho Lee

    2016-03-01

    Full Text Available A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.

  20. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    is a realization of a continuous-discrete multivariate stochastic transfer function model. The proposed prediction error-methods are demonstrated for a SISO system parameterized by the transfer functions with time delays of a continuous-discrete-time linear stochastic system. The simulations for this case suggest......Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which...... computational resources. The identification method is suitable for predictive control....

  1. Model for a Healthy Work Environment.

    Science.gov (United States)

    Blevins, Jamie

    2016-01-01

    The Healthy Work Environment (HWE) Model, considered a model of standards of professional behaviors, was created to help foster an environment that is happy, healthy, realistic, and feasible. The model focuses on areas of PEOPLE and PRACTICE, where each letter of these words identifies core, professional qualities and behaviors to foster an environment amenable and conducive to accountability for one's behavior and action. Each of these characteristics is supported from a Christian, biblical perspective. The HWE Model provides a mental and physical checklist of what is important in creating and sustaining a healthy work environment in education and practice.

  2. A NEW MODEL FOR WORK STRESS PATTERNS

    Directory of Open Access Journals (Sweden)

    Belal Barhem

    2004-01-01

    Full Text Available This study tests a new work stress model by evaluating the major work stress sources and work stress coping strategies experienced by the Malaysian and Jordanian Customs Department employees. It further ranks the sources and coping strategies of work stress, and evaluates the relationships between stress patterns. The sample consists of 216 Malaysian Customs employees and 248 Jordanian Customs officers, from which correlation, means, path analysis and frequencies were computed. The major findings of the study show that Malaysian and Jordanian Customs employees identified role ambiguity as the main source of work stress while self-knowledge was the major coping strategy they used to overcome work stress. The relationship between sources of work stress and coping strategies is strong in the two cases while the relationship with personal differences is weak.

  3. Critical conceptualism in environmental modeling and prediction.

    Science.gov (United States)

    Christakos, G

    2003-10-15

    Many important problems in environmental science and engineering are of a conceptual nature. Research and development, however, often becomes so preoccupied with technical issues, which are themselves fascinating, that it neglects essential methodological elements of conceptual reasoning and theoretical inquiry. This work suggests that valuable insight into environmental modeling can be gained by means of critical conceptualism which focuses on the software of human reason and, in practical terms, leads to a powerful methodological framework of space-time modeling and prediction. A knowledge synthesis system develops the rational means for the epistemic integration of various physical knowledge bases relevant to the natural system of interest in order to obtain a realistic representation of the system, provide a rigorous assessment of the uncertainty sources, generate meaningful predictions of environmental processes in space-time, and produce science-based decisions. No restriction is imposed on the shape of the distribution model or the form of the predictor (non-Gaussian distributions, multiple-point statistics, and nonlinear models are automatically incorporated). The scientific reasoning structure underlying knowledge synthesis involves teleologic criteria and stochastic logic principles which have important advantages over the reasoning method of conventional space-time techniques. Insight is gained in terms of real world applications, including the following: the study of global ozone patterns in the atmosphere using data sets generated by instruments on board the Nimbus 7 satellite and secondary information in terms of total ozone-tropopause pressure models; the mapping of arsenic concentrations in the Bangladesh drinking water by assimilating hard and soft data from an extensive network of monitoring wells; and the dynamic imaging of probability distributions of pollutants across the Kalamazoo river.

  4. How evolutionary crystal structure prediction works--and why.

    Science.gov (United States)

    Oganov, Artem R; Lyakhov, Andriy O; Valle, Mario

    2011-03-15

    Once the crystal structure of a chemical substance is known, many properties can be predicted reliably and routinely. Therefore if researchers could predict the crystal structure of a material before it is synthesized, they could significantly accelerate the discovery of new materials. In addition, the ability to predict crystal structures at arbitrary conditions of pressure and temperature is invaluable for the study of matter at extreme conditions, where experiments are difficult. Crystal structure prediction (CSP), the problem of finding the most stable arrangement of atoms given only the chemical composition, has long remained a major unsolved scientific problem. Two problems are entangled here: search, the efficient exploration of the multidimensional energy landscape, and ranking, the correct calculation of relative energies. For organic crystals, which contain a few molecules in the unit cell, search can be quite simple as long as a researcher does not need to include many possible isomers or conformations of the molecules; therefore ranking becomes the main challenge. For inorganic crystals, quantum mechanical methods often provide correct relative energies, making search the most critical problem. Recent developments provide useful practical methods for solving the search problem to a considerable extent. One can use simulated annealing, metadynamics, random sampling, basin hopping, minima hopping, and data mining. Genetic algorithms have been applied to crystals since 1995, but with limited success, which necessitated the development of a very different evolutionary algorithm. This Account reviews CSP using one of the major techniques, the hybrid evolutionary algorithm USPEX (Universal Structure Predictor: Evolutionary Xtallography). Using recent developments in the theory of energy landscapes, we unravel the reasons evolutionary techniques work for CSP and point out their limitations. We demonstrate that the energy landscapes of chemical systems have an

  5. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus

    2007-01-01

    In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing p

  6. Childhood asthma prediction models: a systematic review.

    Science.gov (United States)

    Smit, Henriette A; Pinart, Mariona; Antó, Josep M; Keil, Thomas; Bousquet, Jean; Carlsen, Kai H; Moons, Karel G M; Hooft, Lotty; Carlsen, Karin C Lødrup

    2015-12-01

    Early identification of children at risk of developing asthma at school age is crucial, but the usefulness of childhood asthma prediction models in clinical practice is still unclear. We systematically reviewed all existing prediction models to identify preschool children with asthma-like symptoms at risk of developing asthma at school age. Studies were included if they developed a new prediction model or updated an existing model in children aged 4 years or younger with asthma-like symptoms, with assessment of asthma done between 6 and 12 years of age. 12 prediction models were identified in four types of cohorts of preschool children: those with health-care visits, those with parent-reported symptoms, those at high risk of asthma, or children in the general population. Four basic models included non-invasive, easy-to-obtain predictors only, notably family history, allergic disease comorbidities or precursors of asthma, and severity of early symptoms. Eight extended models included additional clinical tests, mostly specific IgE determination. Some models could better predict asthma development and other models could better rule out asthma development, but the predictive performance of no single model stood out in both aspects simultaneously. This finding suggests that there is a large proportion of preschool children with wheeze for which prediction of asthma development is difficult.

  7. Modeling complex work systems - method meets reality

    NARCIS (Netherlands)

    van der Veer, Gerrit C.; Hoeve, Machteld; Lenting, Bert

    1996-01-01

    Modeling an existing task situation is often a first phase in the (re)design of information systems. For complex systems design, this model should consider both the people and the organization involved, the work, and situational aspects. Groupware Task Analysis (GTA) as part of a method for the

  8. Modeling complex work systems - method meets reality

    NARCIS (Netherlands)

    Veer, van der Gerrit C.; Hoeve, Machteld; Lenting, Bert F.

    1996-01-01

    Modeling an existing task situation is often a first phase in the (re)design of information systems. For complex systems design, this model should consider both the people and the organization involved, the work, and situational aspects. Groupware Task Analysis (GTA) as part of a method for the desi

  9. Process model patterns for collaborative work

    OpenAIRE

    Lonchamp, Jacques

    1998-01-01

    Colloque avec actes et comité de lecture.; As most real work is collaborative in nature, process model developers have to model collaborative situations. This paper defines generic collaborative patterns, ie, pragmatic and abstract building blocks for modelling recurrent situations. The first part specifies the graphical notation for the solution description. The second part gives some current patterns for the collaborative production of a single document in isolation and for the synchronizat...

  10. The regional prediction model of PM10 concentrations for Turkey

    Science.gov (United States)

    Güler, Nevin; Güneri İşçi, Öznur

    2016-11-01

    This study is aimed to predict a regional model for weekly PM10 concentrations measured air pollution monitoring stations in Turkey. There are seven geographical regions in Turkey and numerous monitoring stations at each region. Predicting a model conventionally for each monitoring station requires a lot of labor and time and it may lead to degradation in quality of prediction when the number of measurements obtained from any õmonitoring station is small. Besides, prediction models obtained by this way only reflect the air pollutant behavior of a small area. This study uses Fuzzy C-Auto Regressive Model (FCARM) in order to find a prediction model to be reflected the regional behavior of weekly PM10 concentrations. The superiority of FCARM is to have the ability of considering simultaneously PM10 concentrations measured monitoring stations in the specified region. Besides, it also works even if the number of measurements obtained from the monitoring stations is different or small. In order to evaluate the performance of FCARM, FCARM is executed for all regions in Turkey and prediction results are compared to statistical Autoregressive (AR) Models predicted for each station separately. According to Mean Absolute Percentage Error (MAPE) criteria, it is observed that FCARM provides the better predictions with a less number of models.

  11. A prognostic model to predict survival in 867 World Health Organization-defined essential thrombocythemia at diagnosis: a study by the International Working Group on Myelofibrosis Research and Treatment.

    Science.gov (United States)

    Passamonti, Francesco; Thiele, Jürgen; Girodon, Francois; Rumi, Elisa; Carobbio, Alessandra; Gisslinger, Heinz; Kvasnicka, Hans Michael; Ruggeri, Marco; Randi, Maria Luigia; Gangat, Naseema; Vannucchi, Alessandro Maria; Gianatti, Andrea; Gisslinger, Bettina; Müllauer, Leonhard; Rodeghiero, Francesco; d'Amore, Emanuele S G; Bertozzi, Irene; Hanson, Curtis A; Boveri, Emanuela; Marino, Filippo; Maffioli, Margherita; Caramazza, Domenica; Antonioli, Elisabetta; Carrai, Valentina; Buxhofer-Ausch, Veronika; Pascutto, Cristiana; Cazzola, Mario; Barbui, Tiziano; Tefferi, Ayalew

    2012-08-01

    Diagnosis of essential thrombocythemia (ET) has been updated in the last World Health Organization (WHO) classification. We developed a prognostic model to predict survival at diagnosis, named IPSET (International Prognostic Score for ET), studying patients with WHO-defined ET. Age 60 years or older, leukocyte count ≥ 11 × 10(9)/L, and prior thrombosis significantly affected survival, by multivariable Cox regression. On the basis of the hazard ratio, we assigned 2 points to age and 1 each to leukocyte count and thrombosis. So, the IPSET model allocated 867 patients into 3 risk categories with significantly different survival: low (sum of points = 0; median survival not reached), intermediate (sum = 1-2; median survival 24.5 years), and high (sum = 3-4, median survival 13.8 years). The IPSET model was further validated in 2 independent cohorts including 132 WHO-defined ET and 234 Polycythemia Vera Study Group-defined ET patients. The IPSET model was able to predict the occurrence of thrombosis, and not to predict post-ET myelofibrosis. In conclusion, IPSET, based on age ≥ 60 years, leukocyte count ≥ 11 × 10(9)/L, and history of thrombosis allows prognostic assessment of WHO-defined ET and the validation process makes IPSET applicable in all patients phenotypically appearing as ET.

  12. Predictive Cache Modeling and Analysis

    Science.gov (United States)

    2011-11-01

    Extension of the Synthetic Application Generation Framework ........................................................... 35  5.2.2.  Investigating Other...Handlers Target Hardware (Processor, Memory, I/O devices) Application Main Program = Object = Process Legend: Middleware/ Framework Virtual memory Partition... metaheuristic /bin-packing algorithm to optimize task placement based on task communication characterization. Our previous work on task allocation showed

  13. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  14. Climate system model, numerical simulation and climate predictability

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    @@ Thanks to its work of past more than 20 years,a research team led by Prof.ZENG Qingcun and Prof.WANG Huijun from the CAS Institute of Atmospheric Physics (IAP) has scored innovative achievements in their studies of basic theory of climate dynamics,numerical model development,its related computational theory,and the dynamical climate prediction using the climate system models.Their work received a second prize of the National Award for Natural Sciences in 2005.

  15. Work Functions for Models of Scandate Surfaces

    Science.gov (United States)

    Mueller, Wolfgang

    1997-01-01

    The electronic structure, surface dipole properties, and work functions of scandate surfaces have been investigated using the fully relativistic scattered-wave cluster approach. Three different types of model surfaces are considered: (1) a monolayer of Ba-Sc-O on W(100), (2) Ba or BaO adsorbed on Sc2O3 + W, and (3) BaO on SC2O3 + WO3. Changes in the work function due to Ba or BaO adsorption on the different surfaces are calculated by employing the depolarization model of interacting surface dipoles. The largest work function change and the lowest work function of 1.54 eV are obtained for Ba adsorbed on the Sc-O monolayer on W(100). The adsorption of Ba on Sc2O3 + W does not lead to a low work function, but the adsorption of BaO results in a work function of about 1.6-1.9 eV. BaO adsorbed on Sc2O3 + WO3, or scandium tungstates, may also lead to low work functions.

  16. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...

  17. Development of a working Hovercraft model

    Science.gov (United States)

    Noor, S. H. Mohamed; Syam, K.; Jaafar, A. A.; Mohamad Sharif, M. F.; Ghazali, M. R.; Ibrahim, W. I.; Atan, M. F.

    2016-02-01

    This paper presents the development process to fabricate a working hovercraft model. The purpose of this study is to design and investigate of a fully functional hovercraft, based on the studies that had been done. The different designs of hovercraft model had been made and tested but only one of the models is presented in this paper. In this thesis, the weight, the thrust, the lift and the drag force of the model had been measured and the electrical and mechanical parts are also presented. The processing unit of this model is Arduino Uno by using the PSP2 (Playstation 2) as the controller. Since our prototype should be functioning on all kind of earth surface, our model also had been tested in different floor condition. They include water, grass, cement and tile. The Speed of the model is measured in every case as the respond variable, Current (I) as the manipulated variable and Voltage (V) as the constant variable.

  18. Model for predicting mountain wave field uncertainties

    Science.gov (United States)

    Damiens, Florentin; Lott, François; Millet, Christophe; Plougonven, Riwal

    2017-04-01

    Studying the propagation of acoustic waves throughout troposphere requires knowledge of wind speed and temperature gradients from the ground up to about 10-20 km. Typical planetary boundary layers flows are known to present vertical low level shears that can interact with mountain waves, thereby triggering small-scale disturbances. Resolving these fluctuations for long-range propagation problems is, however, not feasible because of computer memory/time restrictions and thus, they need to be parameterized. When the disturbances are small enough, these fluctuations can be described by linear equations. Previous works by co-authors have shown that the critical layer dynamics that occur near the ground produces large horizontal flows and buoyancy disturbances that result in intense downslope winds and gravity wave breaking. While these phenomena manifest almost systematically for high Richardson numbers and when the boundary layer depth is relatively small compare to the mountain height, the process by which static stability affects downslope winds remains unclear. In the present work, new linear mountain gravity wave solutions are tested against numerical predictions obtained with the Weather Research and Forecasting (WRF) model. For Richardson numbers typically larger than unity, the mesoscale model is used to quantify the effect of neglected nonlinear terms on downslope winds and mountain wave patterns. At these regimes, the large downslope winds transport warm air, a so called "Foehn" effect than can impact sound propagation properties. The sensitivity of small-scale disturbances to Richardson number is quantified using two-dimensional spectral analysis. It is shown through a pilot study of subgrid scale fluctuations of boundary layer flows over realistic mountains that the cross-spectrum of mountain wave field is made up of the same components found in WRF simulations. The impact of each individual component on acoustic wave propagation is discussed in terms of

  19. Massive Predictive Modeling using Oracle R Enterprise

    CERN Document Server

    CERN. Geneva

    2014-01-01

    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  20. Modeling complex work systems - method meets reality

    OpenAIRE

    Veer, van der, C.G.; Hoeve, Machteld; Lenting, Bert F.

    1996-01-01

    Modeling an existing task situation is often a first phase in the (re)design of information systems. For complex systems design, this model should consider both the people and the organization involved, the work, and situational aspects. Groupware Task Analysis (GTA) as part of a method for the design of complex systems, has been applied in a situation of redesign of a Dutch public administration system. The most feasible method to collect information in this case was ethnography, the resulti...

  1. A thermodynamic model to predict wax formation in petroleum fluids

    Energy Technology Data Exchange (ETDEWEB)

    Coutinho, J.A.P. [Universidade de Aveiro (Portugal). Dept. de Quimica. Centro de Investigacao em Quimica]. E-mail: jcoutinho@dq.ua.pt; Pauly, J.; Daridon, J.L. [Universite de Pau et des Pays de l' Adour, Pau (France). Lab. des Fluides Complexes

    2001-12-01

    Some years ago the authors proposed a model for the non-ideality of the solid phase, based on the Predictive Local Composition concept. This was first applied to the Wilson equation and latter extended to NRTL and UNIQUAC models. Predictive UNIQUAC proved to be extraordinarily successful in predicting the behaviour of both model and real hydrocarbon fluids at low temperatures. This work illustrates the ability of Predictive UNIQUAC in the description of the low temperature behaviour of petroleum fluids. It will be shown that using Predictive UNIQUAC in the description of the solid phase non-ideality a complete prediction of the low temperature behaviour of synthetic paraffin solutions, fuels and crude oils is achieved. The composition of both liquid and solid phases, the amount of crystals formed and the cloud points are predicted within the accuracy of the experimental data. The extension of Predictive UNIQUAC to high pressures, by coupling it with an EOS/G{sup E} model based on the SRK EOS used with the LCVM mixing rule, is proposed and predictions of phase envelopes for live oils are compared with experimental data. (author)

  2. A THERMODYNAMIC MODEL TO PREDICT WAX FORMATION IN PETROLEUM FLUIDS

    Directory of Open Access Journals (Sweden)

    J.A.P. Coutinho

    2001-12-01

    Full Text Available Some years ago the authors proposed a model for the non-ideality of the solid phase, based on the Predictive Local Composition concept. This was first applied to the Wilson equation and latter extended to NRTL and UNIQUAC models. Predictive UNIQUAC proved to be extraordinarily successful in predicting the behaviour of both model and real hydrocarbon fluids at low temperatures. This work illustrates the ability of Predictive UNIQUAC in the description of the low temperature behaviour of petroleum fluids. It will be shown that using Predictive UNIQUAC in the description of the solid phase non-ideality a complete prediction of the low temperature behaviour of synthetic paraffin solutions, fuels and crude oils is achieved. The composition of both liquid and solid phases, the amount of crystals formed and the cloud points are predicted within the accuracy of the experimental data. The extension of Predictive UNIQUAC to high pressures, by coupling it with an EOS/G E model based on the SRK EOS used with the LCVM mixing rule, is proposed and predictions of phase envelopes for live oils are compared with experimental data.

  3. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  4. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  5. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Working with Teaching Assistants: Three Models Evaluated

    Science.gov (United States)

    Cremin, Hilary; Thomas, Gary; Vincett, Karen

    2005-01-01

    Questions about how best to deploy teaching assistants (TAs) are particularly opposite given the greatly increasing numbers of TAs in British schools and given findings about the difficulty effecting adult teamwork in classrooms. In six classrooms, three models of team organisation and planning for the work of teaching assistants -- "room…

  16. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  17. Predicting working days for secondary tillage and planting operation in fall

    Directory of Open Access Journals (Sweden)

    A Kosari Moghaddam

    2016-09-01

    Full Text Available Introduction The working day is an important component in selection and analysis of farm machinery systems. The number of working days is affected by various factors such as climate, soil characteristics and type of operation. Daily soil moisture models based on weather long-term data and soil characteristics were almost used for calculating probability of working days. The goal of this study was to develop a simulation model to predict the number of working days for secondary tillage and planting operation in fall at 50, 80 and 90% probability levels. Materials and Methods A Simulation model was developed using 21 years weather data and soil characteristics for calculate daily soil moisture content in Research Station of Ferdowsi University of Mashhad. So soil moisture was calculated using daily soil water equation for top 25 centimeter of soil depth. Moisture equal or lower than 85% of soil field capacity and precipitation lower than 4 millimeter (local data were considered as soil workability criteria. Then the working days were determined for secondary tillage and planting operation at 50, 80 and 90% probability levels in falls. The number of days at 50% probability was the mean over 21 years and the number of days at 80% and 90% were determined for each two weeks period as the average number of working days minus the product of t value and standard deviation of those numbers. Model Evaluation Evaluation of model included a comparison of predicted and the observed the number of working days in Research Station of Ferdowsi University of Mashhad during 2002-2010 and sensitivity analysis was implemented to test the effect of changes in soil workability criterion (80, 90, 95 and 100% of soil field capacity, drainage coefficient (25 % decrease and increase and soil field capacity (40% increase on simulation results. Results and Discussion Comparison of predicted and observed days showed that correlation coefficient was 0.998 and the difference

  18. Do measures of working memory predict academic proficiency better than measures of intelligence?

    Directory of Open Access Journals (Sweden)

    KERRY LEE

    2009-12-01

    Full Text Available It is often asserted that working memory predicts more variance in academic proficiency than do measures of intelligence. We used data from three studies to show that the validity of this assertion is highly dependent on the method of analysis. Using the same measures of intelligence, but different measures of working memory and algebraic proficiency, we found working memory provided better explanatory power only when analysis was conducted on the observed variable level. When the same data were analysed using structural equation models, only measures of intelligence had a direct effect on algebraic proficiency. From a theoretical viewpoint, our findings are consistent with a claim that working memory is a constituent component of (fluid intelligence.

  19. A Course in... Model Predictive Control.

    Science.gov (United States)

    Arkun, Yaman; And Others

    1988-01-01

    Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)

  20. Equivalency and unbiasedness of grey prediction models

    Institute of Scientific and Technical Information of China (English)

    Bo Zeng; Chuan Li; Guo Chen; Xianjun Long

    2015-01-01

    In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction mo-dels, the equivalence and unbiasedness of grey prediction mo-dels are analyzed and verified. The results show that al the grey prediction models that are strictly derived from x(0)(k) +az(1)(k) = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homoge-neous exponential sequence can be accomplished. However, the models derived from dx(1)/dt+ax(1) =b are only close to those derived from x(0)(k)+az(1)(k)=b provided that|a|has to satisfy|a| < 0.1; neither could the unbiased simulation for the homoge-neous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.

  1. Predictability of extreme values in geophysical models

    Directory of Open Access Journals (Sweden)

    A. E. Sterk

    2012-09-01

    Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.

  2. Working Memory and Auditory Imagery Predict Sensorimotor Synchronization with Expressively Timed Music.

    Science.gov (United States)

    Colley, Ian D; Keller, Peter E; Halpern, Andrea R

    2017-08-11

    Sensorimotor synchronization (SMS) is prevalent and readily studied in musical settings, as most people are able to perceive and synchronize with a beat (e.g. by finger tapping). We took an individual differences approach to understanding SMS to real music characterized by expressive timing (i.e. fluctuating beat regularity). Given the dynamic nature of SMS, we hypothesized that individual differences in working memory and auditory imagery-both fluid cognitive processes-would predict SMS at two levels: 1) mean absolute asynchrony (a measure of synchronization error), and 2) anticipatory timing (i.e. predicting, rather than reacting to beat intervals). In Experiment 1, participants completed two working memory tasks, four auditory imagery tasks, and an SMS-tapping task. Hierarchical regression models were used to predict SMS performance, with results showing dissociations among imagery types in relation to mean absolute asynchrony, and evidence of a role for working memory in anticipatory timing. In Experiment 2, a new sample of participants completed an expressive timing perception task to examine the role of imagery in perception without action. Results suggest that imagery vividness is important for perceiving and control is important for synchronizing with, irregular but ecologically valid musical time series. Working memory is implicated in synchronizing by anticipating events in the series.

  3. A Social Work Model of Empathy

    Directory of Open Access Journals (Sweden)

    Karen E. Gerdes

    2009-12-01

    Full Text Available This article presents a social work model of empathy that reflects the latest interdisciplinary research findings on empathy. The model reflects the social work commitment to social justice. The three model components are: 1 the affective response to another’s emotions and actions; 2 the cognitive processing of one’s affective response and the other person’s perspective; and 3 the conscious decision-making to take empathic action. Mirrored affective responses are involuntary, while cognitive processing and conscious decision-making are voluntary. The affective component requires healthy, neural pathways to function appropriately and accurately. The cognitive aspects of perspective-taking, self-awareness, and emotion regulation can be practiced and cultivated, particularly through the use of mindfulness techniques. Empathic action requires that we move beyond affective responses and cognitive processing toward utilizing social work values and knowledge to inform our actions. By introducing the proposed model of empathy, we hope it will serve as a catalyst for discussion and future research and development of the model. Key Words: Empathy, Social Empathy, Social Cognitive Neuroscience

  4. Economic Modeling in SocialWork Education

    Directory of Open Access Journals (Sweden)

    Barry R. Cournoyer

    2000-12-01

    Full Text Available Economic modeling provides academic administrators with a logical framework for analyzing costs associated with the processes involved in the delivery of social work education. The specific costs associated with activities such as teaching, research, and service may be determined for a school of social work as a whole or for specific responsibility centers (e.g., programs and services within the school. Economic modeling utilizes modern spreadsheet software that can be configured in relation to the idiosyncratic needs and budgeting strategies that exist in virtually all colleges and universities. As a versatile planning tool, it enables managers to identify specific “cost-drivers” that cause the occurrence of real costs in relation to designated programmatic initiatives. In addition, economic modeling provides academic planners and decision-makers a useful vehicle for considering the economic impact of various projected (“what if” scenarios.

  5. Hybrid modeling and prediction of dynamical systems

    Science.gov (United States)

    Lloyd, Alun L.; Flores, Kevin B.

    2017-01-01

    Scientific analysis often relies on the ability to make accurate predictions of a system’s dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often used for this purpose. Accurate estimation of the model state and parameters prior to prediction is necessary, but may be complicated by issues such as noisy data and uncertainty in parameters and initial conditions. At the other end of the spectrum exist nonparametric methods, which rely solely on data to build their predictions. While these nonparametric methods do not require a model of the system, their performance is strongly influenced by the amount and noisiness of the data. In this article, we consider a hybrid approach to modeling and prediction which merges recent advancements in nonparametric analysis with standard parametric methods. The general idea is to replace a subset of a mechanistic model’s equations with their corresponding nonparametric representations, resulting in a hybrid modeling and prediction scheme. Overall, we find that this hybrid approach allows for more robust parameter estimation and improved short-term prediction in situations where there is a large uncertainty in model parameters. We demonstrate these advantages in the classical Lorenz-63 chaotic system and in networks of Hindmarsh-Rose neurons before application to experimentally collected structured population data. PMID:28692642

  6. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

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

    2016-12-01

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

  7. Property predictions using microstructural modeling

    Energy Technology Data Exchange (ETDEWEB)

    Wang, K.G. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)]. E-mail: wangk2@rpi.edu; Guo, Z. [Sente Software Ltd., Surrey Technology Centre, 40 Occam Road, Guildford GU2 7YG (United Kingdom); Sha, W. [Metals Research Group, School of Civil Engineering, Architecture and Planning, The Queen' s University of Belfast, Belfast BT7 1NN (United Kingdom); Glicksman, M.E. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States); Rajan, K. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)

    2005-07-15

    Precipitation hardening in an Fe-12Ni-6Mn maraging steel during overaging is quantified. First, applying our recent kinetic model of coarsening [Phys. Rev. E, 69 (2004) 061507], and incorporating the Ashby-Orowan relationship, we link quantifiable aspects of the microstructures of these steels to their mechanical properties, including especially the hardness. Specifically, hardness measurements allow calculation of the precipitate size as a function of time and temperature through the Ashby-Orowan relationship. Second, calculated precipitate sizes and thermodynamic data determined with Thermo-Calc[copyright] are used with our recent kinetic coarsening model to extract diffusion coefficients during overaging from hardness measurements. Finally, employing more accurate diffusion parameters, we determined the hardness of these alloys independently from theory, and found agreement with experimental hardness data. Diffusion coefficients determined during overaging of these steels are notably higher than those found during the aging - an observation suggesting that precipitate growth during aging and precipitate coarsening during overaging are not controlled by the same diffusion mechanism.

  8. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  9. Predictive error analysis for a water resource management model

    Science.gov (United States)

    Gallagher, Mark; Doherty, John

    2007-02-01

    SummaryIn calibrating a model, a set of parameters is assigned to the model which will be employed for the making of all future predictions. If these parameters are estimated through solution of an inverse problem, formulated to be properly posed through either pre-calibration or mathematical regularisation, then solution of this inverse problem will, of necessity, lead to a simplified parameter set that omits the details of reality, while still fitting historical data acceptably well. Furthermore, estimates of parameters so obtained will be contaminated by measurement noise. Both of these phenomena will lead to errors in predictions made by the model, with the potential for error increasing with the hydraulic property detail on which the prediction depends. Integrity of model usage demands that model predictions be accompanied by some estimate of the possible errors associated with them. The present paper applies theory developed in a previous work to the analysis of predictive error associated with a real world, water resource management model. The analysis offers many challenges, including the fact that the model is a complex one that was partly calibrated by hand. Nevertheless, it is typical of models which are commonly employed as the basis for the making of important decisions, and for which such an analysis must be made. The potential errors associated with point-based and averaged water level and creek inflow predictions are examined, together with the dependence of these errors on the amount of averaging involved. Error variances associated with predictions made by the existing model are compared with "optimized error variances" that could have been obtained had calibration been undertaken in such a way as to minimize predictive error variance. The contributions by different parameter types to the overall error variance of selected predictions are also examined.

  10. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...

  11. Precision Plate Plan View Pattern Predictive Model

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yang; YANG Quan; HE An-rui; WANG Xiao-chen; ZHANG Yun

    2011-01-01

    According to the rolling features of plate mill, a 3D elastic-plastic FEM (finite element model) based on full restart method of ANSYS/LS-DYNA was established to study the inhomogeneous plastic deformation of multipass plate rolling. By analyzing the simulation results, the difference of head and tail ends predictive models was found and modified. According to the numerical simulation results of 120 different kinds of conditions, precision plate plan view pattern predictive model was established. Based on these models, the sizing MAS (mizushima automatic plan view pattern control system) method was designed and used on a 2 800 mm plate mill. Comparing the rolled plates with and without PVPP (plan view pattern predictive) model, the reduced width deviation indicates that the olate !olan view Dattern predictive model is preeise.

  12. The Job Demands-Resources model as predictor of work identity and work engagement: A comparative analysis

    Directory of Open Access Journals (Sweden)

    Roslyn De Braine

    2011-05-01

    Full Text Available Orientation: Research shows that engaged employees experience high levels of energy and strong identification with their work, hence this study’s focus on work identity and dedication.Research purpose: This study explored possible differences in the Job Demands-Resources model (JD-R as predictor of overall work engagement, dedication only and work-based identity, through comparative predictive analyses.Motivation for the study: This study may shed light on the dedication component of work engagement. Currently no literature indicates that the JD-R model has been used to predict work-based identity.Research design: A census-based survey was conducted amongst a target population of 23134 employees that yielded a sample of 2429 (a response rate of about 10.5%. The Job Demands- Resources scale (JDRS was used to measure job demands and job resources. A work-based identity scale was developed for this study. Work engagement was studied with the Utrecht Work Engagement Scale (UWES. Factor and reliability analyses were conducted on the scales and general multiple regression models were used in the predictive analyses.Main findings: The JD-R model yielded a greater amount of variance in dedication than in work engagement. It, however, yielded the greatest amount of variance in work-based identity, with job resources being its strongest predictor.Practical/managerial implications: Identification and work engagement levels can be improved by managing job resources and demands.Contribution/value-add: This study builds on the literature of the JD-R model by showing that it can be used to predict work-based identity.

  13. Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

    Science.gov (United States)

    Osman, Marisol; Vera, C. S.

    2016-11-01

    This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to

  14. NBC Hazard Prediction Model Capability Analysis

    Science.gov (United States)

    1999-09-01

    Puff( SCIPUFF ) Model Verification and Evaluation Study, Air Resources Laboratory, NOAA, May 1998. Based on the NOAA review, the VLSTRACK developers...TO SUBSTANTIAL DIFFERENCES IN PREDICTIONS HPAC uses a transport and dispersion (T&D) model called SCIPUFF and an associated mean wind field model... SCIPUFF is a model for atmospheric dispersion that uses the Gaussian puff method - an arbitrary time-dependent concentration field is represented

  15. Motor threshold predicts working memory performance in healthy humans.

    Science.gov (United States)

    Schicktanz, Nathalie; Schwegler, Kyrill; Fastenrath, Matthias; Spalek, Klara; Milnik, Annette; Papassotiropoulos, Andreas; Nyffeler, Thomas; de Quervain, Dominique J-F

    2014-01-01

    Cognitive functions, such as working memory, depend on neuronal excitability in a distributed network of cortical regions. It is not known, however, if interindividual differences in cortical excitability are related to differences in working memory performance. In the present transcranial magnetic stimulation study, which included 188 healthy young subjects, we show that participants with lower resting motor threshold, which is related to higher corticospinal excitability, had increased 2-back working memory performance. The findings may help to better understand the link between cortical excitability and cognitive functions and may also have important clinical implications with regard to conditions of altered cortical excitability.

  16. MODELING WORK OF SORTING STATION USING UML

    Directory of Open Access Journals (Sweden)

    O. V. Gorbova

    2014-12-01

    Full Text Available Purpose. The purpose of this paper is the construction of methods and models for the graphical representation process of sorting station, using the unified modeling language (UML. Methodology. Methods of graph theory, finite automata and the representation theory of queuing systems were used as the methods of investigation. A graphical representation of the process was implemented with using the Unified Modeling Language UML. The sorting station process representation is implemented as a state diagram and actions through a set of IBM Rational Rose. Graphs can show parallel operation of sorting station, the parallel existence and influence of objects process and the transition from one state to another. The IBM Rational Rose complex allows developing a diagram of work sequence of varying degrees of detailing. Findings. The study has developed a graphical representation method of the process of sorting station of different kind of complexity. All graphical representations are made using the UML. They are represented as a directed graph with the states. It is clear enough in the study of the subject area. Applying the methodology of the representation process, it allows becoming friendly with the work of any automation object very fast, and exploring the process during algorithms construction of sorting stations and other railway facilities. This model is implemented with using the Unified Modeling Language (UML using a combination of IBM Rational Rose. Originality. The representation process of sorting station was developed by means of the Unified Modeling Language (UML use. Methodology of representation process allows creating the directed graphs based on the order of execution of the works chain, objects and performers of these works. The UML allows visualizing, specifying, constructing and documenting, formalizing the representation process of sorting station and developing sequence diagrams of works of varying degrees of detail. Practical

  17. A Fusion Model for CPU Load Prediction in Cloud Computing

    Directory of Open Access Journals (Sweden)

    Dayu Xu

    2013-11-01

    Full Text Available Load prediction plays a key role in cost-optimal resource allocation and datacenter energy saving. In this paper, we use real-world traces from Cloud platform and propose a fusion model to forecast the future CPU loads. First, long CPU load time series data are divided into short sequences with same length from the historical data on the basis of cloud control cycle. Then we use kernel fuzzy c-means clustering algorithm to put the subsequences into different clusters. For each cluster, with current load sequence, a genetic algorithm optimized wavelet Elman neural network prediction model is exploited to predict the CPU load in next time interval. Finally, we obtain the optimal cloud computing CPU load prediction results from the cluster and its corresponding predictor with minimum forecasting error. Experimental results show that our algorithm performs better than other models reported in previous works.

  18. Do workaholism and work engagement predict employee well-being and performance in opposite directions?

    Science.gov (United States)

    Shimazu, Akihito; Schaufeli, Wilmar B; Kubota, Kazumi; Kawakami, Norito

    2012-01-01

    This study investigated the distinctiveness between workaholism and work engagement by examining their longitudinal relationships (measurement interval=7 months) with well-being and performance in a sample of 1,967 Japanese employees from various occupations. Based on a previous cross-sectional study (Shimazu & Schaufeli, 2009), we expected that workaholism predicts future unwell-being (i.e., high ill-health and low life satisfaction) and poor job performance, whereas work engagement predicts future well-being (i.e., low ill-health and high life satisfaction) and superior job performance. T1-T2 changes in ill-health, life satisfaction and job performance were measured as residual scores that were then included in the structural equation model. Results showed that workaholism and work engagement were weakly and positively related to each other. In addition, workaholism was related to an increase in ill-health and to a decrease in life satisfaction. In contrast, work engagement was related to a decrease in ill-health and to increases in both life satisfaction and job performance. These findings suggest that workaholism and work engagement are two different kinds of concepts that are oppositely related to well-being and performance.

  19. Teaching mathematical modelling through project work

    DEFF Research Database (Denmark)

    Blomhøj, Morten; Kjeldsen, Tinne Hoff

    2006-01-01

    are reported in manners suitable for internet publication for colleagues. The reports and the related discussions reveal interesting dilemmas concerning the teaching of mathematical modelling and how to cope with these through “setting the scene” for the students modelling projects and through dialogues...... in their own classes, evaluate and report a project based problem oriented course in mathematical modelling. The in-service course runs over one semester and includes three seminars of 3, 1 and 2 days. Experiences show that the course objectives in general are fulfilled and that the course projects......The paper presents and analyses experiences from developing and running an in-service course in project work and mathematical modelling for mathematics teachers in the Danish gymnasium, e.g. upper secondary level, grade 10-12. The course objective is to support the teachers to develop, try out...

  20. Teaching mathematical modelling through project work

    DEFF Research Database (Denmark)

    Blomhøj, Morten; Kjeldsen, Tinne Hoff

    2006-01-01

    The paper presents and analyses experiences from developing and running an in-service course in project work and mathematical modelling for mathematics teachers in the Danish gymnasium, e.g. upper secondary level, grade 10-12. The course objective is to support the teachers to develop, try out...... in their own classes, evaluate and report a project based problem oriented course in mathematical modelling. The in-service course runs over one semester and includes three seminars of 3, 1 and 2 days. Experiences show that the course objectives in general are fulfilled and that the course projects...... are reported in manners suitable for internet publication for colleagues. The reports and the related discussions reveal interesting dilemmas concerning the teaching of mathematical modelling and how to cope with these through “setting the scene” for the students modelling projects and through dialogues...

  1. Corporate prediction models, ratios or regression analysis?

    NARCIS (Netherlands)

    Bijnen, E.J.; Wijn, M.F.C.M.

    1994-01-01

    The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in

  2. Modelling Chemical Reasoning to Predict Reactions

    CERN Document Server

    Segler, Marwin H S

    2016-01-01

    The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180,000 randomly selected binary reactions. We show that our data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-) discovering novel transformations (even including transition-metal catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph, and because each single reaction prediction is typically ac...

  3. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  4. A Work Psychological Model that Works: Expanding the Job Demands-Resources Model

    NARCIS (Netherlands)

    Xanthopoulou, D.

    2007-01-01

    The main purpose of the current thesis was to test and expand the recently developed Job Demands-Resources (JD-R) model. The advantage of this model is that it recognizes the uniqueness of each work environment, which has its own specific job demands and job resources. According to the JD-R model, j

  5. A Work Psychological Model that Works: Expanding the Job Demands-Resources Model

    NARCIS (Netherlands)

    Xanthopoulou, D.

    2007-01-01

    The main purpose of the current thesis was to test and expand the recently developed Job Demands-Resources (JD-R) model. The advantage of this model is that it recognizes the uniqueness of each work environment, which has its own specific job demands and job resources. According to the JD-R model,

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

    Directory of Open Access Journals (Sweden)

    E Mohammadi Zeydi

    2008-12-01

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

  7. Genetic models of homosexuality: generating testable predictions

    OpenAIRE

    Gavrilets, Sergey; Rice, William R.

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality inclu...

  8. Evaluation of the usefulness of 2 prediction models of clinical prediction models in physical therapy: a qualitative process evaluation.

    NARCIS (Netherlands)

    Oort, L. van; Verhagen, A.F.; Koes, B.; Vet, R. de; Anema, H.; Heymans, M.

    2014-01-01

    OBJECTIVE: The purposes of this study were to (1) evaluate the usefulness of 2 prediction models by assessing the actual use and advantages/disadvantages of application in daily clinical practice and (2) propose recommendations to enhance their implementation. METHODS: Physical therapists working in

  9. Towards an Islamic model of work motivation

    Directory of Open Access Journals (Sweden)

    Akram Abdul Cader

    2016-07-01

    Full Text Available Optimal motivation (al-himmah al-‘āliyyah is an important concept in Islamic psychology. Current Islamic models predominantly focus on integration with Western theories. This study proposes a synthesised model of Islamic motivation through an interpretive approach of Islamic theological texts (Qur’ān and Sunnah, classical Islamic works, and a systematic analysis of Western academic research. Islamic work motivation focuses on states of the nafs (self: al-nafs al-muṭma’innah (tranquil, al-nafs al-lawwāmah (self-reproaching, and al-nafs al-ammārah bi-al-sū’ (inclined to evil. Tawḥīd (monotheism, mediated by sincerity and Sunnah compliance, drives īmān (belief. Optimal motivation is a result of strengthened īmān moderated by knowledge, patience, reliance, piety, encouragement, and admonishment. The resultant action, integrated with a rewards/punishment system, yields motivated behaviour. Motivated behaviour is classified in three behavioural types: ẓālim li-nafsih (self-oppressive, muqtaṣid (moderate, and sābiq bi-al-khayrāt (foremost in good. Optimal motivation is the state of tranquillity where the individual sincerely strives towards good action. The model provides practitioners with a model that can be used to manage motivation and provides researchers a comprehensive framework of Islamic motivation.

  10. Wind farm production prediction - The Zephyr model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Giebel, G. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Madsen, H. [IMM (DTU), Kgs. Lyngby (Denmark); Nielsen, T.S. [IMM (DTU), Kgs. Lyngby (Denmark); Joergensen, J.U. [Danish Meteorologisk Inst., Copenhagen (Denmark); Lauersen, L. [Danish Meteorologisk Inst., Copenhagen (Denmark); Toefting, J. [Elsam, Fredericia (DK); Christensen, H.S. [Eltra, Fredericia (Denmark); Bjerge, C. [SEAS, Haslev (Denmark)

    2002-06-01

    This report describes a project - funded by the Danish Ministry of Energy and the Environment - which developed a next generation prediction system called Zephyr. The Zephyr system is a merging between two state-of-the-art prediction systems: Prediktor of Risoe National Laboratory and WPPT of IMM at the Danish Technical University. The numerical weather predictions were generated by DMI's HIRLAM model. Due to technical difficulties programming the system, only the computational core and a very simple version of the originally very complex system were developed. The project partners were: Risoe, DMU, DMI, Elsam, Eltra, Elkraft System, SEAS and E2. (au)

  11. Work Ethic and Academic Performance: Predicting Citizenship and Counterproductive Behavior

    Science.gov (United States)

    Meriac, John P.

    2012-01-01

    In this study, work ethic was examined as a predictor of academic performance, compared with standardized test scores and high school grade point average (GPA). Academic performance was expanded to include student organizational citizenship behavior (OCB) and student counterproductive behavior, comprised of cheating and disengagement, in addition…

  12. Predicting Behavior from Cognitive Cause Maps of a Work Setting.

    Science.gov (United States)

    Komocar, John

    Cognitive cause maps permit a topological investigation of the complexity of organizational events and behaviors. Because cognitive cause maps are believed to be ordered according to a givens-means-ends schema, they contain information about an individual's motivation structure. In a work setting an individual engages in several different acts.…

  13. Work Performance: A New Approach to Expectancy Theory Predictions

    Science.gov (United States)

    1976-09-01

    an organization is capable of affecting work produc- tivity by influencing employee motivation . Currently, the dominant approach to the study of... employee motivation and performance is that of expectancy theory. Despite its wide usage, expectancy theory has not been as successful in accounting

  14. Working memory capacity predicts conflict-task performance

    NARCIS (Netherlands)

    Gulbinaite, Rasa; Johnson, Addie

    2014-01-01

    The relationship between the ability to maintain task goals and working memory capacity (WMC) is firmly established, but evidence for WMC-related differences in conflict processing is mixed. We investigated whether WMC (measured using two complex-span tasks) mediates differences in adjustments of co

  15. On Practical tuning of Model Uncertainty in Wind Turbine Model Predictive Control

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Hovgaard, Tobias

    2015-01-01

    Model predictive control (MPC) has in previous works been applied on wind turbines with promising results. These results apply linear MPC, i.e., linear models linearized at different operational points depending on the wind speed. The linearized models are derived from a nonlinear first principle...

  16. Predicting the Yield Stress of SCC using Materials Modelling

    DEFF Research Database (Denmark)

    Thrane, Lars Nyholm; Hasholt, Marianne Tange; Pade, Claus

    2005-01-01

    A conceptual model for predicting the Bingham rheological parameter yield stress of SCC has been established. The model used here is inspired by previous work of Oh et al. (1), predicting that the yield stress of concrete relative to the yield stress of paste is a function of the relative thickness...... of excess paste around the aggregate. The thickness of excess paste is itself a function of particle shape, particle size distribution, and particle packing. Seven types of SCC were tested at four different excess paste contents in order to verify the conceptual model. Paste composition and aggregate shape...... and distribution were varied between SCC types. The results indicate that yield stress of SCC may be predicted using the model....

  17. Physics-Informed Machine Learning for Predictive Turbulence Modeling: A Priori Assessment of Prediction Confidence

    CERN Document Server

    Wu, Jin-Long; Xiao, Heng; Ling, Julia

    2016-01-01

    Although Reynolds-Averaged Navier-Stokes (RANS) equations are still the dominant tool for engineering design and analysis applications involving turbulent flows, standard RANS models are known to be unreliable in many flows of engineering relevance, including flows with separation, strong pressure gradients or mean flow curvature. With increasing amounts of 3-dimensional experimental data and high fidelity simulation data from Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS), data-driven turbulence modeling has become a promising approach to increase the predictive capability of RANS simulations. Recently, a data-driven turbulence modeling approach via machine learning has been proposed to predict the Reynolds stress anisotropy of a given flow based on high fidelity data from closely related flows. In this work, the closeness of different flows is investigated to assess the prediction confidence a priori. Specifically, the Mahalanobis distance and the kernel density estimation (KDE) technique...

  18. Predictive model for segmented poly(urea

    Directory of Open Access Journals (Sweden)

    Frankl P.

    2012-08-01

    Full Text Available Segmented poly(urea has been shown to be of significant benefit in protecting vehicles from blast and impact and there have been several experimental studies to determine the mechanisms by which this protective function might occur. One suggested route is by mechanical activation of the glass transition. In order to enable design of protective structures using this material a constitutive model and equation of state are needed for numerical simulation hydrocodes. Determination of such a predictive model may also help elucidate the beneficial mechanisms that occur in polyurea during high rate loading. The tool deployed to do this has been Group Interaction Modelling (GIM – a mean field technique that has been shown to predict the mechanical and physical properties of polymers from their structure alone. The structure of polyurea has been used to characterise the parameters in the GIM scheme without recourse to experimental data and the equation of state and constitutive model predicts response over a wide range of temperatures and strain rates. The shock Hugoniot has been predicted and validated against existing data. Mechanical response in tensile tests has also been predicted and validated.

  19. Working memory capacity predicts effects of methylphenidate on reversal learning.

    Science.gov (United States)

    van der Schaaf, Marieke E; Fallon, Sean J; Ter Huurne, Niels; Buitelaar, Jan; Cools, Roshan

    2013-09-01

    Increased use of stimulant medication, such as methylphenidate, by healthy college students has raised questions about its cognitive-enhancing effects. Methylphenidate acts by increasing extracellular catecholamine levels and is generally accepted to remediate cognitive and reward deficits in patients with attention deficit hyperactivity disorder. However, the cognitive-enhancing effects of such 'smart drugs' in the healthy population are still unclear. Here, we investigated effects of methylphenidate (Ritalin, 20  mg) on reward and punishment learning in healthy students (N=19) in a within-subject, double-blind, placebo-controlled cross-over design. Results revealed that methylphenidate effects varied both as a function of task demands and as a function of baseline working memory capacity. Specifically, methylphenidate improved reward vs punishment learning in high-working memory subjects, whereas it impaired reward vs punishment learning in low-working memory subjects. These results contribute to our understanding of individual differences in the cognitive-enhancing effects of methylphenidate in the healthy population. Moreover, they highlight the importance of taking into account both inter- and intra-individual differences in dopaminergic drug research.

  20. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro

    2016-03-01

    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  1. Predictive QSAR modeling of phosphodiesterase 4 inhibitors.

    Science.gov (United States)

    Kovalishyn, Vasyl; Tanchuk, Vsevolod; Charochkina, Larisa; Semenuta, Ivan; Prokopenko, Volodymyr

    2012-02-01

    A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development. Copyright © 2011 Elsevier Inc. All rights reserved.

  2. Support vector machine-based multi-model predictive control

    Institute of Scientific and Technical Information of China (English)

    Zhejing BA; Youxian SUN

    2008-01-01

    In this paper,a support vector machine-based multi-model predictive control is proposed,in which SVM classification combines well with SVM regression.At first,each working environment is modeled by SVM regression and the support vector machine network-based model predictive control(SVMN-MPC)algorithm corresponding to each environment is developed,and then a multi-class SVM model is established to recognize multiple operating conditions.As for control,the current environment is identified by the multi-class SVM model and then the corresponding SVMN.MPCcontroller is activated at each sampling instant.The proposed modeling,switching and controller design is demonstrated in simulation results.

  3. Robust Model Predictive Control of a Wind Turbine

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2012-01-01

    In this work the problem of robust model predictive control (robust MPC) of a wind turbine in the full load region is considered. A minimax robust MPC approach is used to tackle the problem. Nonlinear dynamics of the wind turbine are derived by combining blade element momentum (BEM) theory...... and first principle modeling of the turbine flexible structure. Thereafter the nonlinear model is linearized using Taylor series expansion around system operating points. Operating points are determined by effective wind speed and an extended Kalman filter (EKF) is employed to estimate this. In addition...... of the uncertain system is employed and a norm-bounded uncertainty model is used to formulate a minimax model predictive control. The resulting optimization problem is simplified by semidefinite relaxation and the controller obtained is applied on a full complexity, high fidelity wind turbine model. Finally...

  4. Predicting human walking gaits with a simple planar model.

    Science.gov (United States)

    Martin, Anne E; Schmiedeler, James P

    2014-04-11

    Models of human walking with moderate complexity have the potential to accurately capture both joint kinematics and whole body energetics, thereby offering more simultaneous information than very simple models and less computational cost than very complex models. This work examines four- and six-link planar biped models with knees and rigid circular feet. The two differ in that the six-link model includes ankle joints. Stable periodic walking gaits are generated for both models using a hybrid zero dynamics-based control approach. To establish a baseline of how well the models can approximate normal human walking, gaits were optimized to match experimental human walking data, ranging in speed from very slow to very fast. The six-link model well matched the experimental step length, speed, and mean absolute power, while the four-link model did not, indicating that ankle work is a critical element in human walking models of this type. Beyond simply matching human data, the six-link model can be used in an optimization framework to predict normal human walking using a torque-squared objective function. The model well predicted experimental step length, joint motions, and mean absolute power over the full range of speeds.

  5. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-02-01

    Full Text Available Orientation: The article discussed the importance of rigour in credit risk assessment.Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan.Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities.Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems.Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk.Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product.Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  6. Modelling language evolution: Examples and predictions.

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  7. Modelling language evolution: Examples and predictions

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  8. Model Predictive Control of Sewer Networks

    Science.gov (United States)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.

    2017-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.

  9. Frequency weighted model predictive control of wind turbine

    DEFF Research Database (Denmark)

    Klauco, Martin; Poulsen, Niels Kjølstad; Mirzaei, Mahmood;

    2013-01-01

    This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work...... are the rotational degree of freedom of the rotor and the tower for-aft movement. The MPC design is based on a receding horizon policy and a linearised model of the wind turbine. Due to the change of dynamics according to wind speed, several linearisation points must be considered and the control design adjusted...... accordingly. In practice is very hard to measure the effective wind speed, this quantity will be estimated using measurements from the turbine itself. For this purpose stationary predictive Kalman filter has been used. Stochastic simulations of the wind turbine behaviour with applied frequency weighted model...

  10. DKIST Polarization Modeling and Performance Predictions

    Science.gov (United States)

    Harrington, David

    2016-05-01

    Calibrating the Mueller matrices of large aperture telescopes and associated coude instrumentation requires astronomical sources and several modeling assumptions to predict the behavior of the system polarization with field of view, altitude, azimuth and wavelength. The Daniel K Inouye Solar Telescope (DKIST) polarimetric instrumentation requires very high accuracy calibration of a complex coude path with an off-axis f/2 primary mirror, time dependent optical configurations and substantial field of view. Polarization predictions across a diversity of optical configurations, tracking scenarios, slit geometries and vendor coating formulations are critical to both construction and contined operations efforts. Recent daytime sky based polarization calibrations of the 4m AEOS telescope and HiVIS spectropolarimeter on Haleakala have provided system Mueller matrices over full telescope articulation for a 15-reflection coude system. AEOS and HiVIS are a DKIST analog with a many-fold coude optical feed and similar mirror coatings creating 100% polarization cross-talk with altitude, azimuth and wavelength. Polarization modeling predictions using Zemax have successfully matched the altitude-azimuth-wavelength dependence on HiVIS with the few percent amplitude limitations of several instrument artifacts. Polarization predictions for coude beam paths depend greatly on modeling the angle-of-incidence dependences in powered optics and the mirror coating formulations. A 6 month HiVIS daytime sky calibration plan has been analyzed for accuracy under a wide range of sky conditions and data analysis algorithms. Predictions of polarimetric performance for the DKIST first-light instrumentation suite have been created under a range of configurations. These new modeling tools and polarization predictions have substantial impact for the design, fabrication and calibration process in the presence of manufacturing issues, science use-case requirements and ultimate system calibration

  11. Using repeated measures of sleep disturbances to predict future diagnosis-specific work disability

    DEFF Research Database (Denmark)

    Salo, Paula; Vahtera, Jussi; Hall, Martica

    2012-01-01

    It is unknown whether or not measuring sleep disturbances repeatedly, rather than at only one point in time, improves prediction of work disability.......It is unknown whether or not measuring sleep disturbances repeatedly, rather than at only one point in time, improves prediction of work disability....

  12. Modelling Chemical Reasoning to Predict Reactions

    OpenAIRE

    Segler, Marwin H. S.; Waller, Mark P.

    2016-01-01

    The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outpe...

  13. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert; Knox, James

    2016-01-01

    Fully predictive models of the Four Bed Molecular Sieve of the Carbon Dioxide Removal Assembly on the International Space Station are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  14. Raman Model Predicting Hardness of Covalent Crystals

    OpenAIRE

    Zhou, Xiang-Feng; Qian, Quang-Rui; Sun, Jian; Tian, Yongjun; Wang, Hui-Tian

    2009-01-01

    Based on the fact that both hardness and vibrational Raman spectrum depend on the intrinsic property of chemical bonds, we propose a new theoretical model for predicting hardness of a covalent crystal. The quantitative relationship between hardness and vibrational Raman frequencies deduced from the typical zincblende covalent crystals is validated to be also applicable for the complex multicomponent crystals. This model enables us to nondestructively and indirectly characterize the hardness o...

  15. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts

  16. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

  17. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts produ

  18. Prediction modelling for population conviction data

    NARCIS (Netherlands)

    Tollenaar, N.

    2017-01-01

    In this thesis, the possibilities of using prediction models for judicial penal case data are investigated. The development and refinement of a risk taxation scale based on these data is discussed. When false positives are weighted equally severe as false negatives, 70% can be classified correctly.

  19. Predictability of extreme values in geophysical models

    NARCIS (Netherlands)

    Sterk, A.E.; Holland, M.P.; Rabassa, P.; Broer, H.W.; Vitolo, R.

    2012-01-01

    Extreme value theory in deterministic systems is concerned with unlikely large (or small) values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical model

  20. A revised prediction model for natural conception

    NARCIS (Netherlands)

    Bensdorp, A.J.; Steeg, J.W. van der; Steures, P.; Habbema, J.D.; Hompes, P.G.; Bossuyt, P.M.; Veen, F. van der; Mol, B.W.; Eijkemans, M.J.; Kremer, J.A.M.; et al.,

    2017-01-01

    One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis

  1. Distributed Model Predictive Control via Dual Decomposition

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle

    2014-01-01

    This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...

  2. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts produ

  3. Leptogenesis in minimal predictive seesaw models

    CERN Document Server

    Björkeroth, Fredrik; Varzielas, Ivo de Medeiros; King, Stephen F

    2015-01-01

    We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the "atmospheric" and "solar" neutrino masses with Yukawa couplings to $(\

  4. Structure-Based Predictive model for Coal Char Combustion.

    Energy Technology Data Exchange (ETDEWEB)

    Hurt, R.; Colo, J [Brown Univ., Providence, RI (United States). Div. of Engineering; Essenhigh, R.; Hadad, C [Ohio State Univ., Columbus, OH (United States). Dept. of Chemistry; Stanley, E. [Boston Univ., MA (United States). Dept. of Physics

    1997-09-24

    During the third quarter of this project, progress was made on both major technical tasks. Progress was made in the chemistry department at OSU on the calculation of thermodynamic properties for a number of model organic compounds. Modelling work was carried out at Brown to adapt a thermodynamic model of carbonaceous mesophase formation, originally applied to pitch carbonization, to the prediction of coke texture in coal combustion. This latter work makes use of the FG-DVC model of coal pyrolysis developed by Advanced Fuel Research to specify the pool of aromatic clusters that participate in the order/disorder transition. This modelling approach shows promise for the mechanistic prediction of the rank dependence of char structure and will therefore be pursued further. Crystalline ordering phenomena were also observed in a model char prepared from phenol-formaldehyde carbonized at 900{degrees}C and 1300{degrees}C using high-resolution TEM fringe imaging. Dramatic changes occur in the structure between 900 and 1300{degrees}C, making this char a suitable candidate for upcoming in situ work on the hot stage TEM. Work also proceeded on molecular dynamics simulations at Boston University and on equipment modification and testing for the combustion experiments with widely varying flame types at Ohio State.

  5. Specialized Language Models using Dialogue Predictions

    CERN Document Server

    Popovici, C; Popovici, Cosmin; Baggia, Paolo

    1996-01-01

    This paper analyses language modeling in spoken dialogue systems for accessing a database. The use of several language models obtained by exploiting dialogue predictions gives better results than the use of a single model for the whole dialogue interaction. For this reason several models have been created, each one for a specific system question, such as the request or the confirmation of a parameter. The use of dialogue-dependent language models increases the performance both at the recognition and at the understanding level, especially on answers to system requests. Moreover other methods to increase performance, like automatic clustering of vocabulary words or the use of better acoustic models during recognition, does not affect the improvements given by dialogue-dependent language models. The system used in our experiments is Dialogos, the Italian spoken dialogue system used for accessing railway timetable information over the telephone. The experiments were carried out on a large corpus of dialogues coll...

  6. A prediction model for ocular damage - Experimental validation.

    Science.gov (United States)

    Heussner, Nico; Vagos, Márcia; Spitzer, Martin S; Stork, Wilhelm

    2015-08-01

    With the increasing number of laser applications in medicine and technology, accidental as well as intentional exposure of the human eye to laser sources has become a major concern. Therefore, a prediction model for ocular damage (PMOD) is presented within this work and validated for long-term exposure. This model is a combination of a raytracing model with a thermodynamical model of the human and an application which determines the thermal damage by the implementation of the Arrhenius integral. The model is based on our earlier work and is here validated against temperature measurements taken with porcine eye samples. For this validation, three different powers were used: 50mW, 100mW and 200mW with a spot size of 1.9mm. Also, the measurements were taken with two different sensing systems, an infrared camera and a fibre optic probe placed within the tissue. The temperatures were measured up to 60s and then compared against simulations. The measured temperatures were found to be in good agreement with the values predicted by the PMOD-model. To our best knowledge, this is the first model which is validated for both short-term and long-term irradiations in terms of temperature and thus demonstrates that temperatures can be accurately predicted within the thermal damage regime. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Caries risk assessment models in caries prediction

    Directory of Open Access Journals (Sweden)

    Amila Zukanović

    2013-11-01

    Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.

  8. Disease prediction models and operational readiness.

    Directory of Open Access Journals (Sweden)

    Courtney D Corley

    Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology

  9. Working memory predicts the rejection of false memories.

    Science.gov (United States)

    Leding, Juliana K

    2012-01-01

    The relationship between working memory capacity (WMC) and false memories in the memory conjunction paradigm was explored. Previous research using other paradigms has shown that individuals high in WMC are not as likely to experience false memories as low-WMC individuals, the explanation being that high-WMC individuals are better able to engage in source monitoring. In the memory conjunction paradigm participants are presented at study with parent words (e.g., eyeglasses, whiplash). At test, in addition to being presented with targets and foils, participants are presented with lures that are composed of previously studied features (e.g., eyelash). It was found that high-WMC individuals had lower levels of false recognition than low-WMC individuals. Furthermore, recall-to-reject responses were analysed (e.g., "I know I didn't see eyelash because I remember seeing eyeglasses") and it was found that high-WMC individuals were more likely to utilise this memory editing strategy, providing direct evidence that one reason that high-WMC individuals are not as prone to false memories is because they are better able to engage in source monitoring.

  10. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...

  11. Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

    Science.gov (United States)

    Curtis, Gary P.; Lu, Dan; Ye, Ming

    2015-01-01

    While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. This study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. These reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Limitations of applying MLBMA to the

  12. Towards predictive food process models: A protocol for parameter estimation.

    Science.gov (United States)

    Vilas, Carlos; Arias-Méndez, Ana; Garcia, Miriam R; Alonso, Antonio A; Balsa-Canto, E

    2016-05-31

    Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control. The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties. This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods.

  13. Distinct work-related, clinical and psychological factors predict return to work following treatment in four different cancer types.

    Science.gov (United States)

    Cooper, Alethea F; Hankins, Matthew; Rixon, Lorna; Eaton, Emma; Grunfeld, Elizabeth A

    2013-03-01

    Many factors influence return to work (RTW) following cancer treatment. However specific factors affecting RTW across different cancer types are unclear. This study examined the role of clinical, sociodemographic, work and psychological factors in RTW following treatment for breast, gynaecological, head and neck, and urological cancer. A 12-month prospective questionnaire study was conducted with 290 patients. Cox regression analyses were conducted to calculate hazard ratios (HR) for time to RTW. Between 89-94% of cancer survivors returned to work. Breast cancer survivors took the longest to return (median 30 weeks), and urology cancer survivors returned the soonest (median 5 weeks). Earlier return among breast cancer survivors was predicted by a greater sense of control over their cancer at work (HR 1.2; 95% CI: 1.09-1.37) and by full-time work (HR 2.1; CI: 1.24-3.4). Predictive of a longer return among gynaecological cancer survivors was a belief that cancer treatment may impair ability to work (HR 0.75; CI: 0.62-0.91). Among urological cancer survivors constipation was predictive of longer RTW (HR 0.99; CI: 0.97-1.00), whereas undertaking flexible working was predictive of returning sooner (HR 1.70; CI: 1.07-2.7). Head and neck cancer survivors who perceived greater negative consequences of their cancer took longer to return (HR 0.27; CI: 0.11-0.68). Those reporting better physical functioning returned sooner (HR1.04; CI: 1.01-1.08). A different profile of predictive factors emerged for the four cancer types. In addition to optimal symptom management and workplace adaptations, the findings suggest that eliciting and challenging specific cancer and treatment-related perceptions may facilitate RTW. Copyright © 2012 John Wiley & Sons, Ltd.

  14. Ionosphere monitoring and forecast activities within the IAG working group "Ionosphere Prediction"

    Science.gov (United States)

    Hoque, Mainul; Garcia-Rigo, Alberto; Erdogan, Eren; Cueto Santamaría, Marta; Jakowski, Norbert; Berdermann, Jens; Hernandez-Pajares, Manuel; Schmidt, Michael; Wilken, Volker

    2017-04-01

    Ionospheric disturbances can affect technologies in space and on Earth disrupting satellite and airline operations, communications networks, navigation systems. As the world becomes ever more dependent on these technologies, ionospheric disturbances as part of space weather pose an increasing risk to the economic vitality and national security. Therefore, having the knowledge of ionospheric state in advance during space weather events is becoming more and more important. To promote scientific cooperation we recently formed a Working Group (WG) called "Ionosphere Predictions" within the International Association of Geodesy (IAG) under Sub-Commission 4.3 "Atmosphere Remote Sensing" of the Commission 4 "Positioning and Applications". The general objective of the WG is to promote the development of ionosphere prediction algorithm/models based on the dependence of ionospheric characteristics on solar and magnetic conditions combining data from different sensors to improve the spatial and temporal resolution and sensitivity taking advantage of different sounding geometries and latency. Our presented work enables the possibility to compare total electron content (TEC) prediction approaches/results from different centers contributing to this WG such as German Aerospace Center (DLR), Universitat Politècnica de Catalunya (UPC), Technische Universität München (TUM) and GMV. DLR developed a model-assisted TEC forecast algorithm taking benefit from actual trends of the TEC behavior at each grid point. Since during perturbations, characterized by large TEC fluctuations or ionization fronts, this approach may fail, the trend information is merged with the current background model which provides a stable climatological TEC behavior. The presented solution is a first step to regularly provide forecasted TEC services via SWACI/IMPC by DLR. UPC forecast model is based on applying linear regression to a temporal window of TEC maps in the Discrete Cosine Transform (DCT) domain

  15. Burnout and Work Demands Predict Reduced Job Satisfaction in Health Professionals Working In a Surgery Clinic

    Directory of Open Access Journals (Sweden)

    Dragan Mijakoski

    2015-03-01

    CONCLUSIONS: Adequate management of work demands, particularly excessive workload, time pressure, and lack of staff can lead to prevention of burnout and reduced job satisfaction in surgery clinic HPs, and contribute to better quality of patient care.

  16. ENSO Prediction using Vector Autoregressive Models

    Science.gov (United States)

    Chapman, D. R.; Cane, M. A.; Henderson, N.; Lee, D.; Chen, C.

    2013-12-01

    A recent comparison (Barnston et al, 2012 BAMS) shows the ENSO forecasting skill of dynamical models now exceeds that of statistical models, but the best statistical models are comparable to all but the very best dynamical models. In this comparison the leading statistical model is the one based on the Empirical Model Reduction (EMR) method. Here we report on experiments with multilevel Vector Autoregressive models using only sea surface temperatures (SSTs) as predictors. VAR(L) models generalizes Linear Inverse Models (LIM), which are a VAR(1) method, as well as multilevel univariate autoregressive models. Optimal forecast skill is achieved using 12 to 14 months of prior state information (i.e 12-14 levels), which allows SSTs alone to capture the effects of other variables such as heat content as well as seasonality. The use of multiple levels allows the model advancing one month at a time to perform at least as well for a 6 month forecast as a model constructed to explicitly forecast 6 months ahead. We infer that the multilevel model has fully captured the linear dynamics (cf. Penland and Magorian, 1993 J. Climate). Finally, while VAR(L) is equivalent to L-level EMR, we show in a 150 year cross validated assessment that we can increase forecast skill by improving on the EMR initialization procedure. The greatest benefit of this change is in allowing the prediction to make effective use of information over many more months.

  17. Why Does Working Memory Capacity Predict Variation in Reading Comprehension? On the Influence of Mind Wandering and Executive Attention

    OpenAIRE

    McVay, Jennifer C.; Kane, Michael J.

    2011-01-01

    Some people are better readers than others, and this variation in comprehension ability is predicted by measures of working memory capacity (WMC). The primary goal of this study was to investigate the mediating role of mind wandering experiences in the association between WMC and normal individual differences in reading comprehension, as predicted by the executive-attention theory of WMC (e.g., Engle & Kane, 2004). We used a latent-variable, structural-equation-model approach, testing skilled...

  18. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

    2014-02-15

    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  19. Gas explosion prediction using CFD models

    Energy Technology Data Exchange (ETDEWEB)

    Niemann-Delius, C.; Okafor, E. [RWTH Aachen Univ. (Germany); Buhrow, C. [TU Bergakademie Freiberg Univ. (Germany)

    2006-07-15

    A number of CFD models are currently available to model gaseous explosions in complex geometries. Some of these tools allow the representation of complex environments within hydrocarbon production plants. In certain explosion scenarios, a correction is usually made for the presence of buildings and other complexities by using crude approximations to obtain realistic estimates of explosion behaviour as can be found when predicting the strength of blast waves resulting from initial explosions. With the advance of computational technology, and greater availability of computing power, computational fluid dynamics (CFD) tools are becoming increasingly available for solving such a wide range of explosion problems. A CFD-based explosion code - FLACS can, for instance, be confidently used to understand the impact of blast overpressures in a plant environment consisting of obstacles such as buildings, structures, and pipes. With its porosity concept representing geometry details smaller than the grid, FLACS can represent geometry well, even when using coarse grid resolutions. The performance of FLACS has been evaluated using a wide range of field data. In the present paper, the concept of computational fluid dynamics (CFD) and its application to gas explosion prediction is presented. Furthermore, the predictive capabilities of CFD-based gaseous explosion simulators are demonstrated using FLACS. Details about the FLACS-code, some extensions made to FLACS, model validation exercises, application, and some results from blast load prediction within an industrial facility are presented. (orig.)

  20. Genetic models of homosexuality: generating testable predictions.

    Science.gov (United States)

    Gavrilets, Sergey; Rice, William R

    2006-12-22

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism.

  1. A Study On Distributed Model Predictive Consensus

    CERN Document Server

    Keviczky, Tamas

    2008-01-01

    We investigate convergence properties of a proposed distributed model predictive control (DMPC) scheme, where agents negotiate to compute an optimal consensus point using an incremental subgradient method based on primal decomposition as described in Johansson et al. [2006, 2007]. The objective of the distributed control strategy is to agree upon and achieve an optimal common output value for a group of agents in the presence of constraints on the agent dynamics using local predictive controllers. Stability analysis using a receding horizon implementation of the distributed optimal consensus scheme is performed. Conditions are given under which convergence can be obtained even if the negotiations do not reach full consensus.

  2. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    R. G. SILVA

    1999-03-01

    Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.

  3. Injury severity measures for predicting return-to-work after a traumatic brain injury.

    Science.gov (United States)

    Chien, Ding-Kuo; Hwang, Hei-Fen; Lin, Mau-Roung

    2017-01-01

    This study compared the ability of five injury severity measures, namely the Abbreviated Injury Scale to the Head (AIS-H), Glasgow Coma Scale (GCS), Glasgow Outcome Scale (GOS), Extended Glasgow Outcome Scale (GOSE), and Injury Severity Score (ISS), to predict return-to-work after a traumatic brain injury (TBI). Furthermore, factors potentially associated with return-to-work were investigated. In total, 207 individuals aged ≤65 years newly diagnosed with a TBI and employed at the time of injury were recruited and followed-up for 1year by telephone every 3 months. A bivariate proportional hazards model analysis revealed that all five injury severity measures were significantly associated with return-to-work after a TBI. The AIS-H and non-head ISS explained 23.8% of the variation in the duration of returning to work from discharge after hospitalization for a TBI; similarly, the GCS, GOS, GOSE, and ISS respectively accounted for 4.7%, 21.4%, 12.9%, and 48.4% of the variation. A multivariable analysis revealed that individuals with higher injury severity as measured by the ISS (hazard ratio [HR], 0.94; 95% confidence interval [CI], 0.92-0.97), a lack of autonomy in transportation (HR, 2.55; 95% CI, 1.23-5.32), cognitive impairment (HR, 0.47; 95% CI, 0.28-0.79), and depression (HR, 0.97; 95% CI, 0.95-0.99) were significantly less likely to be employed after a TBI. In conclusion, of the five injury severity measures, the ISS may be the most capable measure of predicting return-to-work after a TBI. In addition to injury severity, autonomy in transportation, cognitive function, and the depressive status may also influence the employment status during the first year after a TBI.

  4. Caspase Work Model During Pathogen Infection

    Institute of Scientific and Technical Information of China (English)

    Yah-bin Ma; Hui-yun Chang

    2011-01-01

    Caspases are an evolutionarily conserved family of aspartate-specific cystein-dependent proteases with essential functions in apoptosis and normally exist in ceils as inactive proenzymes.In addition to the inflammatory caspases,the initiator and effector caspases have been shown to have an important role in regulating the immune response,but are involved in different ways.We give a brief introduction on the benefit of apoptosis on the clearance of invasive pathogens,and the caspase functions involved in the immune response.Then we construct a working model of caspases during pathogen invasion.A detailed description of the three modes is given in the discussion.These three modes are regulated by different inhibitors,and there may be a novel way to treat intracellular pathogen and autoimmune diseases based on the specific inhibitors.

  5. Modeling the prediction of business intelligence system effectiveness.

    Science.gov (United States)

    Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

    2016-01-01

    Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

  6. Performance model to predict overall defect density

    Directory of Open Access Journals (Sweden)

    J Venkatesh

    2012-08-01

    Full Text Available Management by metrics is the expectation from the IT service providers to stay as a differentiator. Given a project, the associated parameters and dynamics, the behaviour and outcome need to be predicted. There is lot of focus on the end state and in minimizing defect leakage as much as possible. In most of the cases, the actions taken are re-active. It is too late in the life cycle. Root cause analysis and corrective actions can be implemented only to the benefit of the next project. The focus has to shift left, towards the execution phase than waiting for lessons to be learnt post the implementation. How do we pro-actively predict defect metrics and have a preventive action plan in place. This paper illustrates the process performance model to predict overall defect density based on data from projects in an organization.

  7. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  8. COGNITIVE MODELS OF PREDICTION THE DEVELOPMENT OF A DIVERSIFIED CORPORATION

    Directory of Open Access Journals (Sweden)

    Baranovskaya T. P.

    2016-10-01

    Full Text Available The application of classical forecasting methods applied to a diversified corporation faces some certain difficulties, due to its economic nature. Unlike other businesses, diversified corporations are characterized by multidimensional arrays of data with a high degree of distortion and fragmentation of information due to the cumulative effect of the incompleteness and distortion of accounting information from the enterprises in it. Under these conditions, the applied methods and tools must have high resolution and work effectively with large databases with incomplete information, ensure the correct common comparable quantitative processing of the heterogeneous nature of the factors measured in different units. It is therefore necessary to select or develop some methods that can work with complex poorly formalized tasks. This fact substantiates the relevance of the problem of developing models, methods and tools for solving the problem of forecasting the development of diversified corporations. This is the subject of this work, which makes it relevant. The work aims to: 1 analyze the forecasting methods to justify the choice of system-cognitive analysis as one of the effective methods for the prediction of semi-structured tasks; 2 to adapt and develop the method of systemic-cognitive analysis for forecasting of dynamics of development of the corporation subject to the scenario approach; 3 to develop predictive model scenarios of changes in basic economic indicators of development of the corporation and to assess their credibility; 4 determine the analytical form of the dependence between past and future scenarios of various economic indicators; 5 develop analytical models weighing predictable scenarios, taking into account all prediction results with positive levels of similarity, to increase the level of reliability of forecasts; 6 to develop a calculation procedure to assess the strength of influence on the corporation (sensitivity of its

  9. Pressure prediction model for compression garment design.

    Science.gov (United States)

    Leung, W Y; Yuen, D W; Ng, Sun Pui; Shi, S Q

    2010-01-01

    Based on the application of Laplace's law to compression garments, an equation for predicting garment pressure, incorporating the body circumference, the cross-sectional area of fabric, applied strain (as a function of reduction factor), and its corresponding Young's modulus, is developed. Design procedures are presented to predict garment pressure using the aforementioned parameters for clinical applications. Compression garments have been widely used in treating burning scars. Fabricating a compression garment with a required pressure is important in the healing process. A systematic and scientific design method can enable the occupational therapist and compression garments' manufacturer to custom-make a compression garment with a specific pressure. The objectives of this study are 1) to develop a pressure prediction model incorporating different design factors to estimate the pressure exerted by the compression garments before fabrication; and 2) to propose more design procedures in clinical applications. Three kinds of fabrics cut at different bias angles were tested under uniaxial tension, as were samples made in a double-layered structure. Sets of nonlinear force-extension data were obtained for calculating the predicted pressure. Using the value at 0° bias angle as reference, the Young's modulus can vary by as much as 29% for fabric type P11117, 43% for fabric type PN2170, and even 360% for fabric type AP85120 at a reduction factor of 20%. When comparing the predicted pressure calculated from the single-layered and double-layered fabrics, the double-layered construction provides a larger range of target pressure at a particular strain. The anisotropic and nonlinear behaviors of the fabrics have thus been determined. Compression garments can be methodically designed by the proposed analytical pressure prediction model.

  10. Statistical assessment of predictive modeling uncertainty

    Science.gov (United States)

    Barzaghi, Riccardo; Marotta, Anna Maria

    2017-04-01

    When the results of geophysical models are compared with data, the uncertainties of the model are typically disregarded. We propose a method for defining the uncertainty of a geophysical model based on a numerical procedure that estimates the empirical auto and cross-covariances of model-estimated quantities. These empirical values are then fitted by proper covariance functions and used to compute the covariance matrix associated with the model predictions. The method is tested using a geophysical finite element model in the Mediterranean region. Using a novel χ2 analysis in which both data and model uncertainties are taken into account, the model's estimated tectonic strain pattern due to the Africa-Eurasia convergence in the area that extends from the Calabrian Arc to the Alpine domain is compared with that estimated from GPS velocities while taking into account the model uncertainty through its covariance structure and the covariance of the GPS estimates. The results indicate that including the estimated model covariance in the testing procedure leads to lower observed χ2 values that have better statistical significance and might help a sharper identification of the best-fitting geophysical models.

  11. Seasonal Predictability in a Model Atmosphere.

    Science.gov (United States)

    Lin, Hai

    2001-07-01

    The predictability of atmospheric mean-seasonal conditions in the absence of externally varying forcing is examined. A perfect-model approach is adopted, in which a global T21 three-level quasigeostrophic atmospheric model is integrated over 21 000 days to obtain a reference atmospheric orbit. The model is driven by a time-independent forcing, so that the only source of time variability is the internal dynamics. The forcing is set to perpetual winter conditions in the Northern Hemisphere (NH) and perpetual summer in the Southern Hemisphere.A significant temporal variability in the NH 90-day mean states is observed. The component of that variability associated with the higher-frequency motions, or climate noise, is estimated using a method developed by Madden. In the polar region, and to a lesser extent in the midlatitudes, the temporal variance of the winter means is significantly greater than the climate noise, suggesting some potential predictability in those regions.Forecast experiments are performed to see whether the presence of variance in the 90-day mean states that is in excess of the climate noise leads to some skill in the prediction of these states. Ensemble forecast experiments with nine members starting from slightly different initial conditions are performed for 200 different 90-day means along the reference atmospheric orbit. The serial correlation between the ensemble means and the reference orbit shows that there is skill in the 90-day mean predictions. The skill is concentrated in those regions of the NH that have the largest variance in excess of the climate noise. An EOF analysis shows that nearly all the predictive skill in the seasonal means is associated with one mode of variability with a strong axisymmetric component.

  12. Model of trust in work groups

    Directory of Open Access Journals (Sweden)

    Sidorenkov, Andrey V.

    2013-09-01

    Full Text Available A multi-dimensional model of trust in a small group has been developed and approved. This model includes two dimensions: trust levels (interpersonal trust, micro-group trust, group trust, trust between subgroups, trust between subgroups and group and types of trust (activity-coping, information-influential and confidentially-protective trust. Each level of trust is manifested in three types, so there are fifteen varieties of trust. Two corresponding questionnaires were developed for the study. 347 persons from 32 work groups participated in the research. It was determined that in a small group there is an asymmetry of trust levels within the group. In particular, micro-group trust is demonstrated the most in comparison with other trust levels. There is also an asymmetry in the manifestation of interpersonal trust in a group structure. This is demonstrated by the fact that in informal subgroups, in comparison with a group as a whole, interpersonal confidential and performance trust is the most manifested. In a small group and in informal subgroups there are relationships between trust levels which have certain regularities.

  13. Predicting functional brain ROIs via fiber shape models.

    Science.gov (United States)

    Zhang, Tuo; Guo, Lei; Li, Kaiming; Zhu, Dajing; Cui, Guangbin; Liu, Tianming

    2011-01-01

    Study of structural and functional connectivities of the human brain has received significant interest and effort recently. A fundamental question arises when attempting to measure the structural and/or functional connectivities of specific brain networks: how to best identify possible Regions of Interests (ROIs)? In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on learned fiber shape models from multimodal task-based fMRI and diffusion tensor imaging (DTI) data. In the training stage, ROIs are identified as activation peaks in task-based fMRI data. Then, shape models of white matter fibers emanating from these functional ROIs are learned. In addition, ROIs' location distribution model is learned to be used as an anatomical constraint. In the prediction stage, functional ROIs are predicted in individual brains based on DTI data. The ROI prediction is formulated and solved as an energy minimization problem, in which the two learned models are used as energy terms. Our experiment results show that the average ROI prediction error is 3.45 mm, in comparison with the benchmark data provided by working memory task-based fMRI. Promising results were also obtained on the ADNI-2 longitudinal DTI dataset.

  14. Development of an odorant emission model for sewage treatment works.

    Science.gov (United States)

    Gostelow, P; Parsons, S A; Cobb, J

    2001-01-01

    In the field of odour assessment, much attention has been paid to the measurement of odour concentration. Whilst the concentration of an odour at a receptor is a useful indicator of annoyance, the concentration at the source tells only half the story. The emission rate - the product of odour concentration and air flow rate - is required to appreciate the significance of odour sources. Knowledge of emission rates allows odour sources to be ranked in terms of significance and facilitates appropriate selection and design of odour control units. The emission rate is also a key input for atmospheric dispersion models. Given the increasing importance of odour to sewage treatment works operators, there is a clear need for predictive methods for odour emission rates. Theory suggests that the emission of odorants from sewage to air is controlled by mass transfer resistances in both the gas and liquid phase. These are in turn controlled by odorant and emission source characteristics. The required odorant characteristics are largely known, and mass transfer from many different types of emission sources have been studied. Sewage treatment processes can be described by one or more of six characteristic emission sources, these being quiescent surfaces, channels, weirs and drop structures, diffused aeration, surface aeration and flow over media. This paper describes the development of odorant mass transfer models for these characteristic emission types. The models have been applied in the form of spreadsheet models to the prediction of H2S emissions and the results compared with commercial VOC emission models.

  15. A kinetic model for predicting biodegradation.

    Science.gov (United States)

    Dimitrov, S; Pavlov, T; Nedelcheva, D; Reuschenbach, P; Silvani, M; Bias, R; Comber, M; Low, L; Lee, C; Parkerton, T; Mekenyan, O

    2007-01-01

    Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a '10 day window' criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the '10-day window' criterion.

  16. Modeling and predicting page-view dynamics on Wikipedia

    CERN Document Server

    Thij, Marijn ten; Laniado, David; Kaltenbrunner, Andreas

    2012-01-01

    The simplicity of producing and consuming online content makes it difficult to estimate how much attention will be devoted from Internet users to any given content. This work presents a general overview of temporal patterns in the access to content on a huge collaborative platform. We propose a model for predicting the popularity of promoted content, inspired by the analysis of the page-view dynamics on Wikipedia. Compared to previous studies, the observed popularity patterns are more complex; however, our model uses just few parameters to fully describe them. The model is validated through empirical measurements.

  17. [Assessment of predictive dermal exposure to chemicals in the work environment].

    Science.gov (United States)

    Jankowska, Agnieszka; Czerczak, Sławomir; Kupczewska-Dobecka, Małgorzata

    2017-06-27

    Assessment of dermal exposure to chemicals in the work environment is problematic, mainly as a result of the lack of measurement data on occupational exposure to chemicals. Due to common prevalence of occupational skin exposure and its health consequences it is necessary to look for efficient solutions allowing for reliable exposure assessment. The aim of the study is to present predictive models used to assess non-measured dermal exposure, as well as to acquaint Polish users with the principles of the selected model functioning. This paper presents examples of models to assist the employer in the the assessment of occupational exposure associated with the skin contact with chemicals, developed in European Union (EU) countries, as well as in countries outside the EU. Based on the literature data dermal exposure models EASE (Estimation and Assessment of Substance Exposure), COSHH Essentials (Control of Substances Hazardous to Health Regulations), DREAM (Dermal Exposure Assessment Method), Stoffenmanager , ECETOC TRA (European Centre for Ecotoxicology and Toxicology of Chemicals Targeted Risk Assessment), MEASE (Metal's EASE), PHED (Pesticide Handlers Exposure Database), DERM (Dermal Exposure Ranking Method) and RISKOFDERM (Risk Assessment of Occupational Dermal Exposure to Chemicals) were briefly described. Moreover the characteristics of RISKOFDERM, guidelines for its use, information on input and output data were further detailed. Problem of full work shift dermal exposure assessment is described. An example of exposure assessment using RISKOFDERM and effectiveness evaluation to date were also presented. When no measurements are available, RISKOFDERM allows dermal exposure assessment and thus can improve the risk assessment quality and effectiveness of dermal risk management. Med Pr 2017;68(4):557-569. This work is available in Open Access model and licensed under a CC BY-NC 3.0 PL license.

  18. In silico modeling to predict drug-induced phospholipidosis

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov; Sadrieh, Nakissa

    2013-06-01

    Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL.

  19. On Modeling and Constrained Model Predictive Control of Open Irrigation Canals

    Directory of Open Access Journals (Sweden)

    Lihui Cen

    2017-01-01

    Full Text Available This paper proposes a model predictive control of open irrigation canals with constraints. The Saint-Venant equations are widely used in hydraulics to model an open canal. As a set of hyperbolic partial differential equations, they are not solved explicitly and difficult to design optimal control algorithms. In this work, a prediction model of an open canal is developed by discretizing the Saint-Venant equations in both space and time. Based on the prediction model, a constrained model predictive control was firstly investigated for the case of one single-pool canal and then generalized to the case of a cascaded canal with multipools. The hydraulic software SICC was used to simulate the canal and test the algorithms with application to a real-world irrigation canal of Yehe irrigation area located in Hebei province.

  20. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-16

    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  1. A Predictive Model for Wind Farms Using Dynamic Mode Decomposition

    Science.gov (United States)

    Thomas, Vaughan; Meneveau, Charles; Gayme, Dennice

    2016-11-01

    In this work we extend traditional dynamic mode decomposition (DMD) to develop a linear predictive model for the time evolution of the velocity field for a multiple-turbine wind farm. Traditional DMD identifies a set of DMD modes which can be used to produce a linear system that approximates the dynamics of the original system. Typically, these DMD modes consist of those that both grow and decay, but in order to develop a predictive model we need a system that evolves along a manifold that neither grows nor decays. Here we modify the DMD calculation to build such a model. We then apply this method to three dimensional large eddy simulations (LES) of a multi-turbine wind farm. Our predictive wind farm model is initialized with a small time series of data independent of the original data used to create the system. When initialized in this manner our DMD based model can reproduce the subsequent time evolution of the velocity field over ten inter-turbine convective timescales with a gradual falloff in performance. This work is supported by the National Science Foundation (Grants ECCS-1230788 and OISE-1243482, the WINDINSPIRE project).

  2. A New Model for Prediction of Mean Liquid Circulating Velocity in Bubble Columns

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A new model without any fitting parameter for estimating the mean liquid recirculating velocity has been derived from previous work directly. The prediction agrees with experimental data reasonably well. Accurency of prediction from the new model is comparable with the models reported in the literature. However, the new model has a potential capability to predict the average liquid recirculation velocity at elevated pressure bubble columns since n and c is developed under pressure. However this needs to be further tested experimentally.

  3. Can you please put it out? Predicting non-smokers' assertiveness intentions at work.

    Science.gov (United States)

    Aspropoulos, Eleftherios; Lazuras, Lambros; Rodafinos, Angelos; Eiser, J Richard

    2010-04-01

    The present study aimed to identify the psychosocial predictors of non-smoker employee intentions to ask smokers not to smoke at work. The predictive effects of past behaviour, anticipated regret, social norms, attitudinal, outcome expectancy and behavioural control beliefs were investigated in relation to the Attitudes-Social influence-self-Efficacy (ASE) model. Data were collected from Greek non-smoker employees (n=137, mean age=33.5, SD=10.5, 54.7% female) in 15 companies. The main outcome measure was assertiveness intention. Data on participants' past smoking, age, gender and on current smoking policy in the company were also collected. The majority of employees (77.4%) reported being annoyed by exposure to passive smoking at work, but only 37% reported having asked a smoker colleague not to smoke in the last 30 days. Regression analysis showed that the strongest predictor of non-smokers' assertiveness intentions was how often they believed that other non-smokers were assertive. Perceived control over being assertive, annoyance with secondhand smoke (SHS) exposure at work and past assertive behaviour also significantly predicted assertiveness intentions. Assertiveness by non-smoker employees seems to be guided mainly by normative and behavioural control beliefs, annoyance with SHS exposure at work, and past behaviour. Interventions to promote assertiveness in non-smokers might benefit from efficacy training combined with conveying the messages that the majority of other non-smokers are frequently annoyed by exposure to SHS, and that nearly half of all non-smokers are assertive towards smokers.

  4. Probabilistic prediction models for aggregate quarry siting

    Science.gov (United States)

    Robinson, G.R.; Larkins, P.M.

    2007-01-01

    Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood (prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology, transportation network, and population density variables, were defined using quarry location and time of development data for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas, were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area, and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations. Both the aggregate industry and the land management community can use the model approach to target areas for more detailed site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation and population features. ?? International Association for Mathematical Geology 2007.

  5. Predicting Footbridge Response using Stochastic Load Models

    DEFF Research Database (Denmark)

    Pedersen, Lars; Frier, Christian

    2013-01-01

    Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing s...... as it pinpoints which decisions to be concerned about when the goal is to predict footbridge response. The studies involve estimating footbridge responses using Monte-Carlo simulations and focus is on estimating vertical structural response to single person loading....

  6. Nonconvex Model Predictive Control for Commercial Refrigeration

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Larsen, Lars F.S.; Jørgensen, John Bagterp

    2013-01-01

    is to minimize the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost...... the iterations, which is more than fast enough to run in real-time. We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost...

  7. Predictive In Vivo Models for Oncology.

    Science.gov (United States)

    Behrens, Diana; Rolff, Jana; Hoffmann, Jens

    2016-01-01

    Experimental oncology research and preclinical drug development both substantially require specific, clinically relevant in vitro and in vivo tumor models. The increasing knowledge about the heterogeneity of cancer requested a substantial restructuring of the test systems for the different stages of development. To be able to cope with the complexity of the disease, larger panels of patient-derived tumor models have to be implemented and extensively characterized. Together with individual genetically engineered tumor models and supported by core functions for expression profiling and data analysis, an integrated discovery process has been generated for predictive and personalized drug development.Improved “humanized” mouse models should help to overcome current limitations given by xenogeneic barrier between humans and mice. Establishment of a functional human immune system and a corresponding human microenvironment in laboratory animals will strongly support further research.Drug discovery, systems biology, and translational research are moving closer together to address all the new hallmarks of cancer, increase the success rate of drug development, and increase the predictive value of preclinical models.

  8. Constructing predictive models of human running.

    Science.gov (United States)

    Maus, Horst-Moritz; Revzen, Shai; Guckenheimer, John; Ludwig, Christian; Reger, Johann; Seyfarth, Andre

    2015-02-06

    Running is an essential mode of human locomotion, during which ballistic aerial phases alternate with phases when a single foot contacts the ground. The spring-loaded inverted pendulum (SLIP) provides a starting point for modelling running, and generates ground reaction forces that resemble those of the centre of mass (CoM) of a human runner. Here, we show that while SLIP reproduces within-step kinematics of the CoM in three dimensions, it fails to reproduce stability and predict future motions. We construct SLIP control models using data-driven Floquet analysis, and show how these models may be used to obtain predictive models of human running with six additional states comprising the position and velocity of the swing-leg ankle. Our methods are general, and may be applied to any rhythmic physical system. We provide an approach for identifying an event-driven linear controller that approximates an observed stabilization strategy, and for producing a reduced-state model which closely recovers the observed dynamics. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  9. Statistical Seasonal Sea Surface based Prediction Model

    Science.gov (United States)

    Suarez, Roberto; Rodriguez-Fonseca, Belen; Diouf, Ibrahima

    2014-05-01

    The interannual variability of the sea surface temperature (SST) plays a key role in the strongly seasonal rainfall regime on the West African region. The predictability of the seasonal cycle of rainfall is a field widely discussed by the scientific community, with results that fail to be satisfactory due to the difficulty of dynamical models to reproduce the behavior of the Inter Tropical Convergence Zone (ITCZ). To tackle this problem, a statistical model based on oceanic predictors has been developed at the Universidad Complutense of Madrid (UCM) with the aim to complement and enhance the predictability of the West African Monsoon (WAM) as an alternative to the coupled models. The model, called S4CAST (SST-based Statistical Seasonal Forecast) is based on discriminant analysis techniques, specifically the Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA). Beyond the application of the model to the prediciton of rainfall in West Africa, its use extends to a range of different oceanic, atmospheric and helth related parameters influenced by the temperature of the sea surface as a defining factor of variability.

  10. Diversity Competent Group Work Supervision: An Application of the Supervision of Group Work Model (SGW)

    Science.gov (United States)

    Okech, Jane E. Atieno; Rubel, Deborah

    2007-01-01

    This article emphasizes the need for concrete descriptions of supervision to promote diversity-competent group work and presents an application of the supervision of group work model (SGW) to this end. The SGW, a supervision model adapted from the discrimination model, is uniquely suited for promoting diversity competence in group work, since it…

  11. Diversity Competent Group Work Supervision: An Application of the Supervision of Group Work Model (SGW)

    Science.gov (United States)

    Okech, Jane E. Atieno; Rubel, Deborah

    2007-01-01

    This article emphasizes the need for concrete descriptions of supervision to promote diversity-competent group work and presents an application of the supervision of group work model (SGW) to this end. The SGW, a supervision model adapted from the discrimination model, is uniquely suited for promoting diversity competence in group work, since it…

  12. The development of U. S. soil erosion prediction and modeling

    Directory of Open Access Journals (Sweden)

    John M. Laflen

    2013-09-01

    Full Text Available Soil erosion prediction technology began over 70 years ago when Austin Zingg published a relationship between soil erosion (by water and land slope and length, followed shortly by a relationship by Dwight Smith that expanded this equation to include conservation practices. But, it was nearly 20 years before this work's expansion resulted in the Universal Soil Loss Equation (USLE, perhaps the foremost achievement in soil erosion prediction in the last century. The USLE has increased in application and complexity, and its usefulness and limitations have led to the development of additional technologies and new science in soil erosion research and prediction. Main among these new technologies is the Water Erosion Prediction Project (WEPP model, which has helped to overcome many of the shortcomings of the USLE, and increased the scale over which erosion by water can be predicted. Areas of application of erosion prediction include almost all land types: urban, rural, cropland, forests, rangeland, and construction sites. Specialty applications of WEPP include prediction of radioactive material movement with soils at a superfund cleanup site, and near real-time daily estimation of soil erosion for the entire state of Iowa.

  13. Predictive modeling by the cerebellum improves proprioception.

    Science.gov (United States)

    Bhanpuri, Nasir H; Okamura, Allison M; Bastian, Amy J

    2013-09-04

    Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance.

  14. A prediction model for Clostridium difficile recurrence

    Directory of Open Access Journals (Sweden)

    Francis D. LaBarbera

    2015-02-01

    Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.

  15. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K

    2001-01-01

    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  16. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  17. Modeling and Prediction of Krueger Device Noise

    Science.gov (United States)

    Guo, Yueping; Burley, Casey L.; Thomas, Russell H.

    2016-01-01

    This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.

  18. A generative model for predicting terrorist incidents

    Science.gov (United States)

    Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger

    2017-05-01

    A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations

  19. Nonlinear turbulence models for predicting strong curvature effects

    Institute of Scientific and Technical Information of China (English)

    XU Jing-lei; MA Hui-yang; HUANG Yu-ning

    2008-01-01

    Prediction of the characteristics of turbulent flows with strong streamline curvature, such as flows in turbomachines, curved channel flows, flows around airfoils and buildings, is of great importance in engineering applicatious and poses a very practical challenge for turbulence modeling. In this paper, we analyze qualitatively the curvature effects on the structure of turbulence and conduct numerical simulations of a turbulent U- duct flow with a number of turbulence models in order to assess their overall performance. The models evaluated in this work are some typical linear eddy viscosity turbulence models, nonlinear eddy viscosity turbulence models (NLEVM) (quadratic and cubic), a quadratic explicit algebraic stress model (EASM) and a Reynolds stress model (RSM) developed based on the second-moment closure. Our numerical results show that a cubic NLEVM that performs considerably well in other benchmark turbulent flows, such as the Craft, Launder and Suga model and the Huang and Ma model, is able to capture the major features of the highly curved turbulent U-duct flow, including the damping of turbulence near the convex wall, the enhancement of turbulence near the concave wall, and the subsequent turbulent flow separation. The predictions of the cubic models are quite close to that of the RSM, in relatively good agreement with the experimental data, which suggests that these inodels may be employed to simulate the turbulent curved flows in engineering applications.

  20. Can Muscle Soreness After Intensive Work-related Activities Be Predicted?

    OpenAIRE

    Soer, Remko; Jan H B Geertzen; van der Schans, Cees P; Johan W. Groothoff; Reneman, Michiel F

    2009-01-01

    Objectives: It is currently unknown whether specific determinants are predictive for developing delayed onset muscle soreness (DOMS) after heavy work-related activities. The aim of this study was to analyze whether personal characteristics and performance measures are predictive for onset, intensity, and duration of DOMS after performing work-related activities during a Functional Capacity Evaluation in healthy participants. Methods: Included in this study were 197 healthy participants (102 m...

  1. Optimal feedback scheduling of model predictive controllers

    Institute of Scientific and Technical Information of China (English)

    Pingfang ZHOU; Jianying XIE; Xiaolong DENG

    2006-01-01

    Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.

  2. Does working memory capacity affect the ability to predict upcoming words in discourse?

    NARCIS (Netherlands)

    Otten, M.; van Berkum, J.J.A.

    2009-01-01

    Prior research has indicated that readers and listeners can use information in the prior discourse to rapidly predict specific upcoming words, as the text is unfolding. Here we used event-related potentials to explore whether the ability to make rapid online predictions depends on a reader's working

  3. Can Muscle Soreness After Intensive Work-related Activities Be Predicted?

    NARCIS (Netherlands)

    Soer, Remko; Geertzen, Jan H. B.; van der Schans, Cees P.; Groothoff, Johan W.; Reneman, Michiel F.

    2009-01-01

    Objectives: It is currently unknown whether specific determinants are predictive for developing delayed onset muscle soreness (DOMS) after heavy work-related activities. The aim of this study was to analyze whether personal characteristics and performance measures are predictive for onset, intensity

  4. Overview: What's Worked and What Hasn't as a Guide towards Predictive Admissions Tool Development

    Science.gov (United States)

    Siu, Eric; Reiter, Harold I.

    2009-01-01

    Admissions committees and researchers around the globe have used diligence and imagination to develop and implement various screening measures with the ultimate goal of predicting future clinical and professional performance. What works for predicting future job performance in the human resources world and in most of the academic world may not,…

  5. The Diagnostic Apathia Scale predicts the ability to return to work following depression or anxiety

    DEFF Research Database (Denmark)

    Hellström, Lc; Eplov, Lf; Nordentoft, M

    2014-01-01

    , tiredness/fatigue, insomnia, and reduced ability to work and engage in personal interests. The scale was analysed for psychometric validity (scalability) and for its ability to predict RTW. Finally, the predictive validity of the Diagnostic Apathia Scale regarding RTW was compared with scales measuring...

  6. Objective calibration of numerical weather prediction models

    Science.gov (United States)

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

    2017-07-01

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

  7. Prediction models from CAD models of 3D objects

    Science.gov (United States)

    Camps, Octavia I.

    1992-11-01

    In this paper we present a probabilistic prediction based approach for CAD-based object recognition. Given a CAD model of an object, the PREMIO system combines techniques of analytic graphics and physical models of lights and sensors to predict how features of the object will appear in images. In nearly 4,000 experiments on analytically-generated and real images, we show that in a semi-controlled environment, predicting the detectability of features of the image can successfully guide a search procedure to make informed choices of model and image features in its search for correspondences that can be used to hypothesize the pose of the object. Furthermore, we provide a rigorous experimental protocol that can be used to determine the optimal number of correspondences to seek so that the probability of failing to find a pose and of finding an inaccurate pose are minimized.

  8. Model predictive control of MSMPR crystallizers

    Science.gov (United States)

    Moldoványi, Nóra; Lakatos, Béla G.; Szeifert, Ferenc

    2005-02-01

    A multi-input-multi-output (MIMO) control problem of isothermal continuous crystallizers is addressed in order to create an adequate model-based control system. The moment equation model of mixed suspension, mixed product removal (MSMPR) crystallizers that forms a dynamical system is used, the state of which is represented by the vector of six variables: the first four leading moments of the crystal size, solute concentration and solvent concentration. Hence, the time evolution of the system occurs in a bounded region of the six-dimensional phase space. The controlled variables are the mean size of the grain; the crystal size-distribution and the manipulated variables are the input concentration of the solute and the flow rate. The controllability and observability as well as the coupling between the inputs and the outputs was analyzed by simulation using the linearized model. It is shown that the crystallizer is a nonlinear MIMO system with strong coupling between the state variables. Considering the possibilities of the model reduction, a third-order model was found quite adequate for the model estimation in model predictive control (MPC). The mean crystal size and the variance of the size distribution can be nearly separately controlled by the residence time and the inlet solute concentration, respectively. By seeding, the controllability of the crystallizer increases significantly, and the overshoots and the oscillations become smaller. The results of the controlling study have shown that the linear MPC is an adaptable and feasible controller of continuous crystallizers.

  9. A Chemical Containment Model for the General Purpose Work Station

    Science.gov (United States)

    Flippen, Alexis A.; Schmidt, Gregory K.

    1994-01-01

    Contamination control is a critical safety requirement imposed on experiments flying on board the Spacelab. The General Purpose Work Station, a Spacelab support facility used for life sciences space flight experiments, is designed to remove volatile compounds from its internal airpath and thereby minimize contamination of the Spacelab. This is accomplished through the use of a large, multi-stage filter known as the Trace Contaminant Control System. Many experiments planned for the Spacelab require the use of toxic, volatile fixatives in order to preserve specimens prior to postflight analysis. The NASA-Ames Research Center SLS-2 payload, in particular, necessitated the use of several toxic, volatile compounds in order to accomplish the many inflight experiment objectives of this mission. A model was developed based on earlier theories and calculations which provides conservative predictions of the resultant concentrations of these compounds given various spill scenarios. This paper describes the development and application of this model.

  10. An Anisotropic Hardening Model for Springback Prediction

    Science.gov (United States)

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

  11. Formability prediction for AHSS materials using damage models

    Science.gov (United States)

    Amaral, R.; Santos, Abel D.; José, César de Sá; Miranda, Sara

    2017-05-01

    Advanced high strength steels (AHSS) are seeing an increased use, mostly due to lightweight design in automobile industry and strict regulations on safety and greenhouse gases emissions. However, the use of these materials, characterized by a high strength to weight ratio, stiffness and high work hardening at early stages of plastic deformation, have imposed many challenges in sheet metal industry, mainly their low formability and different behaviour, when compared to traditional steels, which may represent a defying task, both to obtain a successful component and also when using numerical simulation to predict material behaviour and its fracture limits. Although numerical prediction of critical strains in sheet metal forming processes is still very often based on the classic forming limit diagrams, alternative approaches can use damage models, which are based on stress states to predict failure during the forming process and they can be classified as empirical, physics based and phenomenological models. In the present paper a comparative analysis of different ductile damage models is carried out, in order numerically evaluate two isotropic coupled damage models proposed by Johnson-Cook and Gurson-Tvergaard-Needleman (GTN), each of them corresponding to the first two previous group classification. Finite element analysis is used considering these damage mechanics approaches and the obtained results are compared with experimental Nakajima tests, thus being possible to evaluate and validate the ability to predict damage and formability limits for previous defined approaches.

  12. Hidden Semi-Markov Models for Predictive Maintenance

    Directory of Open Access Journals (Sweden)

    Francesco Cartella

    2015-01-01

    Full Text Available Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs with (i no constraints on the state duration density function and (ii being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL of the machine is calculated.

  13. Simple predictions from multifield inflationary models.

    Science.gov (United States)

    Easther, Richard; Frazer, Jonathan; Peiris, Hiranya V; Price, Layne C

    2014-04-25

    We explore whether multifield inflationary models make unambiguous predictions for fundamental cosmological observables. Focusing on N-quadratic inflation, we numerically evaluate the full perturbation equations for models with 2, 3, and O(100) fields, using several distinct methods for specifying the initial values of the background fields. All scenarios are highly predictive, with the probability distribution functions of the cosmological observables becoming more sharply peaked as N increases. For N=100 fields, 95% of our Monte Carlo samples fall in the ranges ns∈(0.9455,0.9534), α∈(-9.741,-7.047)×10-4, r∈(0.1445,0.1449), and riso∈(0.02137,3.510)×10-3 for the spectral index, running, tensor-to-scalar ratio, and isocurvature-to-adiabatic ratio, respectively. The expected amplitude of isocurvature perturbations grows with N, raising the possibility that many-field models may be sensitive to postinflationary physics and suggesting new avenues for testing these scenarios.

  14. Model Predictive Control for the Operation of Building Cooling Systems

    Energy Technology Data Exchange (ETDEWEB)

    Ma, Yudong; Borrelli, Francesco; Hencey, Brandon; Coffey, Brian; Bengea, Sorin; Haves, Philip

    2010-06-29

    A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reduction in the central plant electricity cost and improvement of its efficiency.

  15. Estimating Predictive Variance for Statistical Gas Distribution Modelling

    Science.gov (United States)

    Lilienthal, Achim J.; Asadi, Sahar; Reggente, Matteo

    2009-05-01

    Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.

  16. Catalytic models developed through social work

    DEFF Research Database (Denmark)

    Jensen, Mogens

    2015-01-01

    The article develops the concept of catalytic processes in relation to social work with adolescents in an attempt to both reach a more nuanced understanding of social work and at the same time to develop the concept of catalytic processes in psychology. The social work is pedagogical treatment...... of adolescents placed in out-of-home care and is characterised using three situated cases as empirical data. Afterwards the concept of catalytic processes is briefly presented and then applied in an analysis of pedagogical treatment in the three cases. The result is a different conceptualisation of the social...... work with new possibilities of development of the work, but also suggestions for development of the concept of catalytic processes....

  17. Predictions of models for environmental radiological assessment

    Energy Technology Data Exchange (ETDEWEB)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa, E-mail: suelip@ird.gov.br, E-mail: dejanira@irg.gov.br [Instituto de Radioprotecao e Dosimetria (IRD/CNEN-RJ), Servico de Avaliacao de Impacto Ambiental, Rio de Janeiro, RJ (Brazil); Mahler, Claudio Fernando [Coppe. Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia, Universidade Federal do Rio de Janeiro (UFRJ) - Programa de Engenharia Civil, RJ (Brazil)

    2011-07-01

    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for {sup 137}Cs and {sup 60}Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  18. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained......The primary structure of a protein is the sequence of its amino acids. The secondary structure describes structural properties of the molecule such as which parts of it form sheets, helices or coils. Spacial and other properties are described by the higher order structures. The classification task...

  19. A Modified Model Predictive Control Scheme

    Institute of Scientific and Technical Information of China (English)

    Xiao-Bing Hu; Wen-Hua Chen

    2005-01-01

    In implementations of MPC (Model Predictive Control) schemes, two issues need to be addressed. One is how to enlarge the stability region as much as possible. The other is how to guarantee stability when a computational time limitation exists. In this paper, a modified MPC scheme for constrained linear systems is described. An offline LMI-based iteration process is introduced to expand the stability region. At the same time, a database of feasible control sequences is generated offline so that stability can still be guaranteed in the case of computational time limitations. Simulation results illustrate the effectiveness of this new approach.

  20. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... facilitates plug-and-play addition of subsystems without redesign of any controllers. The method is supported by a number of simulations featuring a three-level smart-grid power control system for a small isolated power grid....

  1. Explicit model predictive control accuracy analysis

    OpenAIRE

    Knyazev, Andrew; Zhu, Peizhen; Di Cairano, Stefano

    2015-01-01

    Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be explicitly computed off-line, which results in an off-line partition of the state space into non-overlapped convex regions, with affine control laws associated to each region of the partition. An actual implementation of this explicit MPC in low cost micro-controllers requires the data to be "quantized", i.e. repre...

  2. Innovative first order elimination kinetics working model for easy learning

    Directory of Open Access Journals (Sweden)

    Navin Budania

    2016-06-01

    Conclusions: First order elimination kinetics is easily understood with the help of above working model. More and more working models could be developed for teaching difficult topics. [Int J Basic Clin Pharmacol 2016; 5(3.000: 862-864

  3. Neighbourhood, Route and Workplace-Related Environmental Characteristics Predict Adults' Mode of Travel to Work.

    Directory of Open Access Journals (Sweden)

    Alice M Dalton

    Full Text Available Commuting provides opportunities for regular physical activity which can reduce the risk of chronic disease. Commuters' mode of travel may be shaped by their environment, but understanding of which specific environmental characteristics are most important and might form targets for intervention is limited. This study investigated associations between mode choice and a range of objectively assessed environmental characteristics.Participants in the Commuting and Health in Cambridge study reported where they lived and worked, their usual mode of travel to work and a variety of socio-demographic characteristics. Using geographic information system (GIS software, 30 exposure variables were produced capturing characteristics of areas around participants' homes and workplaces and their shortest modelled routes to work. Associations between usual mode of travel to work and personal and environmental characteristics were investigated using multinomial logistic regression.Of the 1124 respondents, 50% reported cycling or walking as their usual mode of travel to work. In adjusted analyses, home-work distance was strongly associated with mode choice, particularly for walking. Lower odds of walking or cycling rather than driving were associated with a less frequent bus service (highest versus lowest tertile: walking OR 0.61 [95% CI 0.20-1.85]; cycling OR 0.43 [95% CI 0.23-0.83], low street connectivity (OR 0.22, [0.07-0.67]; OR 0.48 [0.26-0.90] and free car parking at work (OR 0.24 [0.10-0.59]; OR 0.55 [0.32-0.95]. Participants were less likely to cycle if they had access to fewer destinations (leisure facilities, shops and schools close to work (OR 0.36 [0.21-0.62] and a railway station further from home (OR 0.53 [0.30-0.93]. Covariates strongly predicted travel mode (pseudo r-squared 0.74.Potentially modifiable environmental characteristics, including workplace car parking, street connectivity and access to public transport, are associated with travel mode

  4. Predictive factors of work disability in rheumatoid arthritis: a systematic literature review.

    NARCIS (Netherlands)

    Croon, de E.M.; Sluiter, J.K.; Nijssen, TF; Dijkmans, B.A.C.; Lankhorst, G.J.; Frings-Dresen, MH

    2004-01-01

    BACKGROUND: Work disability-a common outcome of rheumatoid arthritis (RA)-is a societal (for example, financial costs) and individual problem (for example, loss of status, income, social support, and distraction from pain and distress). Until now, factors that predict work disability in RA have not

  5. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-10-01

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

  6. Downplaying model power in IT project work

    DEFF Research Database (Denmark)

    Richter, Anne; Buhl, Henrik

    2004-01-01

    Executives and information technology specialists often manage IT projects in project teams. Integrative IT systems provide opportunities to manage and restructure work functions, but the process of change often causes serious problems in implementation and diffusion. A central issue in the resea......Executives and information technology specialists often manage IT projects in project teams. Integrative IT systems provide opportunities to manage and restructure work functions, but the process of change often causes serious problems in implementation and diffusion. A central issue...... possible to put issues such as team functions and quality of work on the agenda. Simultaneously, participation competencies seem to have been enhanced....

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

    Science.gov (United States)

    Tement, Sara; Korunka, Christian

    2013-01-01

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

  8. The experiment of monthly mean circulation prediction using the analogy-dynamical model

    Institute of Scientific and Technical Information of China (English)

    BAO Ming; NI Yunqi; CHOU Jifan

    2004-01-01

    Based on the past related research work, a new analogy-dynamical monthly prediction model is established with the operational dynamic extended-range forecast model T63L16 (hereafter T63) as a dynamic kernel. The monthly mean circulation prediction with T63 is considered as a control experiment, and the prediction with the analogy-dynamical model as a contrast one. It is found that the analogy-dynamical model has more precise forecast skill than the T63 model through monthly mean numerical prediction experiment.

  9. Parental Attachment, Cognitive Working Models, and Depression among African American College Students

    Science.gov (United States)

    Love, Keisha M.; Murdock, Tamera B.

    2012-01-01

    In an attempt to understand the cognitive mechanisms by which parental attachments predict depression among African American college students, the authors examined a mediational path model containing parental attachment, cognitive working models, and depression. The model demonstrated a close fit to the data, and several significant paths emerged.…

  10. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang

    2015-01-01

    For the modern railways, maintenance is critical for ensuring safety, train punctuality and overall capacity utilization. The cost of railway maintenance in Europe is high, on average between 30,000 – 100,000 Euro per km per year [1]. Aiming to reduce such maintenance expenditure, this paper...... presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time...... recovery on the track quality after tamping operation and (5) Tamping machine operation factors. A Danish railway track between Odense and Fredericia with 57.2 km of length is applied for a time period of two to four years in the proposed maintenance model. The total cost can be reduced with up to 50...

  11. Prediction Model for Offloading in Vehicular Wi-Fi Network

    Directory of Open Access Journals (Sweden)

    Mahmoud Abdulwahab Alawi

    2016-12-01

    Full Text Available It cannot be denied that, the inescapable diffusion of smartphones, tablets and other vehicular network applications with diverse networking and multimedia capabilities, and the associated blooming of all kinds of data-hungry multimedia services that passengers normally used while traveling exert a big challenge to cellular infrastructure operators. Wireless fidelity (Wi-Fi as well as fourth generation long term evolution advanced (4G LTE-A network are widely available today, Wi-Fi could be used by the vehicle users to relieve 4G LTE-A networks. Though, using IEE802.11 Wi-Fi AP to offload 4G LTE-A network for moving vehicle is a challenging task since it only covers short distance and not well deployed to cover all the roads. Several studies have proposed the offloading techniques based on predicted available APs for making offload decision. However, most of the proposed prediction mechanisms are only based on historical connection pattern. This work proposed a prediction model which utilized historical connection pattern, vehicular movement and driver profile to predict the next available AP.  The proposed model is compared with the existing models to evaluate its practicability.

  12. Modeling and Prediction of Hot Deformation Flow Curves

    Science.gov (United States)

    Mirzadeh, Hamed; Cabrera, Jose Maria; Najafizadeh, Abbas

    2012-01-01

    The modeling of hot flow stress and prediction of flow curves for unseen deformation conditions are important in metal-forming processes because any feasible mathematical simulation needs accurate flow description. In the current work, in an attempt to summarize, generalize, and introduce efficient methods, the dynamic recrystallization (DRX) flow curves of a 17-4 PH martensitic precipitation hardening stainless steel, a medium carbon microalloyed steel, and a 304 H austenitic stainless steel were modeled and predicted using (1) a hyperbolic sine equation with strain dependent constants, (2) a developed constitutive equation in a simple normalized stress-normalized strain form and its modified version, and (3) a feed-forward artificial neural network (ANN). These methods were critically discussed, and the ANN technique was found to be the best for the modeling available flow curves; however, the developed constitutive equation showed slightly better performance than that of ANN and significantly better predicted values than those of the hyperbolic sine equation in prediction of flow curves for unseen deformation conditions.

  13. Towards an Islamic model of work motivation

    National Research Council Canada - National Science Library

    Akram Abdul Cader

    2016-01-01

    .... Current Islamic models predominantly focus on integration with Western theories. This study proposes a synthesised model of Islamic motivation through an interpretive approach of Islamic theological texts (Qur'an and Sunnah...

  14. A predictive fitness model for influenza

    Science.gov (United States)

    Łuksza, Marta; Lässig, Michael

    2014-03-01

    The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage.

  15. Predictive Model of Radiative Neutrino Masses

    CERN Document Server

    Babu, K S

    2013-01-01

    We present a simple and predictive model of radiative neutrino masses. It is a special case of the Zee model which introduces two Higgs doublets and a charged singlet. We impose a family-dependent Z_4 symmetry acting on the leptons, which reduces the number of parameters describing neutrino oscillations to four. A variety of predictions follow: The hierarchy of neutrino masses must be inverted; the lightest neutrino mass is extremely small and calculable; one of the neutrino mixing angles is determined in terms of the other two; the phase parameters take CP-conserving values with \\delta_{CP} = \\pi; and the effective mass in neutrinoless double beta decay lies in a narrow range, m_{\\beta \\beta} = (17.6 - 18.5) meV. The ratio of vacuum expectation values of the two Higgs doublets, tan\\beta, is determined to be either 1.9 or 0.19 from neutrino oscillation data. Flavor-conserving and flavor-changing couplings of the Higgs doublets are also determined from neutrino data. The non-standard neutral Higgs bosons, if t...

  16. Slip Model Used for Prediction of r Value of BCC Metal Sheets from ODF Coefficients

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Different slip models were used for prediction of rvalue of BCC metal sheets from ODF coefficients. According to the maximum plastic work theory developed by Bishop and Hill, it is expected that the higher of Taylor factors given by a slip model, the better predictio nobtained based on the model. From this point of view, a composed slip model of BCC metals was presented. Based on the model, the agreement of predicted rvalues for deep drawing steels with experimental ones is excellent.

  17. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  18. Interpolation techniques in robust constrained model predictive control

    Science.gov (United States)

    Kheawhom, Soorathep; Bumroongsri, Pornchai

    2017-05-01

    This work investigates interpolation techniques that can be employed on off-line robust constrained model predictive control for a discrete time-varying system. A sequence of feedback gains is determined by solving off-line a series of optimal control optimization problems. A sequence of nested corresponding robustly positive invariant set, which is either ellipsoidal or polyhedral set, is then constructed. At each sampling time, the smallest invariant set containing the current state is determined. If the current invariant set is the innermost set, the pre-computed gain associated with the innermost set is applied. If otherwise, a feedback gain is variable and determined by a linear interpolation of the pre-computed gains. The proposed algorithms are illustrated with case studies of a two-tank system. The simulation results showed that the proposed interpolation techniques significantly improve control performance of off-line robust model predictive control without much sacrificing on-line computational performance.

  19. Continuous-Discrete Time Prediction-Error Identification Relevant for Linear Model Predictive Control

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model......A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...

  20. Strains at the myotendinous junction predicted by a micromechanical model.

    Science.gov (United States)

    Sharafi, Bahar; Ames, Elizabeth G; Holmes, Jeffrey W; Blemker, Silvia S

    2011-11-10

    The goal of this work was to create a finite element micromechanical model of the myotendinous junction (MTJ) to examine how the structure and mechanics of the MTJ affect the local micro-scale strains experienced by muscle fibers. We validated the model through comparisons with histological longitudinal sections of muscles fixed in slack and stretched positions. The model predicted deformations of the A-bands within the fiber near the MTJ that were similar to those measured from the histological sections. We then used the model to predict the dependence of local fiber strains on activation and the mechanical properties of the endomysium. The model predicted that peak micro-scale strains increase with activation and as the compliance of the endomysium decreases. Analysis of the models revealed that, in passive stretch, local fiber strains are governed by the difference of the mechanical properties between the fibers and the endomysium. In active stretch, strain distributions are governed by the difference in cross-sectional area along the length of the tapered region of the fiber near the MTJ. The endomysium provides passive resistance that balances the active forces and prevents the tapered region of the fiber from undergoing excessive strain. These model predictions lead to the following hypotheses: (i) the increased likelihood of injury during active lengthening of muscle fibers may be due to the increase in peak strain with activation and (ii) endomysium may play a role in protecting fibers from injury by reducing the strains within the fiber at the MTJ. Copyright © 2011 Elsevier Ltd. All rights reserved.

  1. How personal resources predict work engagement and self-rated performance among construction workers: a social cognitive perspective.

    Science.gov (United States)

    Lorente, Laura; Salanova, Marisa; Martínez, Isabel M; Vera, María

    2014-06-01

    Traditionally, research focussing on psychosocial factors in the construction industry has focused mainly on the negative aspects of health and on results such as occupational accidents. This study, however, focuses on the specific relationships among the different positive psychosocial factors shared by construction workers that could be responsible for occupational well-being and outcomes such as performance. The main objective of this study was to test whether personal resources predict self-rated job performance through job resources and work engagement. Following the predictions of Bandura's Social Cognitive Theory and the motivational process of the Job Demands-Resources Model, we expect that the relationship between personal resources and performance will be fully mediated by job resources and work engagement. The sample consists of 228 construction workers. Structural equation modelling supports the research model. Personal resources (i.e. self-efficacy, mental and emotional competences) play a predicting role in the perception of job resources (i.e. job control and supervisor social support), which in turn leads to work engagement and self-rated performance. This study emphasises the crucial role that personal resources play in determining how people perceive job resources by determining the levels of work engagement and, hence, their self-rated job performance. Theoretical and practical implications are discussed.

  2. Beyond the Standard Model: Working group report

    Indian Academy of Sciences (India)

    Gautam Bhattacharyya; Amitava Raychaudhuri

    2000-07-01

    This report summarises the work done in the ‘Beyond the Standard Model’ working group of the Sixth Workshop on High Energy Physics Phenomenology (WHEPP-6) held at the Institute of Mathematical Sciences, Chennai, Jan 3–15, 2000. The participants in this working group were: R Adhikari, B Ananthanarayan, K P S Balaji, Gour Bhattacharya, Gautam Bhattacharyya, Chao-Hsi Chang (Zhang), D Choudhury, Amitava Datta, Anindya Datta, Asesh K Datta, A Dighe, N Gaur, D Ghosh, A Goyal, K Kar, S F King, Anirban Kundu, U Mahanta, R N Mohapatra, B Mukhopadhyaya, S Pakvasa, P N Pandita, M K Parida, P Poulose, G Raffelt, G Rajasekaran, S Rakshit, Asim K Ray, A Raychaudhuri, S Raychaudhuri, D P Roy, P Roy, S Roy, K Sridhar and S Vempati.

  3. Two criteria for evaluating risk prediction models.

    Science.gov (United States)

    Pfeiffer, R M; Gail, M H

    2011-09-01

    We propose and study two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF (q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF (p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF (q) assesses the effectiveness of a program that follows 100q% of the population at highest risk. PNF (p) assess the feasibility of covering 100p% of cases by indicating how much of the population at highest risk must be followed. We show the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates of PCF and PNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data.

  4. Methods for Handling Missing Variables in Risk Prediction Models

    NARCIS (Netherlands)

    Held, Ulrike; Kessels, Alfons; Aymerich, Judith Garcia; Basagana, Xavier; ter Riet, Gerben; Moons, Karel G. M.; Puhan, Milo A.

    2016-01-01

    Prediction models should be externally validated before being used in clinical practice. Many published prediction models have never been validated. Uncollected predictor variables in otherwise suitable validation cohorts are the main factor precluding external validation.We used individual patient

  5. Factors predicting work outcome in Japanese patients with schizophrenia: role of multiple functioning levels

    Directory of Open Access Journals (Sweden)

    Chika Sumiyoshi

    2015-09-01

    Full Text Available Functional outcomes in individuals with schizophrenia suggest recovery of cognitive, everyday, and social functioning. Specifically improvement of work status is considered to be most important for their independent living and self-efficacy. The main purposes of the present study were 1 to identify which outcome factors predict occupational functioning, quantified as work hours, and 2 to provide cut-offs on the scales for those factors to attain better work status. Forty-five Japanese patients with schizophrenia and 111 healthy controls entered the study. Cognition, capacity for everyday activities, and social functioning were assessed by the Japanese versions of the MATRICS Cognitive Consensus Battery (MCCB, the UCSD Performance-based Skills Assessment-Brief (UPSA-B, and the Social Functioning Scale Individuals’ version modified for the MATRICS-PASS (Modified SFS for PASS, respectively. Potential factors for work outcome were estimated by multiple linear regression analyses (predicting work hours directly and a multiple logistic regression analyses (predicting dichotomized work status based on work hours. ROC curve analyses were performed to determine cut-off points for differentiating between the better- and poor work status. The results showed that a cognitive component, comprising visual/verbal learning and emotional management, and a social functioning component, comprising independent living and vocational functioning, were potential factors for predicting work hours/status. Cut-off points obtained in ROC analyses indicated that 60–70% achievements on the measures of those factors were expected to maintain the better work status. Our findings suggest that improvement on specific aspects of cognitive and social functioning are important for work outcome in patients with schizophrenia.

  6. Predicting change in symptoms of depression during the transition to university: the roles of BDNF and working memory capacity.

    Science.gov (United States)

    LeMoult, Joelle; Carver, Charles S; Johnson, Sheri L; Joormann, Jutta

    2015-03-01

    Studies on depression risk emphasize the importance of both cognitive and genetic vulnerability factors. The present study has provided the first examination of whether working memory capacity, the BDNF Val66Met polymorphism, and their interaction predict changes in symptoms of depression during the transition to university. Early in the semester, students completed a self-report measure of depressive symptoms and a modified version of the reading span task to assess working memory capacity in the presence of both neutral and negative distractors. Whole blood was genotyped for the BDNF Val66Met polymorphism. Students returned at the end of the semester to complete additional self-report questionnaires. Neither working memory capacity nor the BDNF Val66Met polymorphism predicted change in depressive symptoms either independently or in interaction with self-reported semester difficulty. The BDNF Val66Met polymorphism, however, moderated the association between working memory capacity and symptom change. Among met carriers, lower working memory capacity in the presence of negative-but not neutral-distractors was associated with increased symptoms of depression over the semester. For the val/val group, working memory capacity did not predict symptom change. These findings contribute directly to biological and cognitive models of depression and highlight the importance of examining Gene × Cognition interactions when investigating risk for depression.

  7. Micmac Indian Social Work Education: A Model.

    Science.gov (United States)

    Smith, Ann F. V.; Pace, Jacqueline M.

    1987-01-01

    Describes founding, goals, admissions, and implementation of a five-year Micmac Bachelor of Social Work Program at Dalhousie University. Discusses advantages and problems of a decentralized program sponsored by diverse organizations/agencies. Outlines degree requirements, staff qualifications, student personal/financial needs, and program changes…

  8. Micmac Indian Social Work Education: A Model.

    Science.gov (United States)

    Smith, Ann F. V.; Pace, Jacqueline M.

    1987-01-01

    Describes founding, goals, admissions, and implementation of a five-year Micmac Bachelor of Social Work Program at Dalhousie University. Discusses advantages and problems of a decentralized program sponsored by diverse organizations/agencies. Outlines degree requirements, staff qualifications, student personal/financial needs, and program changes…

  9. Economic decision making and the application of nonparametric prediction models

    Science.gov (United States)

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

    2008-01-01

    Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.

  10. Predictive models of procedural human supervisory control behavior

    Science.gov (United States)

    Boussemart, Yves

    Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision J]l8king process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the

  11. Estimating the magnitude of prediction uncertainties for the APLE model

    Science.gov (United States)

    Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, we conduct an uncertainty analysis for the Annual P ...

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  14. The Beyond the Standard Model Working Group Summary Report

    CERN Document Server

    Azuelos, Georges; Hewett, J L; Landsberg, G L; Matchev, K; Paige, Frank E; Rizzo, T; Rurua, L; Abdullin, S; Albert, A; Allanach, Benjamin C; Blazek, T; Cavalli, D; Charles, F; Cheung, K; Dedes, A; Dimopoulos, Savas K; Dreiner, H; Ellwanger, Ulrich; Gorbunov, D S; Heinemeyer, S; Hinchliffe, Ian; Hugonie, C; Moretti, S; Polesello, G; Przysiezniak, H; Richardson, Peter; Vacavant, L; Weiglein, Georg

    2002-01-01

    Report of the "Beyond the Standard Model" working group for the Workshop `Physics at TeV Colliders', Les Houches, France, 21 May - 1 June 2001. It consists of 18 separate parts: 1. Preface; 2. Theoretical Discussion; 3. Numerical Calculation of the mSUGRA and Higgs Spectrum; 4. Theoretical Uncertainties in Sparticle Mass Predictions; 5. High Mass Supersymmetry with High Energy Hadron Colliders; 6. SUSY with Heavy Scalars at LHC; 7. Inclusive Study of MSSM in CMS; 8. Establishing a No-Lose Theorem for NMSSM Higgs Boson Discovery at the LHC; 9. Effects of Supersymmetric Phases on Higgs Production in Association with Squark Pairs in the Minimal Supersymmetric Standard Model; 10. Study of the Lepton Flavour Violating Decays of Charged Fermions in SUSY GUTs; 11. Interactions of the Goldstino Supermultiplet with Standard Model Fields; 12. Attempts at Explaining the NuTeV Observation of Di-Muon Events; 13. Kaluza-Klein States of the Standard Model Gauge Bosons: Constraints From High Energy Experiments; 14. Kaluza-Kl...

  15. Prediction of Catastrophes: an experimental model

    CERN Document Server

    Peters, Randall D; Pomeau, Yves

    2012-01-01

    Catastrophes of all kinds can be roughly defined as short duration-large amplitude events following and followed by long periods of "ripening". Major earthquakes surely belong to the class of 'catastrophic' events. Because of the space-time scales involved, an experimental approach is often difficult, not to say impossible, however desirable it could be. Described in this article is a "laboratory" setup that yields data of a type that is amenable to theoretical methods of prediction. Observations are made of a critical slowing down in the noisy signal of a solder wire creeping under constant stress. This effect is shown to be a fair signal of the forthcoming catastrophe in both of two dynamical models. The first is an "abstract" model in which a time dependent quantity drifts slowly but makes quick jumps from time to time. The second is a realistic physical model for the collective motion of dislocations (the Ananthakrishna set of equations for creep). Hope thus exists that similar changes in the response to ...

  16. Predictive modeling of low solubility semiconductor alloys

    Science.gov (United States)

    Rodriguez, Garrett V.; Millunchick, Joanna M.

    2016-09-01

    GaAsBi is of great interest for applications in high efficiency optoelectronic devices due to its highly tunable bandgap. However, the experimental growth of high Bi content films has proven difficult. Here, we model GaAsBi film growth using a kinetic Monte Carlo simulation that explicitly takes cation and anion reactions into account. The unique behavior of Bi droplets is explored, and a sharp decrease in Bi content upon Bi droplet formation is demonstrated. The high mobility of simulated Bi droplets on GaAsBi surfaces is shown to produce phase separated Ga-Bi droplets as well as depressions on the film surface. A phase diagram for a range of growth rates that predicts both Bi content and droplet formation is presented to guide the experimental growth of high Bi content GaAsBi films.

  17. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy

    2014-01-01

    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  18. Leptogenesis in minimal predictive seesaw models

    Science.gov (United States)

    Björkeroth, Fredrik; de Anda, Francisco J.; de Medeiros Varzielas, Ivo; King, Stephen F.

    2015-10-01

    We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the "atmospheric" and "solar" neutrino masses with Yukawa couplings to ( ν e , ν μ , ν τ ) proportional to (0, 1, 1) and (1, n, n - 2), respectively, where n is a positive integer. The neutrino Yukawa matrix is therefore characterised by two proportionality constants with their relative phase providing a leptogenesis-PMNS link, enabling the lightest right-handed neutrino mass to be determined from neutrino data and the observed BAU. We discuss an SU(5) SUSY GUT example, where A 4 vacuum alignment provides the required Yukawa structures with n = 3, while a {{Z}}_9 symmetry fixes the relatives phase to be a ninth root of unity.

  19. Simple predictive electron transport models applied to sawtoothing plasmas

    Science.gov (United States)

    Kim, D.; Merle, A.; Sauter, O.; Goodman, T. P.

    2016-05-01

    In this work, we introduce two simple transport models to evaluate the time evolution of electron temperature and density profiles during sawtooth cycles (i.e. over a sawtooth period time-scale). Since the aim of these simulations is to estimate reliable profiles within a short calculation time, two simplified ad-hoc models have been developed. The goal for these models is to rely on a few easy-to-check free parameters, such as the confinement time scaling factor and the profiles’ averaged scale-lengths. Due to the simplicity and short calculation time of the models, it is expected that these models can also be applied to real-time transport simulations. We show that it works well for Ohmic and EC heated L- and H-mode plasmas. The differences between these models are discussed and we show that their predictive capabilities are similar. Thus only one model is used to reproduce with simulations the results of sawtooth control experiments on the TCV tokamak. For the sawtooth pacing, the calculated time delays between the EC power off and sawtooth crash time agree well with the experimental results. The map of possible locking range is also well reproduced by the simulation.

  20. Could Freemium Models Work for Legacy Newspapers?

    DEFF Research Database (Denmark)

    Holm, Anna B.

    2016-01-01

    The newspaper industry has long been looking for sustainable business models for their digital editions. One of their popular choices is the freemium business model based on free and premium content with a paywall. However, freemium has not yet lived up to the expectation of the industry and has...... not secured the revenues that industry players hoped for. This article discusses a number of the main principles of the freemium strategy and tactics, and highlights the critical points for legacy newspaper organisations....

  1. Could Freemium Models Work for Legacy Newspapers?

    DEFF Research Database (Denmark)

    Holm, Anna B.

    2016-01-01

    The newspaper industry has long been looking for sustainable business models for their digital editions. One of their popular choices is the freemium business model based on free and premium content with a paywall. However, freemium has not yet lived up to the expectation of the industry and has...... not secured the revenues that industry players hoped for. This article discusses a number of the main principles of the freemium strategy and tactics, and highlights the critical points for legacy newspaper organisations....

  2. Visuospatial Working Memory Capacity Predicts Physiological Arousal in a Narrative Task.

    Science.gov (United States)

    Smithson, Lisa; Nicoladis, Elena

    2016-06-01

    Physiological arousal that occurs during narrative production is thought to reflect emotional processing and cognitive effort (Bar-Haim et al. in Dev Psychobiol 44:238-249, 2004). The purpose of this study was to determine whether individual differences in visuospatial working memory and/or verbal working memory capacity predict physiological arousal in a narrative task. Visuospatial working memory was a significant predictor of skin conductance level (SCL); verbal working memory was not. When visuospatial working memory interference was imposed, visuospatial working memory was no longer a significant predictor of SCL. Visuospatial interference also resulted in a significant reduction in SCL. Furthermore, listener ratings of narrative quality were contingent upon the visuospatial working memory resources of the narrator. Potential implications for educators and clinical practitioners are discussed.

  3. Addressing Conceptual Model Uncertainty in the Evaluation of Model Prediction Errors

    Science.gov (United States)

    Carrera, J.; Pool, M.

    2014-12-01

    Model predictions are uncertain because of errors in model parameters, future forcing terms, and model concepts. The latter remain the largest and most difficult to assess source of uncertainty in long term model predictions. We first review existing methods to evaluate conceptual model uncertainty. We argue that they are highly sensitive to the ingenuity of the modeler, in the sense that they rely on the modeler's ability to propose alternative model concepts. Worse, we find that the standard practice of stochastic methods leads to poor, potentially biased and often too optimistic, estimation of actual model errors. This is bad news because stochastic methods are purported to properly represent uncertainty. We contend that the problem does not lie on the stochastic approach itself, but on the way it is applied. Specifically, stochastic inversion methodologies, which demand quantitative information, tend to ignore geological understanding, which is conceptually rich. We illustrate some of these problems with the application to Mar del Plata aquifer, where extensive data are available for nearly a century. Geologically based models, where spatial variability is handled through zonation, yield calibration fits similar to geostatiscally based models, but much better predictions. In fact, the appearance of the stochastic T fields is similar to the geologically based models only in areas with high density of data. We take this finding to illustrate the ability of stochastic models to accommodate many data, but also, ironically, their inability to address conceptual model uncertainty. In fact, stochastic model realizations tend to be too close to the "most likely" one (i.e., they do not really realize the full conceptualuncertainty). The second part of the presentation is devoted to argue that acknowledging model uncertainty may lead to qualitatively different decisions than just working with "most likely" model predictions. Therefore, efforts should concentrate on

  4. A Predictive Model of Cell Traction Forces Based on Cell Geometry

    OpenAIRE

    Lemmon, Christopher A.; Romer, Lewis H

    2010-01-01

    Recent work has indicated that the shape and size of a cell can influence how a cell spreads, develops focal adhesions, and exerts forces on the substrate. However, it is unclear how cell shape regulates these events. Here we present a computational model that uses cell shape to predict the magnitude and direction of forces generated by cells. The predicted results are compared to experimentally measured traction forces, and show that the model can predict traction force direction, relative m...

  5. Optimal Model and Solution of Railway Hub Shift Working Plan

    Institute of Scientific and Technical Information of China (English)

    He Shiwei; Zhu Songnian; Lin Boliang

    1996-01-01

    Aiming at decreasing the hub transportation costs, a railway hub shift working plan in terms of multicommodity network flow model is set up for considering the coordination of freight working, train working and locomotive working plans. The solution and the calculating results are also introduced.

  6. Comparing model predictions for ecosystem-based management

    DEFF Research Database (Denmark)

    Jacobsen, Nis Sand; Essington, Timothy E.; Andersen, Ken Haste

    2016-01-01

    Ecosystem modeling is becoming an integral part of fisheries management, but there is a need to identify differences between predictions derived from models employed for scientific and management purposes. Here, we compared two models: a biomass-based food-web model (Ecopath with Ecosim (Ew......E)) and a size-structured fish community model. The models were compared with respect to predicted ecological consequences of fishing to identify commonalities and differences in model predictions for the California Current fish community. We compared the models regarding direct and indirect responses to fishing...... on one or more species. The size-based model predicted a higher fishing mortality needed to reach maximum sustainable yield than EwE for most species. The size-based model also predicted stronger top-down effects of predator removals than EwE. In contrast, EwE predicted stronger bottom-up effects...

  7. Factors influencing protein tyrosine nitration--structure-based predictive models.

    Science.gov (United States)

    Bayden, Alexander S; Yakovlev, Vasily A; Graves, Paul R; Mikkelsen, Ross B; Kellogg, Glen E

    2011-03-15

    Models for exploring tyrosine nitration in proteins have been created based on 3D structural features of 20 proteins for which high-resolution X-ray crystallographic or NMR data are available and for which nitration of 35 total tyrosines has been experimentally proven under oxidative stress. Factors suggested in previous work to enhance nitration were examined with quantitative structural descriptors. The role of neighboring acidic and basic residues is complex: for the majority of tyrosines that are nitrated the distance to the heteroatom of the closest charged side chain corresponds to the distance needed for suspected nitrating species to form hydrogen bond bridges between the tyrosine and that charged amino acid. This suggests that such bridges play a very important role in tyrosine nitration. Nitration is generally hindered for tyrosines that are buried and for those tyrosines for which there is insufficient space for the nitro group. For in vitro nitration, closed environments with nearby heteroatoms or unsaturated centers that can stabilize radicals are somewhat favored. Four quantitative structure-based models, depending on the conditions of nitration, have been developed for predicting site-specific tyrosine nitration. The best model, relevant for both in vitro and in vivo cases, predicts 30 of 35 tyrosine nitrations (positive predictive value) and has a sensitivity of 60/71 (11 false positives). Copyright © 2010 Elsevier Inc. All rights reserved.

  8. Spy Works: A Collaborative Creative Writing Model.

    Science.gov (United States)

    Bruce, Charlotte

    2001-01-01

    Students in a creative writing class at McLean High School (Virginia) were asked to write an original piece of spy fiction. A four-way collaboration model, which utilized the strengths and expertise of a teacher, library media specialist, business partner (the technology division of the CIA), and vendor, provided students with unusual learning…

  9. Work situation operative model MOST: linking diagnosis and intervention to improve work conditions.

    Science.gov (United States)

    Morales, Karen Lange; García-Acosta, Gabriel; Urueña-Télleze, William; Pérez, Adriana

    2012-01-01

    This paper presents the model "Work Situation Operative Model" - MOST (after its Spanish acronym). It offers a comprehensive, systemic approach to analysing work stations and/or work processes, serving also as a framework for pursuing various ergonomic and occupational health and safety goals. Originally produced for a food sector company, the model has been extended and successfully applied in several industries in Colombia and Ecuador, including cement, oil, and paper industries. Based on a systemic understanding of work systems and tasks, the model not only allows different, commonly-used methods and tools for evaluating or assessing the risk of muscular-sketetal disorders to be included, but also supports occupational risk management strategies. Hence, one of its more important contributions relies on providing meaningful information that is useful for improving the work station and/or work process through design and re-design, by focusing on the interactions between all system elements.

  10. Remaining Useful Lifetime (RUL - Probabilistic Predictive Model

    Directory of Open Access Journals (Sweden)

    Ephraim Suhir

    2011-01-01

    Full Text Available Reliability evaluations and assurances cannot be delayed until the device (system is fabricated and put into operation. Reliability of an electronic product should be conceived at the early stages of its design; implemented during manufacturing; evaluated (considering customer requirements and the existing specifications, by electrical, optical and mechanical measurements and testing; checked (screened during manufacturing (fabrication; and, if necessary and appropriate, maintained in the field during the product’s operation Simple and physically meaningful probabilistic predictive model is suggested for the evaluation of the remaining useful lifetime (RUL of an electronic device (system after an appreciable deviation from its normal operation conditions has been detected, and the increase in the failure rate and the change in the configuration of the wear-out portion of the bathtub has been assessed. The general concepts are illustrated by numerical examples. The model can be employed, along with other PHM forecasting and interfering tools and means, to evaluate and to maintain the high level of the reliability (probability of non-failure of a device (system at the operation stage of its lifetime.

  11. A Predictive Model of Geosynchronous Magnetopause Crossings

    CERN Document Server

    Dmitriev, A; Chao, J -K

    2013-01-01

    We have developed a model predicting whether or not the magnetopause crosses geosynchronous orbit at given location for given solar wind pressure Psw, Bz component of interplanetary magnetic field (IMF) and geomagnetic conditions characterized by 1-min SYM-H index. The model is based on more than 300 geosynchronous magnetopause crossings (GMCs) and about 6000 minutes when geosynchronous satellites of GOES and LANL series are located in the magnetosheath (so-called MSh intervals) in 1994 to 2001. Minimizing of the Psw required for GMCs and MSh intervals at various locations, Bz and SYM-H allows describing both an effect of magnetopause dawn-dusk asymmetry and saturation of Bz influence for very large southward IMF. The asymmetry is strong for large negative Bz and almost disappears when Bz is positive. We found that the larger amplitude of negative SYM-H the lower solar wind pressure is required for GMCs. We attribute this effect to a depletion of the dayside magnetic field by a storm-time intensification of t...

  12. Predictive modeling for EBPC in EBDW

    Science.gov (United States)

    Zimmermann, Rainer; Schulz, Martin; Hoppe, Wolfgang; Stock, Hans-Jürgen; Demmerle, Wolfgang; Zepka, Alex; Isoyan, Artak; Bomholt, Lars; Manakli, Serdar; Pain, Laurent

    2009-10-01

    We demonstrate a flow for e-beam proximity correction (EBPC) to e-beam direct write (EBDW) wafer manufacturing processes, demonstrating a solution that covers all steps from the generation of a test pattern for (experimental or virtual) measurement data creation, over e-beam model fitting, proximity effect correction (PEC), and verification of the results. We base our approach on a predictive, physical e-beam simulation tool, with the possibility to complement this with experimental data, and the goal of preparing the EBPC methods for the advent of high-volume EBDW tools. As an example, we apply and compare dose correction and geometric correction for low and high electron energies on 1D and 2D test patterns. In particular, we show some results of model-based geometric correction as it is typical for the optical case, but enhanced for the particularities of e-beam technology. The results are used to discuss PEC strategies, with respect to short and long range effects.

  13. Dual Mission: An Innovative Field Model for Training Social Work Students for Work with Veterans

    Science.gov (United States)

    Selber, Katherine; Chavkin, Nancy Feyl; Biggs, Mary Jo Garcia

    2015-01-01

    This descriptive article explores a collaborative model that blends the dual missions of training social work students to work with military personnel, veterans, and their families while serving student veterans on campus. The model consists of 2 main components: (1) a nationally recognized service component for providing academic, health and…

  14. Dual Mission: An Innovative Field Model for Training Social Work Students for Work with Veterans

    Science.gov (United States)

    Selber, Katherine; Chavkin, Nancy Feyl; Biggs, Mary Jo Garcia

    2015-01-01

    This descriptive article explores a collaborative model that blends the dual missions of training social work students to work with military personnel, veterans, and their families while serving student veterans on campus. The model consists of 2 main components: (1) a nationally recognized service component for providing academic, health and…

  15. Job stress models for predicting burnout syndrome: a review.

    Science.gov (United States)

    Chirico, Francesco

    2016-01-01

    In Europe, the Council Directive 89/391 for improvement of workers' safety and health has emphasized the importance of addressing all occupational risk factors, and hence also psychosocial and organizational risk factors. Nevertheless, the construct of "work-related stress" elaborated from EU-OSHA is not totally corresponding with the "psychosocial" risk, that is a broader category of risk, comprising various and different psychosocial risk factors. The term "burnout", without any binding definition, tries to integrate symptoms as well as cause of the burnout process. In Europe, the most important methods developed for the work related stress risk assessment are based on the Cox's transactional model of job stress. Nevertheless, there are more specific models for predicting burnout syndrome. This literature review provides an overview of job burnout, highlighting the most important models of job burnout, such as the Job Strain, the Effort/Reward Imbalance and the Job Demands-Resources models. The difference between these models and the Cox's model of job stress is explored.

  16. Factors Associated with Return to Work Postinjury: Can the Modified Rankin Scale Be Used to Predict Return to Work?

    Science.gov (United States)

    Kohli, Anirudh; Chao, Edward; Spielman, Daniel; Sugano, Dordaneh; Srivastava, Abhishek; Dayama, Anand; Lederman, Andrew; Stern, Michelle; Reddy, Srinivas H; Teperman, Sheldon; Stone, Melvin E

    2016-02-01

    The ability to return to work (RTW) postinjury is one of the primary goals of rehabilitation. The modified Rankin Scale (mRS) is a validated simple scale used to assess the functional status of stroke patients during rehabilitation. We sought to determine the applicability of mRS in predicting RTW postinjury in a general trauma population. The trauma registry was queried for patients, aged 18 to 65 years, discharged from 2012 to 2013. A telephone interview for each patient included questions about employment status and physical ability to determine the mRS. Patients who had RTW postinjury were compared with those who had not (nRTW). Two hundred and thirty-four patients met the inclusion criteria. Of these, 171 (72.5%) patients RTW and 63 (26.7%) did nRTW. Patients who did nRTW were significantly older, had longer length of stay and higher rates of in-hospital complications. Multivariate analysis revealed that older patients were less likely to RTW (odds ratio = 0.961, P = 0.011) and patients with a modified Rankin score ≤2 were 15 times more likely to RTW (odds ratio = 14.932, P returning to work postinjury. This is the first study that shows applicability of the mRS for predicting RTW postinjury in a trauma population.

  17. Ontology-based tools to expedite predictive model construction.

    Science.gov (United States)

    Haug, Peter; Holmen, John; Wu, Xinzi; Mynam, Kumar; Ebert, Matthew; Ferraro, Jeffrey

    2014-01-01

    Large amounts of medical data are collected electronically during the course of caring for patients using modern medical information systems. This data presents an opportunity to develop clinically useful tools through data mining and observational research studies. However, the work necessary to make sense of this data and to integrate it into a research initiative can require substantial effort from medical experts as well as from experts in medical terminology, data extraction, and data analysis. This slows the process of medical research. To reduce the effort required for the construction of computable, diagnostic predictive models, we have developed a system that hybridizes a medical ontology with a large clinical data warehouse. Here we describe components of this system designed to automate the development of preliminary diagnostic models and to provide visual clues that can assist the researcher in planning for further analysis of the data behind these models.

  18. Preliminary results from a four-working space, double-acting piston, Stirling engine controls model

    Science.gov (United States)

    Daniele, C. J.; Lorenzo, C. F.

    1980-01-01

    A four working space, double acting piston, Stirling engine simulation is being developed for controls studies. The development method is to construct two simulations, one for detailed fluid behavior, and a second model with simple fluid behaviour but containing the four working space aspects and engine inertias, validate these models separately, then upgrade the four working space model by incorporating the detailed fluid behaviour model for all four working spaces. The single working space (SWS) model contains the detailed fluid dynamics. It has seven control volumes in which continuity, energy, and pressure loss effects are simulated. Comparison of the SWS model with experimental data shows reasonable agreement in net power versus speed characteristics for various mean pressure levels in the working space. The four working space (FWS) model was built to observe the behaviour of the whole engine. The drive dynamics and vehicle inertia effects are simulated. To reduce calculation time, only three volumes are used in each working space and the gas temperature are fixed (no energy equation). Comparison of the FWS model predicted power with experimental data shows reasonable agreement. Since all four working spaces are simulated, the unique capabilities of the model are exercised to look at working fluid supply transients, short circuit transients, and piston ring leakage effects.

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

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

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

  20. RFI modeling and prediction approach for SATOP applications: RFI prediction models

    Science.gov (United States)

    Nguyen, Tien M.; Tran, Hien T.; Wang, Zhonghai; Coons, Amanda; Nguyen, Charles C.; Lane, Steven A.; Pham, Khanh D.; Chen, Genshe; Wang, Gang

    2016-05-01

    This paper describes a technical approach for the development of RFI prediction models using carrier synchronization loop when calculating Bit or Carrier SNR degradation due to interferences for (i) detecting narrow-band and wideband RFI signals, and (ii) estimating and predicting the behavior of the RFI signals. The paper presents analytical and simulation models and provides both analytical and simulation results on the performance of USB (Unified S-Band) waveforms in the presence of narrow-band and wideband RFI signals. The models presented in this paper will allow the future USB command systems to detect the RFI presence, estimate the RFI characteristics and predict the RFI behavior in real-time for accurate assessment of the impacts of RFI on the command Bit Error Rate (BER) performance. The command BER degradation model presented in this paper also allows the ground system operator to estimate the optimum transmitted SNR to maintain a required command BER level in the presence of both friendly and un-friendly RFI sources.

  1. The Identification of a Threshold of Long Work Hours for Predicting Elevated Risks of Adverse Health Outcomes.

    Science.gov (United States)

    Conway, Sadie H; Pompeii, Lisa A; Gimeno Ruiz de Porras, David; Follis, Jack L; Roberts, Robert E

    2017-07-15

    Working long hours has been associated with adverse health outcomes. However, a definition of long work hours relative to adverse health risk has not been established. Repeated measures of work hours among approximately 2,000 participants from the Panel Study of Income Dynamics (1986-2011), conducted in the United States, were retrospectively analyzed to derive statistically optimized cutpoints of long work hours that best predicted three health outcomes. Work-hours cutpoints were assessed for model fit, calibration, and discrimination separately for the outcomes of poor self-reported general health, incident cardiovascular disease, and incident cancer. For each outcome, the work-hours threshold that best predicted increased risk was 52 hours per week or more for a minimum of 10 years. Workers exposed at this level had a higher risk of poor self-reported general health (relative risk (RR) = 1.28; 95% confidence interval (CI): 1.06, 1.53), cardiovascular disease (RR = 1.42; 95% CI: 1.24, 1.63), and cancer (RR = 1.62; 95% CI: 1.22, 2.17) compared with those working 35-51 hours per week for the same duration. This study provides the first health risk-based definition of long work hours. Further examination of the predictive power of this cutpoint on other health outcomes and in other study populations is needed. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  2. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to unders

  3. Work conditions and employees' self-set goals: goal processes enhance prediction of psychological distress and well-being.

    Science.gov (United States)

    Pomaki, Georgia; Maes, Stan; Ter Doest, Laura

    2004-06-01

    Although previous theory and research suggest that employee well-being should be predicted by work conditions (viz., Karasek and colleagues' job demands-control-social support [J-DCS] model), other factors are also likely to be important. In this study, the authors consider correlates of employee psychological distress and well-being using a goal-focused approach grounded in Ford's (1992) motivational systems theory. Specifically, work conditions and midlevel work goal processes (WGP) were examined in a questionnaire study of health care employees. Regarding predictions derived from the J-DCS model, the authors found full support for the iso-strain, partial support for the nonlinearity, and no support for the buffer hypothesis. Of importance, however, WGP (i.e., cognitions and emotions involved in the pursuit of self-set work goals) explained variance in job satisfaction, burnout, depression, and somatic complaints, over and above that of the J-DCS model. This suggests that investigation of WGP can enhance our understanding of employee psychological distress and well-being.

  4. Can Functional Capacity Tests Predict Future Work Capacity in Patients With Whiplash-Associated Disorders?

    NARCIS (Netherlands)

    Trippolini, Maurizio A.; Dijkstra, Pieter U.; Cote, Pierre; Scholz-Odermatt, Stefan M.; Geertzen, J.H.B.; Reneman, Michiel F.

    2014-01-01

    Objective: To determine whether functional capacity evaluation (FCE) tests predict future work capacity (WC) of patients with whiplash-associated disorders (WADs) grades I and II who did not regain full WC 6 to 12 weeks after injury. Design: Prospective cohort study. Setting: Rehabilitation center.

  5. Profiles of Verbal Working Memory Growth Predict Speech and Language Development in Children with Cochlear Implants

    Science.gov (United States)

    Kronenberger, William G.; Pisoni, David B.; Harris, Michael S.; Hoen, Helena M.; Xu, Huiping; Miyamoto, Richard T.

    2013-01-01

    Purpose: Verbal short-term memory (STM) and working memory (WM) skills predict speech and language outcomes in children with cochlear implants (CIs) even after conventional demographic, device, and medical factors are taken into account. However, prior research has focused on single end point outcomes as opposed to the longitudinal process of…

  6. Factors Complicating Expectancy Theory Predictions of Work Motivation and Job Performance.

    Science.gov (United States)

    Kopelman, Richard E.

    The conventional paradigm for testing expectancy theory predictions of work behavior has been to correlate expectancy-value reports with concurrent measures of motivation and performance. Although this static, two-variable approach has typically yielded statistically significant results, correlations have not been sizable. This study, using a…

  7. An approach to model validation and model-based prediction -- polyurethane foam case study.

    Energy Technology Data Exchange (ETDEWEB)

    Dowding, Kevin J.; Rutherford, Brian Milne

    2003-07-01

    Enhanced software methodology and improved computing hardware have advanced the state of simulation technology to a point where large physics-based codes can be a major contributor in many systems analyses. This shift toward the use of computational methods has brought with it new research challenges in a number of areas including characterization of uncertainty, model validation, and the analysis of computer output. It is these challenges that have motivated the work described in this report. Approaches to and methods for model validation and (model-based) prediction have been developed recently in the engineering, mathematics and statistical literatures. In this report we have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical

  8. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen

    2014-01-01

    Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

  9. Predictability of the Indian Ocean Dipole in the coupled models

    Science.gov (United States)

    Liu, Huafeng; Tang, Youmin; Chen, Dake; Lian, Tao

    2017-03-01

    In this study, the Indian Ocean Dipole (IOD) predictability, measured by the Indian Dipole Mode Index (DMI), is comprehensively examined at the seasonal time scale, including its actual prediction skill and potential predictability, using the ENSEMBLES multiple model ensembles and the recently developed information-based theoretical framework of predictability. It was found that all model predictions have useful skill, which is normally defined by the anomaly correlation coefficient larger than 0.5, only at around 2-3 month leads. This is mainly because there are more false alarms in predictions as leading time increases. The DMI predictability has significant seasonal variation, and the predictions whose target seasons are boreal summer (JJA) and autumn (SON) are more reliable than that for other seasons. All of models fail to predict the IOD onset before May and suffer from the winter (DJF) predictability barrier. The potential predictability study indicates that, with the model development and initialization improvement, the prediction of IOD onset is likely to be improved but the winter barrier cannot be overcome. The IOD predictability also has decadal variation, with a high skill during the 1960s and the early 1990s, and a low skill during the early 1970s and early 1980s, which is very consistent with the potential predictability. The main factors controlling the IOD predictability, including its seasonal and decadal variations, are also analyzed in this study.

  10. Nonconvex model predictive control for commercial refrigeration

    Science.gov (United States)

    Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John

    2013-08-01

    We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

  11. Leptogenesis in minimal predictive seesaw models

    Energy Technology Data Exchange (ETDEWEB)

    Björkeroth, Fredrik [School of Physics and Astronomy, University of Southampton,Southampton, SO17 1BJ (United Kingdom); Anda, Francisco J. de [Departamento de Física, CUCEI, Universidad de Guadalajara,Guadalajara (Mexico); Varzielas, Ivo de Medeiros; King, Stephen F. [School of Physics and Astronomy, University of Southampton,Southampton, SO17 1BJ (United Kingdom)

    2015-10-15

    We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the “atmospheric” and “solar” neutrino masses with Yukawa couplings to (ν{sub e},ν{sub μ},ν{sub τ}) proportional to (0,1,1) and (1,n,n−2), respectively, where n is a positive integer. The neutrino Yukawa matrix is therefore characterised by two proportionality constants with their relative phase providing a leptogenesis-PMNS link, enabling the lightest right-handed neutrino mass to be determined from neutrino data and the observed BAU. We discuss an SU(5) SUSY GUT example, where A{sub 4} vacuum alignment provides the required Yukawa structures with n=3, while a ℤ{sub 9} symmetry fixes the relatives phase to be a ninth root of unity.

  12. QSPR Models for Octane Number Prediction

    Directory of Open Access Journals (Sweden)

    Jabir H. Al-Fahemi

    2014-01-01

    Full Text Available Quantitative structure-property relationship (QSPR is performed as a means to predict octane number of hydrocarbons via correlating properties to parameters calculated from molecular structure; such parameters are molecular mass M, hydration energy EH, boiling point BP, octanol/water distribution coefficient logP, molar refractivity MR, critical pressure CP, critical volume CV, and critical temperature CT. Principal component analysis (PCA and multiple linear regression technique (MLR were performed to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The results of PCA explain the interrelationships between octane number and different variables. Correlation coefficients were calculated using M.S. Excel to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The data set was split into training of 40 hydrocarbons and validation set of 25 hydrocarbons. The linear relationship between the selected descriptors and the octane number has coefficient of determination (R2=0.932, statistical significance (F=53.21, and standard errors (s =7.7. The obtained QSPR model was applied on the validation set of octane number for hydrocarbons giving RCV2=0.942 and s=6.328.

  13. Hologram QSAR model for the prediction of human oral bioavailability.

    Science.gov (United States)

    Moda, Tiago L; Montanari, Carlos A; Andricopulo, Adriano D

    2007-12-15

    A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure-activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q(2)=0.70, r(2)=0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.

  14. Acoustic Predictions in Industrial Spaces Using a Diffusion Model

    Directory of Open Access Journals (Sweden)

    Alexis Billon

    2012-01-01

    Full Text Available Industrial spaces are known to be very noisy working environment. This noise exposure can be uncomfortable, tiring, or even harmful, at worst. Industrial spaces have several characteristics: they are often huge flat volumes fitted with many obstacles and sound sources. Moreover, they are usually surrounded by rooms where low noise levels are required. The existing prediction tools can seldom model all these phenomena accurately. In this paper, a prediction model based on a diffusion equation is presented. The successive developments carried out to deal with the various propagating phenomena met in industrial spaces are shown. For each phenomenon, numerical or experimental examples are given to highlight the validity of this model. It is also shown that its computation load is very little in comparison to ray-tracing-based methods. In addition, this model can be used as a reliable and flexible tool to study the physics of the coupling between rooms. Finally, an application to a virtual factory is presented.

  15. Flow stress prediction for B210P steel at hot working conditions

    Science.gov (United States)

    Jiang, Guangwei; Di, Hongshuang; Cao, Yu; Zhang, Zhongwei; Wang, Yafei; Sui, Pengfei

    2013-05-01

    Prediction of the flow stress is a significant step to optimize the hot working processes. In order to establish a proper deformation constitutive equation, the compressive deformation behavior of B210P steel was investigated at temperature from 950° to 1150° and strain rates from 0.1s-1 to 10s-1 on a Gleeble-2000 thermo-simulation machine. Based on the true stress-strain data from flow stress curves, a revised model describing the relationships of the flow stress, strain rate and temperature of B210P steel at elevated temperatures is proposed considering the effect of strain on flow stress. The activation energies have been in the range of 277.740-420.241kJ/mol for different amounts of strain. Finally, the accuracy of the developed constitutive equation has been verified using standard statistical parameters. The results confirm that the developed strain-dependent constitutive equation gives an accurate and precise estimate of the flow stress in the relevant deformation conditions.

  16. Statistical modeling of a considering work-piece

    Directory of Open Access Journals (Sweden)

    Cornelia Victoria Anghel

    2008-10-01

    Full Text Available In this article are presented the stochastic predictive models for controlling properly the independent variables of the drilling operation a combined approach of statistical design and Response Surface Methodology (RSM.

  17. The Beyond the standard model working group: Summary report

    Energy Technology Data Exchange (ETDEWEB)

    G. Azuelos et al.

    2004-03-18

    In this working group we have investigated a number of aspects of searches for new physics beyond the Standard Model (SM) at the running or planned TeV-scale colliders. For the most part, we have considered hadron colliders, as they will define particle physics at the energy frontier for the next ten years at least. The variety of models for Beyond the Standard Model (BSM) physics has grown immensely. It is clear that only future experiments can provide the needed direction to clarify the correct theory. Thus, our focus has been on exploring the extent to which hadron colliders can discover and study BSM physics in various models. We have placed special emphasis on scenarios in which the new signal might be difficult to find or of a very unexpected nature. For example, in the context of supersymmetry (SUSY), we have considered: how to make fully precise predictions for the Higgs bosons as well as the superparticles of the Minimal Supersymmetric Standard Model (MSSM) (parts III and IV); MSSM scenarios in which most or all SUSY particles have rather large masses (parts V and VI); the ability to sort out the many parameters of the MSSM using a variety of signals and study channels (part VII); whether the no-lose theorem for MSSM Higgs discovery can be extended to the next-to-minimal Supersymmetric Standard Model (NMSSM) in which an additional singlet superfield is added to the minimal collection of superfields, potentially providing a natural explanation of the electroweak value of the parameter {micro} (part VIII); sorting out the effects of CP violation using Higgs plus squark associate production (part IX); the impact of lepton flavor violation of various kinds (part X); experimental possibilities for the gravitino and its sgoldstino partner (part XI); what the implications for SUSY would be if the NuTeV signal for di-muon events were interpreted as a sign of R-parity violation (part XII). Our other main focus was on the phenomenological implications of extra

  18. The Beyond the standard model working group: Summary report

    Energy Technology Data Exchange (ETDEWEB)

    G. Azuelos et al.

    2004-03-18

    In this working group we have investigated a number of aspects of searches for new physics beyond the Standard Model (SM) at the running or planned TeV-scale colliders. For the most part, we have considered hadron colliders, as they will define particle physics at the energy frontier for the next ten years at least. The variety of models for Beyond the Standard Model (BSM) physics has grown immensely. It is clear that only future experiments can provide the needed direction to clarify the correct theory. Thus, our focus has been on exploring the extent to which hadron colliders can discover and study BSM physics in various models. We have placed special emphasis on scenarios in which the new signal might be difficult to find or of a very unexpected nature. For example, in the context of supersymmetry (SUSY), we have considered: how to make fully precise predictions for the Higgs bosons as well as the superparticles of the Minimal Supersymmetric Standard Model (MSSM) (parts III and IV); MSSM scenarios in which most or all SUSY particles have rather large masses (parts V and VI); the ability to sort out the many parameters of the MSSM using a variety of signals and study channels (part VII); whether the no-lose theorem for MSSM Higgs discovery can be extended to the next-to-minimal Supersymmetric Standard Model (NMSSM) in which an additional singlet superfield is added to the minimal collection of superfields, potentially providing a natural explanation of the electroweak value of the parameter {micro} (part VIII); sorting out the effects of CP violation using Higgs plus squark associate production (part IX); the impact of lepton flavor violation of various kinds (part X); experimental possibilities for the gravitino and its sgoldstino partner (part XI); what the implications for SUSY would be if the NuTeV signal for di-muon events were interpreted as a sign of R-parity violation (part XII). Our other main focus was on the phenomenological implications of extra

  19. Model Predictive Control of a Continuous Vacuum Crystalliser in an Industrial Environment: A Feasibility Study

    OpenAIRE

    Moldoványi, N.; Abonyi, J.

    2009-01-01

    Crystallisers are essentially multivariable systems with high interaction amongst the process variables. Model Predictive Controllers (MPC) can handle such highly interacting multivariable systems efficiently due to their coordinated approach. In the absence of a real continuous crystalliser, a detailed momentum-model was applied using the process simulator in Simulink. This process has been controlled by a model predictive controller widely used in industry. A new framework has been worke...

  20. Do burnout and work engagement predict depressive symptoms and life satisfaction? A three-wave seven-year prospective study.

    Science.gov (United States)

    Hakanen, Jari J; Schaufeli, Wilmar B

    2012-12-10

    Burnout and work engagement have been viewed as opposite, yet distinct states of employee well-being. We investigated whether work-related indicators of well-being (i.e. burnout and work engagement) spill-over and generalize to context-free well-being (i.e. depressive symptoms and life satisfaction). More specifically, we examined the causal direction: does burnout/work engagement lead to depressive symptoms/life satisfaction, or the other way around? Three surveys were conducted. In 2003, 71% of all Finnish dentists were surveyed (n=3255), and the response rate of the 3-year follow-up was 84% (n=2555). The second follow-up was conducted four years later with a response rate of 86% (n=1964). Structural equation modeling was used to investigate the cross-lagged associations between the study variables across time. Burnout predicted depressive symptoms and life dissatisfaction from T1 to T2 and from T2 to T3. Conversely, work engagement had a negative effect on depressive symptoms and a positive effect on life satisfaction, both from T1 to T2 and from T2 to T3, even after adjusting for the impact of burnout at every occasion. The study was conducted among one occupational group, which limits its generalizability. Work-related well-being predicts general wellbeing in the long-term. For example, burnout predicts depressive symptoms and not vice versa. In addition, burnout and work engagement are not direct opposites. Instead, both have unique, incremental impacts on life satisfaction and depressive symptoms. Copyright © 2012 Elsevier B.V. All rights reserved.

  1. Predictability in models of the atmospheric circulation.

    NARCIS (Netherlands)

    Houtekamer, P.L.

    1992-01-01

    It will be clear from the above discussions that skill forecasts are still in their infancy. Operational skill predictions do not exist. One is still struggling to prove that skill predictions, at any range, have any quality at all. It is not clear what the statistics of the analysis error are. The

  2. A Model for Flooding Prediction in Circular Tubes

    Institute of Scientific and Technical Information of China (English)

    G.P.Celate; S.Banerjee; 等

    1992-01-01

    Flooding phenomenon limits the stability and the flow of a liquid film falling along the walls of a channel in which a gas in flowing upwards.As knows,the entrainment effect can completely prevent the liquid to fall from its natural flow.The resesent work proposes a new mechanistic model for the prediction of the onset of floodung in vertical and inclined pipes in the presence of obstructions,as well as taking into account the viscosity effect.The good performance of the model in the different geometrical conditions and for variable viscosities of the liquid component assesses the validity of the hypothesis that the instability of a wavelike disturbance limits the countercurrent flow in a channel.

  3. Nonlinear Model Predictive Control for Oil Reservoirs Management

    DEFF Research Database (Denmark)

    Capolei, Andrea

    . With this objective function we link the optimization problem in production optimization to the Markowitz portfolio optimization problem in finance or to the the robust design problem in topology optimization. In this study we focus on open-loop configuration, i.e. without measurement feedback. We demonstrate......, the research community is working on improving current feedback model-based optimal control technologies. The topic of this thesis is production optimization for water flooding in the secondary phase of oil recovery. We developed numerical methods for nonlinear model predictive control (NMPC) of an oil field....... Further, we studied the use of robust control strategies in both open-loop, i.e. without measurement feedback, and closed-loop, i.e. with measurement feedback, configurations. This thesis has three main original contributions: The first contribution in this thesis is to improve the computationally...

  4. A unified spray forming model for the prediction of billet shape geometry

    DEFF Research Database (Denmark)

    Hattel, Jesper; Pryds, Nini

    2004-01-01

    In the present work a unified model for simulating the spray forming process has been developed. Models for the atomization and the deposition processes have been coupled together in order to obtain a new unified description of the spray forming process. The model is able to predict the shape and...

  5. The LGBTQ Responsive Model for Supervision of Group Work

    Science.gov (United States)

    Goodrich, Kristopher M.; Luke, Melissa

    2011-01-01

    Although supervision of group work has been linked to the development of multicultural and social justice competencies, there are no models for supervision of group work specifically designed to address the needs of lesbian, gay, bisexual, transgender, and questioning (LGBTQ) persons. This manuscript presents the LGBTQ Responsive Model for…

  6. The LGBTQ Responsive Model for Supervision of Group Work

    Science.gov (United States)

    Goodrich, Kristopher M.; Luke, Melissa

    2011-01-01

    Although supervision of group work has been linked to the development of multicultural and social justice competencies, there are no models for supervision of group work specifically designed to address the needs of lesbian, gay, bisexual, transgender, and questioning (LGBTQ) persons. This manuscript presents the LGBTQ Responsive Model for…

  7. Working group report: Flavor physics and model building

    Indian Academy of Sciences (India)

    M K Parida; Nita Sinha; B Adhikary; B Allanach; A Alok; K S Babu; B Brahmachari; D Choudhury; E J Chun; P K Das; A Ghosal; D Hitlin; W S Hou; S Kumar; H N Li; E Ma; S K Majee; G Majumdar; B Mishra; G Mohanty; S Nandi; H Pas; M K Parida; S D Rindani; J P Saha; N Sahu; Y Sakai; S Sen; C Sharma; C D Sharma; S Shalgar; N N Singh; S Uma Sankar; N Sinha; R Sinha; F Simonetto; R Srikanth; R Vaidya

    2006-11-01

    This is the report of flavor physics and model building working group at WHEPP-9. While activities in flavor physics have been mainly focused on -physics, those in model building have been primarily devoted to neutrino physics. We present summary of working group discussions carried out during the workshop in the above fields, and also briefly review the progress made in some projects subsequently

  8. I Like, I Cite? Do Facebook Likes Predict the Impact of Scientific Work?

    Science.gov (United States)

    Ringelhan, Stefanie; Wollersheim, Jutta; Welpe, Isabell M

    2015-01-01

    Due to the increasing amount of scientific work and the typical delays in publication, promptly assessing the impact of scholarly work is a huge challenge. To meet this challenge, one solution may be to create and discover innovative indicators. The goal of this paper is to investigate whether Facebook likes for unpublished manuscripts that are uploaded to the Internet could be used as an early indicator of the future impact of the scientific work. To address our research question, we compared Facebook likes for manuscripts uploaded to the Harvard Business School website (Study 1) and the bioRxiv website (Study 2) with traditional impact indicators (journal article citations, Impact Factor, Immediacy Index) for those manuscripts that have been published as a journal article. Although based on our full sample of Study 1 (N = 170), Facebook likes do not predict traditional impact indicators, for manuscripts with one or more Facebook likes (n = 95), our results indicate that the more Facebook likes a manuscript receives, the more journal article citations the manuscript receives. In additional analyses (for which we categorized the manuscripts as psychological and non-psychological manuscripts), we found that the significant prediction of citations stems from the psychological and not the non-psychological manuscripts. In Study 2, we observed that Facebook likes (N = 270) and non-zero Facebook likes (n = 84) do not predict traditional impact indicators. Taken together, our findings indicate an interdisciplinary difference in the predictive value of Facebook likes, according to which Facebook likes only predict citations in the psychological area but not in the non-psychological area of business or in the field of life sciences. Our paper contributes to understanding the possibilities and limits of the use of social media indicators as potential early indicators of the impact of scientific work.

  9. I Like, I Cite? Do Facebook Likes Predict the Impact of Scientific Work?

    Directory of Open Access Journals (Sweden)

    Stefanie Ringelhan

    Full Text Available Due to the increasing amount of scientific work and the typical delays in publication, promptly assessing the impact of scholarly work is a huge challenge. To meet this challenge, one solution may be to create and discover innovative indicators. The goal of this paper is to investigate whether Facebook likes for unpublished manuscripts that are uploaded to the Internet could be used as an early indicator of the future impact of the scientific work. To address our research question, we compared Facebook likes for manuscripts uploaded to the Harvard Business School website (Study 1 and the bioRxiv website (Study 2 with traditional impact indicators (journal article citations, Impact Factor, Immediacy Index for those manuscripts that have been published as a journal article. Although based on our full sample of Study 1 (N = 170, Facebook likes do not predict traditional impact indicators, for manuscripts with one or more Facebook likes (n = 95, our results indicate that the more Facebook likes a manuscript receives, the more journal article citations the manuscript receives. In additional analyses (for which we categorized the manuscripts as psychological and non-psychological manuscripts, we found that the significant prediction of citations stems from the psychological and not the non-psychological manuscripts. In Study 2, we observed that Facebook likes (N = 270 and non-zero Facebook likes (n = 84 do not predict traditional impact indicators. Taken together, our findings indicate an interdisciplinary difference in the predictive value of Facebook likes, according to which Facebook likes only predict citations in the psychological area but not in the non-psychological area of business or in the field of life sciences. Our paper contributes to understanding the possibilities and limits of the use of social media indicators as potential early indicators of the impact of scientific work.

  10. Related work on reference modeling for collaborative networks

    NARCIS (Netherlands)

    Afsarmanesh, H.; Camarinha-Matos, L.M.; Camarinha-Matos, L.M.; Afsarmanesh, H.

    2008-01-01

    Several international research and development initiatives have led to development of models for organizations and organization interactions. These models and their approaches constitute a background for development of reference models for collaborative networks. A brief survey of work on modeling t

  11. Allostasis: a model of predictive regulation.

    Science.gov (United States)

    Sterling, Peter

    2012-04-12

    The premise of the standard regulatory model, "homeostasis", is flawed: the goal of regulation is not to preserve constancy of the internal milieu. Rather, it is to continually adjust the milieu to promote survival and reproduction. Regulatory mechanisms need to be efficient, but homeostasis (error-correction by feedback) is inherently inefficient. Thus, although feedbacks are certainly ubiquitous, they could not possibly serve as the primary regulatory mechanism. A newer model, "allostasis", proposes that efficient regulation requires anticipating needs and preparing to satisfy them before they arise. The advantages: (i) errors are reduced in magnitude and frequency; (ii) response capacities of different components are matched -- to prevent bottlenecks and reduce safety factors; (iii) resources are shared between systems to minimize reserve capacities; (iv) errors are remembered and used to reduce future errors. This regulatory strategy requires a dedicated organ, the brain. The brain tracks multitudinous variables and integrates their values with prior knowledge to predict needs and set priorities. The brain coordinates effectors to mobilize resources from modest bodily stores and enforces a system of flexible trade-offs: from each organ according to its ability, to each organ according to its need. The brain also helps regulate the internal milieu by governing anticipatory behavior. Thus, an animal conserves energy by moving to a warmer place - before it cools, and it conserves salt and water by moving to a cooler one before it sweats. The behavioral strategy requires continuously updating a set of specific "shopping lists" that document the growing need for each key component (warmth, food, salt, water). These appetites funnel into a common pathway that employs a "stick" to drive the organism toward filling the need, plus a "carrot" to relax the organism when the need is satisfied. The stick corresponds broadly to the sense of anxiety, and the carrot broadly to

  12. Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India

    Directory of Open Access Journals (Sweden)

    Vipul K. Dabhi

    2014-01-01

    Full Text Available An accurate prediction of rainfall is crucial for national economy and management of water resources. The variability of rainfall in both time and space makes the rainfall prediction a challenging task. The present work investigates the applicability of a hybrid wavelet-postfix-GP model for daily rainfall prediction of Anand region using meteorological variables. The wavelet analysis is used as a data preprocessing technique to remove the stochastic (noise component from the original time series of each meteorological variable. The Postfix-GP, a GP variant, and ANN are then employed to develop models for rainfall using newly generated subseries of meteorological variables. The developed models are then used for rainfall prediction. The out-of-sample prediction performance of Postfix-GP and ANN models is compared using statistical measures. The results are comparable and suggest that Postfix-GP could be explored as an alternative tool for rainfall prediction.

  13. A prediction model for assessing residential radon concentration in Switzerland

    NARCIS (Netherlands)

    Hauri, D.D.; Huss, A.; Zimmermann, F.; Kuehni, C.E.; Roosli, M.

    2012-01-01

    Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the

  14. Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory

    Directory of Open Access Journals (Sweden)

    Stephen eGrossberg

    2014-10-01

    Full Text Available How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list

  15. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    B. Wayne Bequette; Priyadarshi Mahapatra

    2010-08-31

    The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.

  16. Usability Prediction & Ranking of SDLC Models Using Fuzzy Hierarchical Usability Model

    Science.gov (United States)

    Gupta, Deepak; Ahlawat, Anil K.; Sagar, Kalpna

    2017-06-01

    Evaluation of software quality is an important aspect for controlling and managing the software. By such evaluation, improvements in software process can be made. The software quality is significantly dependent on software usability. Many researchers have proposed numbers of usability models. Each model considers a set of usability factors but do not cover all the usability aspects. Practical implementation of these models is still missing, as there is a lack of precise definition of usability. Also, it is very difficult to integrate these models into current software engineering practices. In order to overcome these challenges, this paper aims to define the term `usability' using the proposed hierarchical usability model with its detailed taxonomy. The taxonomy considers generic evaluation criteria for identifying the quality components, which brings together factors, attributes and characteristics defined in various HCI and software models. For the first time, the usability model is also implemented to predict more accurate usability values. The proposed system is named as fuzzy hierarchical usability model that can be easily integrated into the current software engineering practices. In order to validate the work, a dataset of six software development life cycle models is created and employed. These models are ranked according to their predicted usability values. This research also focuses on the detailed comparison of proposed model with the existing usability models.

  17. Analyses of the redistribution of work following cardiac resynchronisation therapy in a patient specific model.

    Directory of Open Access Journals (Sweden)

    Steven Alexander Niederer

    Full Text Available Regulation of regional work is essential for efficient cardiac function. In patients with heart failure and electrical dysfunction such as left branch bundle block regional work is often depressed in the septum. Following cardiac resynchronisation therapy (CRT this heterogeneous distribution of work can be rebalanced by altering the pattern of electrical activation. To investigate the changes in regional work in these patients and the mechanisms underpinning the improved function following CRT we have developed a personalised computational model. Simulations of electromechanical cardiac function in the model estimate the regional stress, strain and work pre- and post-CRT. These simulations predict that the increase in observed work performed by the septum following CRT is not due to an increase in the volume of myocardial tissue recruited during contraction but rather that the volume of recruited myocardium remains the same and the average peak work rate per unit volume increases. These increases in the peak average rate of work is is attributed to slower and more effective contraction in the septum, as opposed to a change in active tension. Model results predict that this improved septal work rate following CRT is a result of resistance to septal contraction provided by the LV free wall. This resistance results in septal shortening over a longer period which, in turn, allows the septum to contract while generating higher levels of active tension to produce a higher work rate.

  18. The Predictive Value of Subjective Labour Supply Data: A Dynamic Panel Data Model with Measurement Error

    OpenAIRE

    Euwals, Rob

    2002-01-01

    This paper tests the predictive value of subjective labour supply data for adjustments in working hours over time. The idea is that if subjective labour supply data help to predict next year?s working hours, such data must contain at least some information on individual labour supply preferences. This informational content can be crucial to identify models of labour supply. Furthermore, it can be crucial to investigate the need for, or, alternatively, the support for laws and collective agree...

  19. Prediction for Major Adverse Outcomes in Cardiac Surgery: Comparison of Three Prediction Models

    Directory of Open Access Journals (Sweden)

    Cheng-Hung Hsieh

    2007-09-01

    Conclusion: The Parsonnet score performed as well as the logistic regression models in predicting major adverse outcomes. The Parsonnet score appears to be a very suitable model for clinicians to use in risk stratification of cardiac surgery.

  20. On hydrological model complexity, its geometrical interpretations and prediction uncertainty

    NARCIS (Netherlands)

    Arkesteijn, E.C.M.M.; Pande, S.

    2013-01-01

    Knowledge of hydrological model complexity can aid selection of an optimal prediction model out of a set of available models. Optimal model selection is formalized as selection of the least complex model out of a subset of models that have lower empirical risk. This may be considered equivalent to

  1. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

  2. Predictive modeling of dental pain using neural network.

    Science.gov (United States)

    Kim, Eun Yeob; Lim, Kun Ok; Rhee, Hyun Sill

    2009-01-01

    The mouth is a part of the body for ingesting food that is the most basic foundation and important part. The dental pain predicted by the neural network model. As a result of making a predictive modeling, the fitness of the predictive modeling of dental pain factors was 80.0%. As for the people who are likely to experience dental pain predicted by the neural network model, preventive measures including proper eating habits, education on oral hygiene, and stress release must precede any dental treatment.

  3. Predicting non-return to work in patients attending cardiac rehabilitation

    DEFF Research Database (Denmark)

    Samkange-Zeeb, Florence; Altenhöner, Thomas; Berg, Gabriele

    2006-01-01

    Return to work (RTW) is the primary goal in the rehabilitation of patients suffering from coronary heart diseases. However, in spite of expensive rehabilitative efforts, many patients do not resume work following cardiac rehabilitation. To increase cost-effectiveness, predictive tests for non...... programme which can be integrated into existing rehabilitation programmes, we developed a screening instrument for the identification of persons at risk of not returning to work at the onset of the rehabilitation process. More than 65% of the participants who had not returned to work 6 and 12 months...... following rehabilitation had been correctly identified as risk patients at the beginning of the rehabilitation process. Seventy-five percent had been correctly identified as not being at risk. Multiple regression analysis results showed that increased age, profession, positive expectations concerning RTW...

  4. Gaussian Process Regression for Predictive But Interpretable Machine Learning Models: An Example of Predicting Mental Workload across Tasks.

    Science.gov (United States)

    Caywood, Matthew S; Roberts, Daniel M; Colombe, Jeffrey B; Greenwald, Hal S; Weiland, Monica Z

    2016-01-01

    There is increasing interest in real-time brain-computer interfaces (BCIs) for the passive monitoring of human cognitive state, including cognitive workload. Too often, however, effective BCIs based on machine learning techniques may function as "black boxes" that are difficult to analyze or interpret. In an effort toward more interpretable BCIs, we studied a family of N-back working memory tasks using a machine learning model, Gaussian Process Regression (GPR), which was both powerful and amenable to analysis. Participants performed the N-back task with three stimulus variants, auditory-verbal, visual-spatial, and visual-numeric, each at three working memory loads. GPR models were trained and tested on EEG data from all three task variants combined, in an effort to identify a model that could be predictive of mental workload demand regardless of stimulus modality. To provide a comparison for GPR performance, a model was additionally trained using multiple linear regression (MLR). The GPR model was effective when trained on individual participant EEG data, resulting in an average standardized mean squared error (sMSE) between true and predicted N-back levels of 0.44. In comparison, the MLR model using the same data resulted in an average sMSE of 0.55. We additionally demonstrate how GPR can be used to identify which EEG features are relevant for prediction of cognitive workload in an individual participant. A fraction of EEG features accounted for the majority of the model's predictive power; using only the top 25% of features performed nearly as well as using 100% of features. Subsets of features identified by linear models (ANOVA) were not as efficient as subsets identified by GPR. This raises the possibility of BCIs that require fewer model features while capturing all of the information needed to achieve high predictive accuracy.

  5. Adaptation to shift work: physiologically based modeling of the effects of lighting and shifts' start time.

    Science.gov (United States)

    Postnova, Svetlana; Robinson, Peter A; Postnov, Dmitry D

    2013-01-01

    Shift work has become an integral part of our life with almost 20% of the population being involved in different shift schedules in developed countries. However, the atypical work times, especially the night shifts, are associated with reduced quality and quantity of sleep that leads to increase of sleepiness often culminating in accidents. It has been demonstrated that shift workers' sleepiness can be improved by a proper scheduling of light exposure and optimizing shifts timing. Here, an integrated physiologically-based model of sleep-wake cycles is used to predict adaptation to shift work in different light conditions and for different shift start times for a schedule of four consecutive days of work. The integrated model combines a model of the ascending arousal system in the brain that controls the sleep-wake switch and a human circadian pacemaker model. To validate the application of the integrated model and demonstrate its utility, its dynamics are adjusted to achieve a fit to published experimental results showing adaptation of night shift workers (n = 8) in conditions of either bright or regular lighting. Further, the model is used to predict the shift workers' adaptation to the same shift schedule, but for conditions not considered in the experiment. The model demonstrates that the intensity of shift light can be reduced fourfold from that used in the experiment and still produce good adaptation to night work. The model predicts that sleepiness of the workers during night shifts on a protocol with either bright or regular lighting can be significantly improved by starting the shift earlier in the night, e.g.; at 21:00 instead of 00:00. Finally, the study predicts that people of the same chronotype, i.e. with identical sleep times in normal conditions, can have drastically different responses to shift work depending on their intrinsic circadian and homeostatic parameters.

  6. Adaptation to shift work: physiologically based modeling of the effects of lighting and shifts' start time.

    Directory of Open Access Journals (Sweden)

    Svetlana Postnova

    Full Text Available Shift work has become an integral part of our life with almost 20% of the population being involved in different shift schedules in developed countries. However, the atypical work times, especially the night shifts, are associated with reduced quality and quantity of sleep that leads to increase of sleepiness often culminating in accidents. It has been demonstrated that shift workers' sleepiness can be improved by a proper scheduling of light exposure and optimizing shifts timing. Here, an integrated physiologically-based model of sleep-wake cycles is used to predict adaptation to shift work in different light conditions and for different shift start times for a schedule of four consecutive days of work. The integrated model combines a model of the ascending arousal system in the brain that controls the sleep-wake switch and a human circadian pacemaker model. To validate the application of the integrated model and demonstrate its utility, its dynamics are adjusted to achieve a fit to published experimental results showing adaptation of night shift workers (n = 8 in conditions of either bright or regular lighting. Further, the model is used to predict the shift workers' adaptation to the same shift schedule, but for conditions not considered in the experiment. The model demonstrates that the intensity of shift light can be reduced fourfold from that used in the experiment and still produce good adaptation to night work. The model predicts that sleepiness of the workers during night shifts on a protocol with either bright or regular lighting can be significantly improved by starting the shift earlier in the night, e.g.; at 21:00 instead of 00:00. Finally, the study predicts that people of the same chronotype, i.e. with identical sleep times in normal conditions, can have drastically different responses to shift work depending on their intrinsic circadian and homeostatic parameters.

  7. Prediction of peptide bonding affinity: kernel methods for nonlinear modeling

    CERN Document Server

    Bergeron, Charles; Sundling, C Matthew; Krein, Michael; Katt, Bill; Sukumar, Nagamani; Breneman, Curt M; Bennett, Kristin P

    2011-01-01

    This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear partial least squares (PLS) algorithm, outperforms PLS, and that the incorporation of transferable atom equivalent features improves predictive capability.

  8. A new Cumulative Damage Model for Fatigue Life Prediction under Shot Peening Treatment

    Directory of Open Access Journals (Sweden)

    Abdul-Jabar H. Ali

    2015-07-01

    Full Text Available In this paper, fatigue damage accumulation were studied using many methods i.e.Corton-Dalon (CD,Corton-Dalon-Marsh(CDM, new non-linear model and experimental method. The prediction of fatigue lifetimes based on the two classical methods, Corton-Dalon (CDandCorton-Dalon-Marsh (CDM, are uneconomic and non-conservative respectively. However satisfactory predictions were obtained by applying the proposed non-linear model (present model for medium carbon steel compared with experimental work. Many shortcomings of the two classical methods are related to their inability to take into account the surface treatment effect as shot peening. It is clear that the new model shows that a much better and conservative prediction of fatigue life in comparison with CD and CDM methods. The prediction of the present model gave slightly below the experimental data while the CDM gave overestimate prediction and CD showed strongly underestimates the life of specimens.

  9. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  10. Motivation to Improve Work through Learning: A Conceptual Model

    Directory of Open Access Journals (Sweden)

    Kueh Hua Ng

    2014-12-01

    Full Text Available This study aims to enhance our current understanding of the transfer of training by proposing a conceptual model that supports the mediating role of motivation to improve work through learning about the relationship between social support and the transfer of training. The examination of motivation to improve work through motivation to improve work through a learning construct offers a holistic view pertaining to a learner's profile in a workplace setting, which emphasizes learning for the improvement of work performance. The proposed conceptual model is expected to benefit human resource development theory building, as well as field practitioners by emphasizing the motivational aspects crucial for successful transfer of training.

  11. Person-Environment Congruence and Personality Domains in the Prediction of Job Performance and Work Quality

    Science.gov (United States)

    Kieffer, Kevin M.; Schinka, John A.; Curtiss, Glenn

    2004-01-01

    This study examined the contributions of the 5-Factor Model (FFM; P. T. Costa & R. R. McCrae, 1992) and RIASEC (J. L. Holland, 1994) constructs of consistency, differentiation, and person-environment congruence in predicting job performance ratings in a large sample (N = 514) of employees. Hierarchical regression analyses conducted separately by…

  12. Gaussian Process Regression for Predictive But Interpretable Machine Learning Models: An Example of Predicting Mental Workload across Tasks

    Science.gov (United States)

    Caywood, Matthew S.; Roberts, Daniel M.; Colombe, Jeffrey B.; Greenwald, Hal S.; Weiland, Monica Z.

    2017-01-01

    There is increasing interest in real-time brain-computer interfaces (BCIs) for the passive monitoring of human cognitive state, including cognitive workload. Too often, however, effective BCIs based on machine learning techniques may function as “black boxes” that are difficult to analyze or interpret. In an effort toward more interpretable BCIs, we studied a family of N-back working memory tasks using a machine learning model, Gaussian Process Regression (GPR), which was both powerful and amenable to analysis. Participants performed the N-back task with three stimulus variants, auditory-verbal, visual-spatial, and visual-numeric, each at three working memory loads. GPR models were trained and tested on EEG data from all three task variants combined, in an effort to identify a model that could be predictive of mental workload demand regardless of stimulus modality. To provide a comparison for GPR performance, a model was additionally trained using multiple linear regression (MLR). The GPR model was effective when trained on individual participant EEG data, resulting in an average standardized mean squared error (sMSE) between true and predicted N-back levels of 0.44. In comparison, the MLR model using the same data resulted in an average sMSE of 0.55. We additionally demonstrate how GPR can be used to identify which EEG features are relevant for prediction of cognitive workload in an individual participant. A fraction of EEG features accounted for the majority of the model’s predictive power; using only the top 25% of features performed nearly as well as using 100% of features. Subsets of features identified by linear models (ANOVA) were not as efficient as subsets identified by GPR. This raises the possibility of BCIs that require fewer model features while capturing all of the information needed to achieve high predictive accuracy. PMID:28123359

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

    Science.gov (United States)

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

    2016-08-01

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

  14. Simplified Predictive Models for CO2 Sequestration Performance Assessment

    Science.gov (United States)

    Mishra, Srikanta; RaviGanesh, Priya; Schuetter, Jared; Mooney, Douglas; He, Jincong; Durlofsky, Louis

    2014-05-01

    simulations, the LHS-based meta-model yields a more robust predictive model, as verified by a k-fold cross-validation approach. In the third category (RMM), we use a reduced-order modeling procedure that combines proper orthogonal decomposition (POD) for reducing problem dimensionality with trajectory-piecewise linearization (TPWL) for extrapolating system response at new control points from a limited number of trial runs ("snapshots"). We observe significant savings in computational time with very good accuracy from the POD-TPWL reduced order model - which could be important in the context of history matching, uncertainty quantification and optimization problems. The paper will present results from our ongoing investigations, and also discuss future research directions and likely outcomes. This work was supported by U.S. Department of Energy National Energy Technology Laboratory award DE-FE0009051 and Ohio Department of Development grant D-13-02.

  15. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.;

    2016-01-01

    The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...... problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...

  16. The Urgent Need for Improved Climate Models and Predictions

    Science.gov (United States)

    Goddard, Lisa; Baethgen, Walter; Kirtman, Ben; Meehl, Gerald

    2009-09-01

    An investment over the next 10 years of the order of US$2 billion for developing improved climate models was recommended in a report (http://wcrp.wmo.int/documents/WCRP_WorldModellingSummit_Jan2009.pdf) from the May 2008 World Modelling Summit for Climate Prediction, held in Reading, United Kingdom, and presented by the World Climate Research Programme. The report indicated that “climate models will, as in the past, play an important, and perhaps central, role in guiding the trillion dollar decisions that the peoples, governments and industries of the world will be making to cope with the consequences of changing climate.” If trillions of dollars are going to be invested in making decisions related to climate impacts, an investment of $2 billion, which is less than 0.1% of that amount, to provide better climate information seems prudent. One example of investment in adaptation is the World Bank's Climate Investment Fund, which has drawn contributions of more than $6 billion for work on clean technologies and adaptation efforts in nine pilot countries and two pilot regions. This is just the beginning of expenditures on adaptation efforts by the World Bank and other mechanisms, focusing on only a small fraction of the nations of the world and primarily aimed at anticipated anthropogenic climate change. Moreover, decisions are being made now, all around the world—by individuals, companies, and governments—that affect people and their livelihoods today, not just 50 or more years in the future. Climate risk management, whether related to projects of the scope of the World Bank's or to the planning and decisions of municipalities, will be best guided by meaningful climate information derived from observations of the past and model predictions of the future.

  17. Seagrass Health Modeling and Prediction with NASA Science Data

    Science.gov (United States)

    Robinson, Harold D.; Easson, Greg; Slattery, Marc; Anderson, Daniel; Blonski, Slawomir; DeCurtins, Robert; Underwood, Lauren

    2010-01-01

    Previous research has demonstrated that MODIS data products can be used as inputs into the seagrass productivity model developed by Fong and Harwell (1994). To further explore this use to predict seagrass productivity, Moderate Resolution Imaging Spectroradiometer (MODIS) custom data products, including Sea Surface Temperature, Light Attenuation, and Chlorophyll-a have been created for use as model parameter inputs. Coastal researchers can use these MODIS data products and model results in conjunction with historical and daily assessment of seagrass conditions to assess variables that affect the productivity of the seagrass beds. Current monitoring practices involve manual data collection (typically on a quarterly basis) and the data is often insufficient for evaluating the dynamic events that influence seagrass beds. As part of a NASA-funded research grant, the University of Mississippi, is working with researchers at NASA and Radiance Technologies to develop methods to deliver MODIS derived model output for the northern Gulf of Mexico (GOM) to coastal and environmental managers. The result of the project will be a data portal that provides access to MODIS data products and model results from the past 5 years, that includes an automated process to incorporate new data as it becomes available. All model parameters and final output will be available through the use National Oceanic and Atmospheric Administration?s (NOAA) Environmental Research Divisions Data Access Program (ERDDAP) tools as well as viewable using Thematic Realtime Environmental Distributed Data Services (THREDDS) and the Integrated Data Viewer (IDV). These tools provide the ability to create raster-based time sequences of model output and parameters as well as create graphs of model parameters versus time. This tool will provide researchers and coastal managers the ability to analyze the model inputs so that the factors influencing a change in seagrass productivity can be determined over time.

  18. Predictive modeling and reducing cyclic variability in autoignition engines

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-08-30

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

  19. Predictive Model of Graphene Based Polymer Nanocomposites: Electrical Performance

    Science.gov (United States)

    Manta, Asimina; Gresil, Matthieu; Soutis, Constantinos

    2017-04-01

    In this computational work, a new simulation tool on the graphene/polymer nanocomposites electrical response is developed based on the finite element method (FEM). This approach is built on the multi-scale multi-physics format, consisting of a unit cell and a representative volume element (RVE). The FE methodology is proven to be a reliable and flexible tool on the simulation of the electrical response without inducing the complexity of raw programming codes, while it is able to model any geometry, thus the response of any component. This characteristic is supported by its ability in preliminary stage to predict accurately the percolation threshold of experimental material structures and its sensitivity on the effect of different manufacturing methodologies. Especially, the percolation threshold of two material structures of the same constituents (PVDF/Graphene) prepared with different methods was predicted highlighting the effect of the material preparation on the filler distribution, percolation probability and percolation threshold. The assumption of the random filler distribution was proven to be efficient on modelling material structures obtained by solution methods, while the through-the -thickness normal particle distribution was more appropriate for nanocomposites constructed by film hot-pressing. Moreover, the parametrical analysis examine the effect of each parameter on the variables of the percolation law. These graphs could be used as a preliminary design tool for more effective material system manufacturing.

  20. Predicting chick body mass by artificial intelligence-based models

    Directory of Open Access Journals (Sweden)

    Patricia Ferreira Ponciano Ferraz

    2014-07-01

    Full Text Available The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks - with the variables dry-bulb air temperature, duration of thermal stress (days, chick age (days, and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs and neuro-fuzzy networks (NFNs. The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

  1. Intelligent predictive model of ventilating capacity of imperial smelt furnace

    Institute of Scientific and Technical Information of China (English)

    唐朝晖; 胡燕瑜; 桂卫华; 吴敏

    2003-01-01

    In order to know the ventilating capacity of imperial smelt furnace (ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.

  2. A Prediction Model of the Capillary Pressure J-Function

    Science.gov (United States)

    Xu, W. S.; Luo, P. Y.; Sun, L.; Lin, N.

    2016-01-01

    The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative. PMID:27603701

  3. Adaptation of Predictive Models to PDA Hand-Held Devices

    Directory of Open Access Journals (Sweden)

    Lin, Edward J

    2008-01-01

    Full Text Available Prediction models using multiple logistic regression are appearing with increasing frequency in the medical literature. Problems associated with these models include the complexity of computations when applied in their pure form, and lack of availability at the bedside. Personal digital assistant (PDA hand-held devices equipped with spreadsheet software offer the clinician a readily available and easily applied means of applying predictive models at the bedside. The purposes of this article are to briefly review regression as a means of creating predictive models and to describe a method of choosing and adapting logistic regression models to emergency department (ED clinical practice.

  4. A model to predict the power output from wind farms

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Riso National Lab., Roskilde (Denmark)

    1997-12-31

    This paper will describe a model that can predict the power output from wind farms. To give examples of input the model is applied to a wind farm in Texas. The predictions are generated from forecasts from the NGM model of NCEP. These predictions are made valid at individual sites (wind farms) by applying a matrix calculated by the sub-models of WASP (Wind Atlas Application and Analysis Program). The actual wind farm production is calculated using the Riso PARK model. Because of the preliminary nature of the results, they will not be given. However, similar results from Europe will be given.

  5. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.

    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of

  6. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of new technologies

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

    Science.gov (United States)

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

    2016-01-01

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

  8. Prediction of strain localization in sheet metal forming using elastoplastic-damage model and localization criterion

    OpenAIRE

    Haddag, Badis; ABED-MERAIM, Farid; BALAN, Tudor

    2007-01-01

    The aim of this work is to study the strain localization during the plastic deformation of sheets metals. This phenomenon is precursor for the fracture of drawing parts, thus its prediction using advanced behavior models is important in order to obtain safe final parts. Most often, an accurate prediction of localization during forming process requires damage to be included in the simulation. For this purpose, an advanced, anisotropic elastoplastic model, combining isotropic and kinematic hard...

  9. Cross–Project Defect Prediction With Respect To Code Ownership Model: An Empirical Study

    Directory of Open Access Journals (Sweden)

    Marian Jureczko

    2015-06-01

    Full Text Available The paper presents an analysis of 83 versions of industrial, open-source and academic projects. We have empirically evaluated whether those project types constitute separate classes of projects with regard to defect prediction. Statistical tests proved that there exist significant differences between the models trained on the aforementioned project classes. This work makes the next step towards cross-project reusability of defect prediction models and facilitates their adoption, which has been very limited so far.

  10. Application of simulated annealing algorithm to improve work roll wear model in plate mills

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Employing Simulated Annealing Algorithm (SAA) and many measured data, a calculation model of work roll wear was built in the 2 800 mm 4-high mill of Wuhan Iron and Steel (Group) Co.(WISCO). The model was a semi-theory practical formula. Its pattern and magnitude were still hardly defined with classical optimization methods. But the problem could be resolved by SAA. It was pretty high precision to predict the values for the wear profiles of work roll in a rolling unit. Afterone-year application, the results show that the model is feasible in engineering, and it can be applied to predict the wear profiles of work roll in other mills

  11. Modeling and prediction of children’s growth data via functional principal component analysis

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    We use the functional principal component analysis(FPCA) to model and predict the weight growth in children.In particular,we examine how the approach can help discern growth patterns of underweight children relative to their normal counterparts,and whether a commonly used transformation to normality plays any constructive roles in a predictive model based on the FPCA.Our work supplements the conditional growth charts developed by Wei and He(2006) by constructing a predictive growth model based on a small number of principal components scores on individual’s past.

  12. Predicting Career Advancement with Structural Equation Modelling

    Science.gov (United States)

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  13. Predicting Career Advancement with Structural Equation Modelling

    Science.gov (United States)

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  14. Modeling and prediction of surgical procedure times

    NARCIS (Netherlands)

    P.S. Stepaniak (Pieter); C. Heij (Christiaan); G. de Vries (Guus)

    2009-01-01

    textabstractAccurate prediction of medical operation times is of crucial importance for cost efficient operation room planning in hospitals. This paper investigates the possible dependence of procedure times on surgeon factors like age, experience, gender, and team composition. The effect of these f

  15. Prediction Model of Sewing Technical Condition by Grey Neural Network

    Institute of Scientific and Technical Information of China (English)

    DONG Ying; FANG Fang; ZHANG Wei-yuan

    2007-01-01

    The grey system theory and the artificial neural network technology were applied to predict the sewing technical condition. The representative parameters, such as needle, stitch, were selected. Prediction model was established based on the different fabrics' mechanical properties that measured by KES instrument. Grey relevant degree analysis was applied to choose the input parameters of the neural network. The result showed that prediction model has good precision. The average relative error was 4.08% for needle and 4.25% for stitch.

  16. Active diagnosis of hybrid systems - A model predictive approach

    OpenAIRE

    2009-01-01

    A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and faulty outputs constrained by tolerable performance requirements. As in standard model predictive control, the first element of the optimal input is applied to the system and the whole procedure is repeate...

  17. Evaluation of Fast-Time Wake Vortex Prediction Models

    Science.gov (United States)

    Proctor, Fred H.; Hamilton, David W.

    2009-01-01

    Current fast-time wake models are reviewed and three basic types are defined. Predictions from several of the fast-time models are compared. Previous statistical evaluations of the APA-Sarpkaya and D2P fast-time models are discussed. Root Mean Square errors between fast-time model predictions and Lidar wake measurements are examined for a 24 hr period at Denver International Airport. Shortcomings in current methodology for evaluating wake errors are also discussed.

  18. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “any fall” and “recurrent falls.” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  19. Testing and analysis of internal hardwood log defect prediction models

    Science.gov (United States)

    R. Edward. Thomas

    2011-01-01

    The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...

  20. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  1. Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode

  2. Refining the committee approach and uncertainty prediction in hydrological modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode

  3. Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode

  4. Refining the committee approach and uncertainty prediction in hydrological modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode

  5. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  6. Model predictive control approach for a CPAP-device

    Directory of Open Access Journals (Sweden)

    Scheel Mathias

    2017-09-01

    Full Text Available The obstructive sleep apnoea syndrome (OSAS is characterized by a collapse of the upper respiratory tract, resulting in a reduction of the blood oxygen- and an increase of the carbon dioxide (CO2 - concentration, which causes repeated sleep disruptions. The gold standard to treat the OSAS is the continuous positive airway pressure (CPAP therapy. The continuous pressure keeps the upper airway open and prevents the collapse of the upper respiratory tract and the pharynx. Most of the available CPAP-devices cannot maintain the pressure reference [1]. In this work a model predictive control approach is provided. This control approach has the possibility to include the patient’s breathing effort into the calculation of the control variable. Therefore a patient-individualized control strategy can be developed.

  7. Motivation to Improve Work through Learning: A Conceptual Model

    OpenAIRE

    Kueh Hua Ng; Rusli Ahmad

    2014-01-01

    This study aims to enhance our current understanding of the transfer of training by proposing a conceptual model that supports the mediating role of motivation to improve work through learning about the relationship between social support and the transfer of training. The examination of motivation to improve work through motivation to improve work through a learning construct offers a holistic view pertaining to a learner's profile in a workplace setting, which emphasizes learning for the imp...

  8. Prediction Uncertainty Analyses for the Combined Physically-Based and Data-Driven Models

    Science.gov (United States)

    Demissie, Y. K.; Valocchi, A. J.; Minsker, B. S.; Bailey, B. A.

    2007-12-01

    The unavoidable simplification associated with physically-based mathematical models can result in biased parameter estimates and correlated model calibration errors, which in return affect the accuracy of model predictions and the corresponding uncertainty analyses. In this work, a physically-based groundwater model (MODFLOW) together with error-correcting artificial neural networks (ANN) are used in a complementary fashion to obtain an improved prediction (i.e. prediction with reduced bias and error correlation). The associated prediction uncertainty of the coupled MODFLOW-ANN model is then assessed using three alternative methods. The first method estimates the combined model confidence and prediction intervals using first-order least- squares regression approximation theory. The second method uses Monte Carlo and bootstrap techniques for MODFLOW and ANN, respectively, to construct the combined model confidence and prediction intervals. The third method relies on a Bayesian approach that uses analytical or Monte Carlo methods to derive the intervals. The performance of these approaches is compared with Generalized Likelihood Uncertainty Estimation (GLUE) and Calibration-Constrained Monte Carlo (CCMC) intervals of the MODFLOW predictions alone. The results are demonstrated for a hypothetical case study developed based on a phytoremediation site at the Argonne National Laboratory. This case study comprises structural, parameter, and measurement uncertainties. The preliminary results indicate that the proposed three approaches yield comparable confidence and prediction intervals, thus making the computationally efficient first-order least-squares regression approach attractive for estimating the coupled model uncertainty. These results will be compared with GLUE and CCMC results.

  9. Impact of modellers' decisions on hydrological a priori predictions

    Science.gov (United States)

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

    2014-06-01

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

  10. Econometric models for predicting confusion crop ratios

    Science.gov (United States)

    Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)

    1979-01-01

    Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

  11. Towards a moderated mediation model of innovative work behaviour enhancement

    NARCIS (Netherlands)

    Stoffers, J.M.M.; Heijden, B.I.J.M. van der; Notelaers, G.L.A.

    2014-01-01

    Purpose – The purpose of this paper is to investigate a moderated mediation model of innovative work behaviour enhancement. Perceived firm (organizational and market) performance was assumed to moderate the relationships between leader-member exchange (LMX) and organizational

  12. Distributed Leadership as Work Redesign: Retrofitting the Job Characteristics Model

    Science.gov (United States)

    Mayrowetz, David; Murphy, Joseph; Louis, Karen Seashore; Smylie, Mark A.

    2007-01-01

    In this article, we revive work redesign theory, specifically Hackman and Oldham's Job Characteristics Model (JCM), to examine distributed leadership initiatives. Based on our early observations of six schools engaged in distributed leadership reform and a broad review of literature, including empirical tests of work redesign theory, we retrofit…

  13. Collaborative Online Teaching: A Model for Gerontological Social Work Education

    Science.gov (United States)

    Fulton, Amy E.; Walsh, Christine A.; Azulai, Anna; Gulbrandsen, Cari; Tong, Hongmei

    2015-01-01

    Social work students and faculty are increasingly embracing online education and collaborative teaching. Yet models to support these activities have not been adequately developed. This paper describes how a team of instructors developed, delivered, and evaluated an undergraduate gerontological social work course using a collaborative online…

  14. A Conceptual Model of the World of Work.

    Science.gov (United States)

    VanRooy, William H.

    The conceptual model described in this paper resulted from the need to organize a body of knowledge related to the world of work which would enable curriculum developers to prepare accurate, realistic instructional materials. The world of work is described by applying Malinowski's scientific study of the structural components of culture. It is…

  15. Beyond standard model report of working group II

    CERN Document Server

    Joshipura, A S; Joshipura, Anjan S; Roy, Probir

    1995-01-01

    Working group II at WHEPP3 concentrated on issues related to the supersymmetric standard model as well as SUSY GUTS and neutrino properties. The projects identified by various working groups as well as progress made in them since WHEPP3 are briefly reviewed.

  16. PEEX Modelling Platform for Seamless Environmental Prediction

    Science.gov (United States)

    Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku

    2017-04-01

    The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.

  17. Modeling visual working memory with the MemToolbox.

    Science.gov (United States)

    Suchow, Jordan W; Brady, Timothy F; Fougnie, Daryl; Alvarez, George A

    2013-08-20

    The MemToolbox is a collection of MATLAB functions for modeling visual working memory. In support of its goal to provide a full suite of data analysis tools, the toolbox includes implementations of popular models of visual working memory, real and simulated data sets, Bayesian and maximum likelihood estimation procedures for fitting models to data, visualizations of data and fit, validation routines, model comparison metrics, and experiment scripts. The MemToolbox is released under the permissive BSD license and is available at http://memtoolbox.org.

  18. Values and uncertainties in the predictions of global climate models.

    Science.gov (United States)

    Winsberg, Eric

    2012-06-01

    Over the last several years, there has been an explosion of interest and attention devoted to the problem of Uncertainty Quantification (UQ) in climate science-that is, to giving quantitative estimates of the degree of uncertainty associated with the predictions of global and regional climate models. The technical challenges associated with this project are formidable, and so the statistical community has understandably devoted itself primarily to overcoming them. But even as these technical challenges are being met, a number of persistent conceptual difficulties remain. So why is UQ so important in climate science? UQ, I would like to argue, is first and foremost a tool for communicating knowledge from experts to policy makers in a way that is meant to be free from the influence of social and ethical values. But the standard ways of using probabilities to separate ethical and social values from scientific practice cannot be applied in a great deal of climate modeling, because the roles of values in creating the models cannot be discerned after the fact-the models are too complex and the result of too much distributed epistemic labor. I argue, therefore, that typical approaches for handling ethical/social values in science do not work well here.

  19. Effect of empowerment on professional practice environments, work satisfaction, and patient care quality: further testing the Nursing Worklife Model.

    Science.gov (United States)

    Spence Laschinger, Heather K

    2008-01-01

    The purpose of this study was to test Leiter and Laschinger's Nursing Worklife Model linking structural empowerment to Lake's 5-factor professional practice work environment model and work quality outcomes. A predictive, nonexperimental design was used to test the model in a random sample of 234 staff nurses. The analysis revealed that professional practice environment characteristics mediated the relationship between structurally empowering work conditions and both job satisfaction and nurse-assessed patient care quality.

  20. Application of product modelling - seen from a work preparation viewpoint

    DEFF Research Database (Denmark)

    Hvam, Lars

    Manufacturing companies spends an increasing amount of the total work resources in the manufacturing planning system with the activities of e.g. specifying products and methods, scheduling, procurement etc. By this the potential for obtaining increased productivity moves from the direct costs...... the specification work. The theoretical fundament of the project include four elements. The first element (work preparation) consider methods for analysing and preparing the direct work in the production, pointing to an analogy between analysing the direct work in the production and the work in the planning systems......, over building a model, and to the final programming of an application. It has been stressed out to carry out all the phases in the outline of procedure in the empirical work, one of the reasons being to prove that it is possible, with a reasonable consumption of resources, to build an application...

  1. Models Predicting Success of Infertility Treatment: A Systematic Review

    Science.gov (United States)

    Zarinara, Alireza; Zeraati, Hojjat; Kamali, Koorosh; Mohammad, Kazem; Shahnazari, Parisa; Akhondi, Mohammad Mehdi

    2016-01-01

    Background: Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn’t simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models. Methods: This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated. Results: Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems. Conclusion: Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples’ needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable. PMID:27141461

  2. Predictive analytics technology review: Similarity-based modeling and beyond

    Energy Technology Data Exchange (ETDEWEB)

    Herzog, James; Doan, Don; Gandhi, Devang; Nieman, Bill

    2010-09-15

    Over 11 years ago, SmartSignal introduced Predictive Analytics for eliminating equipment failures, using its patented SBM technology. SmartSignal continues to lead and dominate the market and, in 2010, went one step further and introduced Predictive Diagnostics. Now, SmartSignal is combining Predictive Diagnostics with RCM methodology and industry expertise. FMEA logic reengineers maintenance work management, eliminates unneeded inspections, and focuses efforts on the real issues. This integrated solution significantly lowers maintenance costs, protects against critical asset failures, and improves commercial availability, and reduces work orders 20-40%. Learn how.

  3. Knock prediction for dual fuel engines by using a simplified combustion model

    Institute of Scientific and Technical Information of China (English)

    费少梅; 刘震涛; 严兆大

    2003-01-01

    The present work used a methane-air mixture chemical kinetics scheme consisting of 119 elementary reaction steps and 41 chemical species to develop a simplified combustion model for prediction of the knock in dual fuel engines. Calculated values by the model for natural gas operation showed good agreement with corresponding experimental values over a broad range of operating conditions.

  4. Demonstration of leapfrogging for implementing nonlinear model predictive control on a heat exchanger.

    Science.gov (United States)

    Sridhar, Upasana Manimegalai; Govindarajan, Anand; Rhinehart, R Russell

    2016-01-01

    This work reveals the applicability of a relatively new optimization technique, Leapfrogging, for both nonlinear regression modeling and a methodology for nonlinear model-predictive control. Both are relatively simple, yet effective. The application on a nonlinear, pilot-scale, shell-and-tube heat exchanger reveals practicability of the techniques.

  5. The "CEO" of women's work lives: how Big Five Conscientiousness, Extraversion, and Openness predict 50 years of work experiences in a changing sociocultural context.

    Science.gov (United States)

    George, Linda G; Helson, Ravenna; John, Oliver P

    2011-10-01

    Few long-term longitudinal studies have examined how dimensions of personality are related to work lives, especially in women. We propose a life-course framework for studying work over time, from preparatory activities (in the 20s) to descending work involvement (after age 60), using 50 years of life data from the women in the Mills Longitudinal Study. We hypothesized differential work effects for Extraversion (work as pursuit of rewards), Openness (work as self-actualization), and Conscientiousness (work as duty) and measured these 3 traits as predictor variables when the women were still in college. In a prospective longitudinal design, we then studied how these traits predicted the women's subsequent work lives from young adulthood to age 70 and how these effects depended on the changing sociocultural context. Specifically, the young adulthood of the Mills women in the mid-1960s was rigidly gender typed and family oriented; neither work nor education variables at that time were predicted from earlier personality traits. However, as women's roles changed, later work variables became related to all 3 traits, as expected from current Big Five theory and research. For example, early personality traits predicted the timing of involvement in work, the kinds of jobs chosen, and the status and satisfaction achieved, as well as continued work participation and financial security in late adulthood. Early traits were also linked to specific cultural influences, such as the traditional feminine role, the women's movement, and graduate education for careers.

  6. Gaussian mixture models as flux prediction method for central receivers

    Science.gov (United States)

    Grobler, Annemarie; Gauché, Paul; Smit, Willie

    2016-05-01

    Flux prediction methods are crucial to the design and operation of central receiver systems. Current methods such as the circular and elliptical (bivariate) Gaussian prediction methods are often used in field layout design and aiming strategies. For experimental or small central receiver systems, the flux profile of a single heliostat often deviates significantly from the circular and elliptical Gaussian models. Therefore a novel method of flux prediction was developed by incorporating the fitting of Gaussian mixture models onto flux profiles produced by flux measurement or ray tracing. A method was also developed to predict the Gaussian mixture model parameters of a single heliostat for a given time using image processing. Recording the predicted parameters in a database ensures that more accurate predictions are made in a shorter time frame.

  7. Examining the Roles of Reasoning and Working Memory in Predicting Casual Game Performance across Extended Gameplay.

    Science.gov (United States)

    Kranz, Michael B; Baniqued, Pauline L; Voss, Michelle W; Lee, Hyunkyu; Kramer, Arthur F

    2017-01-01

    The variety and availability of casual video games presents an exciting opportunity for applications such as cognitive training. Casual games have been associated with fluid abilities such as working memory (WM) and reasoning, but the importance of these cognitive constructs in predicting performance may change across extended gameplay and vary with game structure. The current investigation examined the relationship between cognitive abilities and casual game performance over time by analyzing first and final session performance over 4-5 weeks of game play. We focused on two groups of subjects who played different types of casual games previously shown to relate to WM and reasoning when played for a single session: (1) puzzle-based games played adaptively across sessions and (2) speeded switching games played non-adaptively across sessions. Reasoning uniquely predicted first session casual game scores for both groups and accounted for much of the relationship with WM. Furthermore, over time, WM became uniquely important for predicting casual game performance for the puzzle-based adaptive games but not for the speeded switching non-adaptive games. These results extend the burgeoning literature on cognitive abilities involved in video games by showing differential relationships of fluid abilities across different game types and extended play. More broadly, the current study illustrates the usefulness of using multiple cognitive measures in predicting performance, and provides potential directions for game-based cognitive training research.

  8. Examining the Roles of Reasoning and Working Memory in Predicting Casual Game Performance across Extended Gameplay

    Science.gov (United States)

    Kranz, Michael B.; Baniqued, Pauline L.; Voss, Michelle W.; Lee, Hyunkyu; Kramer, Arthur F.

    2017-01-01

    The variety and availability of casual video games presents an exciting opportunity for applications such as cognitive training. Casual games have been associated with fluid abilities such as working memory (WM) and reasoning, but the importance of these cognitive constructs in predicting performance may change across extended gameplay and vary with game structure. The current investigation examined the relationship between cognitive abilities and casual game performance over time by analyzing first and final session performance over 4–5 weeks of game play. We focused on two groups of subjects who played different types of casual games previously shown to relate to WM and reasoning when played for a single session: (1) puzzle-based games played adaptively across sessions and (2) speeded switching games played non-adaptively across sessions. Reasoning uniquely predicted first session casual game scores for both groups and accounted for much of the relationship with WM. Furthermore, over time, WM became uniquely important for predicting casual game performance for the puzzle-based adaptive games but not for the speeded switching non-adaptive games. These results extend the burgeoning literature on cognitive abilities involved in video games by showing differential relationships of fluid abilities across different game types and extended play. More broadly, the current study illustrates the usefulness of using multiple cognitive measures in predicting performance, and provides potential directions for game-based cognitive training research. PMID:28326042

  9. Linear prediction of atmospheric wave-fronts for tomographic Adaptive Optics systems: modelling and robustness assessment

    CERN Document Server

    Jackson, Kate; Lardiere, Olivier; Andersen, Dave; Bradley, Colin

    2015-01-01

    We use a theoretical frame-work to analytically assess temporal prediction error functions on von-Karman turbulence when a zonal representation of wave-fronts is assumed. Linear prediction models analysed include auto-regressive of order up to three, bilinear interpolation functions and a minimum mean square error predictor. This is an extension of the authors' previously published work (see ref. 2) in which the efficacy of various temporal prediction models was established. Here we examine the tolerance of these algorithms to specific forms of model errors, thus defining the expected change in behaviour of the previous results under less ideal conditions. Results show that +/- 100pc wind-speed error and +/- 50 deg are tolerable before the best linear predictor delivers poorer performance than the no-prediction case.

  10. Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial.

    Science.gov (United States)

    Collis, Joe; Connor, Anthony J; Paczkowski, Marcin; Kannan, Pavitra; Pitt-Francis, Joe; Byrne, Helen M; Hubbard, Matthew E

    2017-03-13

    In this work, we present a pedagogical tumour growth example, in which we apply calibration and validation techniques to an uncertain, Gompertzian model of tumour spheroid growth. The key contribution of this article is the discussion and application of these methods (that are not commonly employed in the field of cancer modelling) in the context of a simple model, whose deterministic analogue is widely known within the community. In the course of the example, we calibrate the model against experimental data that are subject to measurement errors, and then validate the resulting uncertain model predictions. We then analyse the sensitivity of the model predictions to the underlying measurement model. Finally, we propose an elementary learning approach for tuning a threshold parameter in the validation procedure in order to maximize predictive accuracy of our validated model.

  11. Nonlinear model predictive control of a packed distillation column

    Energy Technology Data Exchange (ETDEWEB)

    Patwardhan, A.A.; Edgar, T.F. (Univ. of Texas, Austin, TX (United States). Dept. of Chemical Engineering)

    1993-10-01

    A rigorous dynamic model based on fundamental chemical engineering principles was formulated for a packed distillation column separating a mixture of cyclohexane and n-heptane. This model was simplified to a form suitable for use in on-line model predictive control calculations. A packed distillation column was operated at several operating conditions to estimate two unknown model parameters in the rigorous and simplified models. The actual column response to step changes in the feed rate, distillate rate, and reboiler duty agreed well with dynamic model predictions. One unusual characteristic observed was that the packed column exhibited gain-sign changes, which are very difficult to treat using conventional linear feedback control. Nonlinear model predictive control was used to control the distillation column at an operating condition where the process gain changed sign. An on-line, nonlinear model-based scheme was used to estimate unknown/time-varying model parameters.

  12. Error estimates for density-functional theory predictions of surface energy and work function

    Science.gov (United States)

    De Waele, Sam; Lejaeghere, Kurt; Sluydts, Michael; Cottenier, Stefaan

    2016-12-01

    Density-functional theory (DFT) predictions of materials properties are becoming ever more widespread. With increased use comes the demand for estimates of the accuracy of DFT results. In view of the importance of reliable surface properties, this work calculates surface energies and work functions for a large and diverse test set of crystalline solids. They are compared to experimental values by performing a linear regression, which results in a measure of the predictable and material-specific error of the theoretical result. Two of the most prevalent functionals, the local density approximation (LDA) and the Perdew-Burke-Ernzerhof parametrization of the generalized gradient approximation (PBE-GGA), are evaluated and compared. Both LDA and GGA-PBE are found to yield accurate work functions with error bars below 0.3 eV, rivaling the experimental precision. LDA also provides satisfactory estimates for the surface energy with error bars smaller than 10%, but GGA-PBE significantly underestimates the surface energy for materials with a large correlation energy.

  13. Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant

    Institute of Scientific and Technical Information of China (English)

    CHEN Yue-hua; CAO Guang-yi; ZHU Xin-jian

    2007-01-01

    This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.

  14. Adjusting to Job Demands: The Role of Work Self-Determination and Job Control in Predicting Burnout

    Science.gov (United States)

    Fernet, Claude; Guay, Frederic; Senecal, Caroline

    2004-01-01

    This study examined the dynamic interplay among job demands, job control, and work self-determination in order to predict burnout dimensions. A three-way interaction effect was found between job demands, job control and work self-determination in predicting each dimension of burnout (emotional exhaustion, depersonalization, and personal…

  15. Can Working Memory and Inhibitory Control Predict Second Language Learning in the Classroom?

    Directory of Open Access Journals (Sweden)

    Jared A. Linck

    2015-10-01

    Full Text Available The role of executive functioning in second language (L2 aptitude remains unclear. Whereas some studies report a relationship between working memory (WM and L2 learning, others have argued against this association. Similarly, being bilingual appears to benefit inhibitory control, and individual differences in inhibitory control are related to online L2 processing. The current longitudinal study examines whether these two components of executive functioning predict learning gains in an L2 classroom context using a pretest/posttest design. We assessed 25 university students in language courses, who completed measures of WM and inhibitory control. They also completed a proficiency measure at the beginning and end of a semester and reported their grade point average (GPA. WM was positively related to L2 proficiency and learning, but inhibitory control was not. These results support the notion that WM is an important component of L2 aptitude, particularly for predicting the early stages of L2 classroom learning.

  16. Modeling the antecedents of proactive behavior at work.

    Science.gov (United States)

    Parker, Sharon K; Williams, Helen M; Turner, Nick

    2006-05-01

    Using a sample of U.K. wire makers (N = 282), the authors tested a model in which personality and work environment antecedents affect proactive work behavior via cognitive-motivational mechanisms. Self-reported proactive work behaviors (proactive idea implementation and proactive problem solving) were validated against rater assessments for a subsample (n = 60) of wire makers. With the exception of supportive supervision, each antecedent was important, albeit through different processes. Proactive personality was significantly associated with proactive work behavior via role breadth self-efficacy and flexible role orientation, job autonomy was also linked to proactive behavior via these processes, as well as directly; and coworker trust was associated with proactive behavior via flexible role orientation. In further support of the model, the cognitive-motivational processes for proactive work behavior differed from those for the more passive outcome of generalized compliance.

  17. A burnout prediction model based around char morphology

    Energy Technology Data Exchange (ETDEWEB)

    T. Wu; E. Lester; M. Cloke [University of Nottingham, Nottingham (United Kingdom). Nottingham Energy and Fuel Centre

    2005-07-01

    Poor burnout in a coal-fired power plant has marked penalties in the form of reduced energy efficiency and elevated waste material that can not be utilized. The prediction of coal combustion behaviour in a furnace is of great significance in providing valuable information not only for process optimization but also for coal buyers in the international market. Coal combustion models have been developed that can make predictions about burnout behaviour and burnout potential. Most of these kinetic models require standard parameters such as volatile content, particle size and assumed char porosity in order to make a burnout prediction. This paper presents a new model called the Char Burnout Model (ChB) that also uses detailed information about char morphology in its prediction. The model can use data input from one of two sources. Both sources are derived from image analysis techniques. The first from individual analysis and characterization of real char types using an automated program. The second from predicted char types based on data collected during the automated image analysis of coal particles. Modelling results were compared with a different carbon burnout kinetic model and burnout data from re-firing the chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen across several residence times. An improved agreement between ChB model and DTF experimental data proved that the inclusion of char morphology in combustion models can improve model predictions. 27 refs., 4 figs., 4 tabs.

  18. Work.

    Science.gov (United States)

    Haines, Annette M.

    2003-01-01

    Draws upon Maria Montessori's writings to examine work as a universal human tendency throughout life. Discusses the work of adaptation of the infant, work of "psycho-muscular organism" for the preschooler, work of the imagination for the elementary child, community work of the adolescent, and work of the adult. Asserts that…

  19. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel;

    2006-01-01

    Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...... day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis). Results Our analyses show significant differences between predictions from different models......, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence-only approaches) and assumptions made by each...

  20. A new ensemble model for short term wind power prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan;

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re......-search of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset....... The conferred results show that the prediction errors can be decreased, while the computation time is reduced....

  1. Prediction and modeling signals from the monitoring of stand-alone photovoltaic systems using an adaptive neural network model

    Energy Technology Data Exchange (ETDEWEB)

    Mellit, A. [University Center of Medea, Institute of Engineering Sciences, Ain Dahab (Algeria); Benghanem, M. [University of Sciences Technology Houari Boumediene (USTHB), Faculty of ElectricalEngineering, El-Alia, Algiers (Algeria); Hadj Arab, A. [Development Center of Renewable Energy (CDER), Bouzareah, Algiers (Algeria); Guessoum, A. [Ministry for the Higher Education and Scientific Research, Algiers (Algeria)

    2004-07-01

    The main of this work is to train the RBF-IIR model to learn the prediction and modeling of the signals from stand-alone PV system. Once trained, the RBF-IIR estimates these signals faster. The validation of the model was performed with unknown signals data, which the network has not seen before. The ability of the network to make acceptable predictions even in an unusual day is an advantage of the present method. The estimation with correlation coefficient varied between 82 to 99 % was obtained. This accuracy is well within the acceptable level used by design engineers. The advantage of this model is to predict of different signal coming from the stand-alone prediction signals allow to analyzing and studying the performance of the PV systems and the sizing of PV system. Also this model have been compared between different neural networks structures, and given good results. (orig.)

  2. Physics-Informed Machine Learning for Predictive Turbulence Modeling: Using Data to Improve RANS Modeled Reynolds Stresses

    CERN Document Server

    Wang, Jian-Xun; Xiao, Heng

    2016-01-01

    Turbulence modeling is a critical component in numerical simulations of industrial flows based on Reynolds-averaged Navier-Stokes (RANS) equations. However, after decades of efforts in the turbulence modeling community, universally applicable RANS models with predictive capabilities are still lacking. Recently, data-driven methods have been proposed as a promising alternative to the traditional approaches of turbulence model development. In this work we propose a data-driven, physics-informed machine learning approach for predicting discrepancies in RANS modeled Reynolds stresses. The discrepancies are formulated as functions of the mean flow features. By using a modern machine learning technique based on random forests, the discrepancy functions are first trained with benchmark flow data and then used to predict Reynolds stresses discrepancies in new flows. The method is used to predict the Reynolds stresses in the flow over periodic hills by using two training flow scenarios of increasing difficulties: (1) ...

  3. Improving Environmental Model Calibration and Prediction

    Science.gov (United States)

    2011-01-18

    groundwater model calibration. Adv. Water Resour., 29(4):605–623, 2006. [9] B.E. Skahill, J.S. Baggett, S. Frankenstein , and C.W. Downer. More efficient...of Hydrology, Environmental Modelling & Software, or Water Resources Research). Skahill, B., Baggett, J., Frankenstein , S., and Downer, C.W. (2009

  4. [Structural Equation Modeling of Quality of Work Life in Clinical Nurses based on the Culture-Work-Health Model].

    Science.gov (United States)

    Kim, Miji; Ryu, Eunjung

    2015-12-01

    The purpose of this study was to construct and test a structural equation model of quality of work life for clinical nurses based on Peterson and Wilson's Culture-Work-Health model (CWHM). A structured questionnaire was completed by 523 clinical nurses to analyze the relationships between concepts of CWHM-organizational culture, social support, employee health, organizational health, and quality of work life. Among these conceptual variables of CWHM, employee health was measured by perceived health status, and organizational health was measured by presenteeism. SPSS21.0 and AMOS 21.0 programs were used to analyze the efficiency of the hypothesized model and calculate the direct and indirect effects of factors affecting quality of work life among clinical nurses. The goodness-of-fit statistics of the final modified hypothetical model are as follows: χ²=586.03, χ²/df=4.19, GFI=.89, AGFI=.85, CFI=.91, TLI=.90, NFI=.89, and RMSEA=.08. The results revealed that organizational culture, social support, organizational health, and employee health accounted for 69% of clinical nurses' quality of work life. The major findings of this study indicate that it is essential to create a positive organizational culture and provide adequate organizational support to maintain a balance between the health of clinical nurses and the organization. Further repeated and expanded studies are needed to explore the multidimensional aspects of clinical nurses' quality of work life in Korea, including various factors, such as work environment, work stress, and burnout.

  5. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    load shifting capabilities of the units that adapts to the given price predictions. We furthermore evaluated control performance in terms of economic savings for different control strategies and forecasts. Chapter 5 describes and compares the proposed large-scale Aggregator control strategies....... Aggregators are assumed to play an important role in the future Smart Grid and coordinate a large portfolio of units. The developed economic MPC controllers interfaces each unit directly to an Aggregator. We developed several MPC-based aggregation strategies that coordinates the global behavior of a portfolio...

  6. ECONOMIC AND MATHEMATICAL MODEL OF PREDICTION OF DEVIATION IN MOSCOW SUBURBAN RAILWAY COMPLEX

    Directory of Open Access Journals (Sweden)

    Dmitry I. Valdman

    2013-01-01

    Full Text Available The article deals with the theoretical aspects of mathematical modeling and forecasting. Additionally, it describes a mathematical model for forecasting the number of incidents, depending on the number of different types of planned works with one and the same subject in service facilities, validation of the model via substituting of the data and comparing the predicted values calculated by the model and the actual values for the same periods.

  7. Combining logistic regression and neural networks to create predictive models.

    OpenAIRE

    Spackman, K. A.

    1992-01-01

    Neural networks are being used widely in medicine and other areas to create predictive models from data. The statistical method that most closely parallels neural networks is logistic regression. This paper outlines some ways in which neural networks and logistic regression are similar, shows how a small modification of logistic regression can be used in the training of neural network models, and illustrates the use of this modification for variable selection and predictive model building wit...

  8. Assessment of performance of survival prediction models for cancer prognosis

    Directory of Open Access Journals (Sweden)

    Chen Hung-Chia

    2012-07-01

    Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in

  9. Evaluating a Community-School Model of Social Work Practice

    Science.gov (United States)

    Diehl, Daniel; Frey, Andy

    2008-01-01

    While research has shown that social workers can have positive impacts on students' school adjustment, evaluations of overall practice models continue to be limited. This article evaluates a model of community-school social work practice by examining its effect on problem behaviors and concerns identified by teachers and parents at referral. As…

  10. Investigating the LGBTQ Responsive Model for Supervision of Group Work

    Science.gov (United States)

    Luke, Melissa; Goodrich, Kristopher M.

    2013-01-01

    This article reports an investigation of the LGBTQ Responsive Model for Supervision of Group Work, a trans-theoretical supervisory framework to address the needs of lesbian, gay, bisexual, transgender, and questioning (LGBTQ) persons (Goodrich & Luke, 2011). Findings partially supported applicability of the LGBTQ Responsive Model for Supervision…

  11. Investigating the LGBTQ Responsive Model for Supervision of Group Work

    Science.gov (United States)

    Luke, Melissa; Goodrich, Kristopher M.

    2013-01-01

    This article reports an investigation of the LGBTQ Responsive Model for Supervision of Group Work, a trans-theoretical supervisory framework to address the needs of lesbian, gay, bisexual, transgender, and questioning (LGBTQ) persons (Goodrich & Luke, 2011). Findings partially supported applicability of the LGBTQ Responsive Model for Supervision…

  12. Validation of ice loads predicted from meteorological models

    Energy Technology Data Exchange (ETDEWEB)

    Veal, A.; Skea, A. [UK Met Office, Exeter, England (United Kingdom); Wareing, B. [Brian Wareing Tech Ltd., England (United Kingdom)

    2005-07-01

    Results of a field trial conducted on 2 Gerber PVM-100 instruments at Deadwater Fell test site in the United Kingdom were presented. The trials were conducted to assess whether the instruments were capable of measuring the liquid water content of the air, as well as to validate an ice model in terms of accretion rates on different sized conductors. Ambient air temperature, wind speed and direction were monitored at the Deadwater Fell weather station along with load cell values. Time lapse video recorders and a web camera system were used to view the performance of the conductors in varying weather conditions. All data was collected and stored at the site. It was anticipated that output from the instruments could be related to the conditions under which overhead line conductors suffer from ice loads, and help to revise weather maps which have proved to be incompatible with utility experience and the lifetimes achieved by overhead line designs. The data provided from the Deadwater work included logged data from the Gerbers, weather data and load data from a 10 mm diameter aluminium alloy conductor. When the combination of temperature, wind direction and Gerber output indicated icing conditions, they were confirmed by the conductor's load cell data. The tests confirmed the validity of the Gerber instruments to predict the occurrence of icing conditions, when combined with other meteorological data. It was concluded that the instruments may aid in optimized prediction methods for ice loads and icing events. 2 refs., 4 figs.

  13. Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    Considering the complexity of vegetables price forecast,the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm and using the characteristics of genetic algorithm and neural work.Taking mushrooms as an example,the parameters of the model are analyzed through experiment.In the end,the results of genetic algorithm and BP neural network are compared.The results show that the absolute error of prediction data is in the scale of 10%;in the scope that the absolute error in the prediction data is in the scope of 20% and 15%.The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model,especially the absolute error of prediction data is within the scope of 20%.The accuracy of genetic algorithm based on neural network is obviously better than BP neural network model,which represents the favorable generalization capability of the model.

  14. Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory.

    Science.gov (United States)

    Kazerounian, Sohrob; Grossberg, Stephen

    2014-01-01

    How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list ABADBD. Comparisons

  15. Prediction of Repair Work Duration for Gas Transport Systems Based on Small Data Samples

    DEFF Research Database (Denmark)

    Lesnykh, Valery; Litvin, Yuri; Kozin, Igor

    2016-01-01

    Prediction of the duration of a repair and maintenance project of a gas transport system is an important part of planning activities. There exist numerous sources of uncertainties that may result in time overruns possibly leading to multiple negative consequences. Our experience in planning...... this work suggests that accepting the stochastic nature of the project duration is a constructive step towards the preparedness to contingencies and defining penalties for repair companies. To support this approach, one needs to construct probability distributions of the durations of the projects...

  16. Does evening work predict sickness absence among female carers of the elderly?

    DEFF Research Database (Denmark)

    Tüchsen, Finn; Christensen, Karl Bang; Nabe-Nielsen, Kirsten

    2008-01-01

    OBJECTIVES: The aim of the present study was to predict the risk ratio of sickness absence lasting > or = 2 weeks due to shift work among Danish workers caring for the elderly during the evening and at night. METHODS: A sample of Danish carers of the elderly were interviewed in 2005. The response...... rate was 78%. A cohort of 5627 shift and day workers was followed for sickness absence lasting > or = 2 weeks and for sickness absence lasting > or = 8 weeks in a sickness compensation register covering all social transfer payments in Denmark. RESULTS: Among the evening workers, the rate ratio (RR...

  17. A systematic review of predictive modeling for bronchiolitis.

    Science.gov (United States)

    Luo, Gang; Nkoy, Flory L; Gesteland, Per H; Glasgow, Tiffany S; Stone, Bryan L

    2014-10-01

    Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease course to help determine a proper management plan. This paper reviews the existing state of the art of predictive modeling for bronchiolitis. Predictive modeling for respiratory syncytial virus (RSV) infection is covered whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases. A systematic review was conducted through a PubMed search up to April 25, 2014. The literature on predictive modeling for bronchiolitis was retrieved using a comprehensive search query, which was developed through an iterative process. Search results were limited to human subjects, the English language, and children (birth to 18 years). The literature search returned 2312 references in total. After manual review, 168 of these references were determined to be relevant and are discussed in this paper. We identify several limitations and open problems in predictive modeling for bronchiolitis, and provide some preliminary thoughts on how to address them, with the hope to stimulate future research in this domain. Many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  18. Microcellular propagation prediction model based on an improved ray tracing algorithm.

    Science.gov (United States)

    Liu, Z-Y; Guo, L-X; Fan, T-Q

    2013-11-01

    Two-dimensional (2D)/two-and-one-half-dimensional ray tracing (RT) algorithms for the use of the uniform theory of diffraction and geometrical optics are widely used for channel prediction in urban microcellular environments because of their high efficiency and reliable prediction accuracy. In this study, an improved RT algorithm based on the "orientation face set" concept and on the improved 2D polar sweep algorithm is proposed. The goal is to accelerate point-to-point prediction, thereby making RT prediction attractive and convenient. In addition, the use of threshold control of each ray path and the handling of visible grid points for reflection and diffraction sources are adopted, resulting in an improved efficiency of coverage prediction over large areas. Measured results and computed predictions are also compared for urban scenarios. The results indicate that the proposed prediction model works well and is a useful tool for microcellular communication applications.

  19. Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2002-01-01

    Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...

  20. An Evaluation of Muzzle Flash Prediction Models

    Science.gov (United States)

    1983-11-01

    21005 10. PROGRAM ELEMENT, PROJECT. TASK AREA & WORK UNIT NUMBERS 1L161102AH43 It. CONTROLLING OFFICE NAME AND ADDRESS US Army AMCCOM, ARDC...Bracuti Dover, NJ 07801 Commander Armament R&D Ctr, USAAMCCOM ATTN: DRSMC-SCA, L. Stiefel B. Brodman DRSMC-LCB-I, D. Spring DRSMC-LCE, R...Number 2. Does this report satisfy a need? (Comment on purpose, related project, or other area of interest for which report will be used.) 3. How

  1. Prediction of Repair Work Duration for Gas Transport Systems Based on Small Data Samples

    DEFF Research Database (Denmark)

    Lesnykh, Valery; Litvin, Yuri; Kozin, Igor

    2016-01-01

    Prediction of the duration of a repair and maintenance project of a gas transport system is an important part of planning activities. There exist numerous sources of uncertainties that may result in time overruns possibly leading to multiple negative consequences. Our experience in planning...... this work suggests that accepting the stochastic nature of the project duration is a constructive step towards the preparedness to contingencies and defining penalties for repair companies. To support this approach, one needs to construct probability distributions of the durations of the projects...... of concurrently running subprojects. Following this, guidance is provided on how to decide about what duration should define the deadline for completion of the whole work. A simple example is provided....

  2. Models for short term malaria prediction in Sri Lanka

    Directory of Open Access Journals (Sweden)

    Galappaththy Gawrie NL

    2008-05-01

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

  3. Models of Verbal Working Memory Capacity: What Does It Take to Make Them Work?

    Science.gov (United States)

    Cowan, Nelson; Rouder, Jeffrey N.; Blume, Christopher L.; Saults, J. Scott

    2012-01-01

    Theories of working memory (WM) capacity limits will be more useful when we know what aspects of performance are governed by the limits and what aspects are governed by other memory mechanisms. Whereas considerable progress has been made on models of WM capacity limits for visual arrays of separate objects, less progress has been made in…

  4. Asymmetric cross-domain interference between two working memory tasks : Implications for models of working memory

    NARCIS (Netherlands)

    Morey, Candice C.; Morey, Richard D.; van der Reijden, Madeleine; Holweg, Margot

    2013-01-01

    Observations of higher dual-task costs for within-domain than cross-domain task combinations constitute classic evidence for multi-component models of working memory (e.g., Baddeley, 1986; Logie, 2011). However, we report an asymmetric pattern of interference between verbal and visual-spatial tasks,

  5. A Probabilistic Model of Visual Working Memory: Incorporating Higher Order Regularities into Working Memory Capacity Estimates

    Science.gov (United States)

    Brady, Timothy F.; Tenenbaum, Joshua B.

    2013-01-01

    When remembering a real-world scene, people encode both detailed information about specific objects and higher order information like the overall gist of the scene. However, formal models of change detection, like those used to estimate visual working memory capacity, assume observers encode only a simple memory representation that includes no…

  6. Asymmetric cross-domain interference between two working memory tasks : Implications for models of working memory

    NARCIS (Netherlands)

    Morey, Candice C.; Morey, Richard D.; van der Reijden, Madeleine; Holweg, Margot

    2013-01-01

    Observations of higher dual-task costs for within-domain than cross-domain task combinations constitute classic evidence for multi-component models of working memory (e.g., Baddeley, 1986; Logie, 2011). However, we report an asymmetric pattern of interference between verbal and visual-spatial tasks,

  7. Aggregate driver model to enable predictable behaviour

    Science.gov (United States)

    Chowdhury, A.; Chakravarty, T.; Banerjee, T.; Balamuralidhar, P.

    2015-09-01

    The categorization of driving styles, particularly in terms of aggressiveness and skill is an emerging area of interest under the broader theme of intelligent transportation. There are two possible discriminatory techniques that can be applied for such categorization; a microscale (event based) model and a macro-scale (aggregate) model. It is believed that an aggregate model will reveal many interesting aspects of human-machine interaction; for example, we may be able to understand the propensities of individuals to carry out a given task over longer periods of time. A useful driver model may include the adaptive capability of the human driver, aggregated as the individual propensity to control speed/acceleration. Towards that objective, we carried out experiments by deploying smartphone based application to be used for data collection by a group of drivers. Data is primarily being collected from GPS measurements including position & speed on a second-by-second basis, for a number of trips over a two months period. Analysing the data set, aggregate models for individual drivers were created and their natural aggressiveness were deduced. In this paper, we present the initial results for 12 drivers. It is shown that the higher order moments of the acceleration profile is an important parameter and identifier of journey quality. It is also observed that the Kurtosis of the acceleration profiles stores major information about the driving styles. Such an observation leads to two different ranking systems based on acceleration data. Such driving behaviour models can be integrated with vehicle and road model and used to generate behavioural model for real traffic scenario.

  8. Validating predictions from climate envelope models

    Science.gov (United States)

    Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.

    2013-01-01

    Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

  9. Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks

    Directory of Open Access Journals (Sweden)

    Asiya Khan

    2010-01-01

    Full Text Available The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS networks. In order to characterize the Quality of Service (QoS level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS. The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.

  10. Accurate Holdup Calculations with Predictive Modeling & Data Integration

    Energy Technology Data Exchange (ETDEWEB)

    Azmy, Yousry [North Carolina State Univ., Raleigh, NC (United States). Dept. of Nuclear Engineering; Cacuci, Dan [Univ. of South Carolina, Columbia, SC (United States). Dept. of Mechanical Engineering

    2017-04-03

    In facilities that process special nuclear material (SNM) it is important to account accurately for the fissile material that enters and leaves the plant. Although there are many stages and processes through which materials must be traced and measured, the focus of this project is material that is “held-up” in equipment, pipes, and ducts during normal operation and that can accumulate over time into significant quantities. Accurately estimating the holdup is essential for proper SNM accounting (vis-à-vis nuclear non-proliferation), criticality and radiation safety, waste management, and efficient plant operation. Usually it is not possible to directly measure the holdup quantity and location, so these must be inferred from measured radiation fields, primarily gamma and less frequently neutrons. Current methods to quantify holdup, i.e. Generalized Geometry Holdup (GGH), primarily rely on simple source configurations and crude radiation transport models aided by ad hoc correction factors. This project seeks an alternate method of performing measurement-based holdup calculations using a predictive model that employs state-of-the-art radiation transport codes capable of accurately simulating such situations. Inverse and data assimilation methods use the forward transport model to search for a source configuration that best matches the measured data and simultaneously provide an estimate of the level of confidence in the correctness of such configuration. In this work the holdup problem is re-interpreted as an inverse problem that is under-determined, hence may permit multiple solutions. A probabilistic approach is applied to solving the resulting inverse problem. This approach rates possible solutions according to their plausibility given the measurements and initial information. This is accomplished through the use of Bayes’ Theorem that resolves the issue of multiple solutions by giving an estimate of the probability of observing each possible solution. To use

  11. Associations between a Leader's Work Passion and an Employee's Work Passion: A Moderated Mediation Model

    Science.gov (United States)

    Li, Jingjing; Zhang, Jian; Yang, Zhiguo

    2017-01-01

    Based on the theory of emotional contagion and goal content, this study explored the positive associations between a leader's work passion and employees' work passion. This study investigated 364 employees and their immediate leaders from China, constructed a moderated mediation model, and used SPSS-PROCESS in conjunction with the Johnson-Neyman technique to analyze the data. The results showed that a leader's work passion was transferred to employees via emotional contagion, and the contagion process was moderated by leader–employee goal content congruence. This study provides a potential way to stimulate employees' work passion from the perspective of leader–employee interactions. Moreover, the limitations of the study and potential topics for future research are discussed. PMID:28894430

  12. Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.

    Science.gov (United States)

    Wang, Ming; Long, Qi

    2016-09-01

    Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to high-dimensional settings. The predictive accuracy of prediction models for cancer recurrence after prostatectomy is assessed by applying the proposed approaches. We find that the estimated predictive accuracy for the models in consideration is sensitive to NCAR assumption, and thus identify the best predictive model. Finally, we further evaluate the performance of the proposed methods in both settings of low-dimensional and high-dimensional data under CAR and NCAR through simulations.

  13. How algebraic Bethe ansatz works for integrable model

    CERN Document Server

    Fadeev, L

    1996-01-01

    I study the technique of Algebraic Bethe Ansatz for solving integrable models and show how it works in detail on the simplest example of spin 1/2 XXX magnetic chain. Several other models are treated more superficially, only the specific details are given. Several parameters, appearing in these generalizations: spin s, anisotropy parameter \\ga, shift \\om in the alternating chain, allow to include in our treatment most known examples of soliton theory, including relativistic model of Quantum Field Theory.

  14. 3D computer modeling of sitting working place.

    Science.gov (United States)

    Mijović, B; Ujević, D; Skoko, M; Baksa, S

    2002-12-01

    Ergonomic contribution to designing and modeling of sitting working place by use of a computer and computer programs have been presented in this work. The influences of modeling working places on regular posture of a man/woman during work have been reconsidered, so that consumption of energy and fatigue are brought down to a minimum. For that purpose a computer program has been made which with input data on various kinds of work, sex and height of a worker determines the optimal ergonomic parameters during the modeling of a sitting working place. By computer visualisation the values of angle of spine curving have been calculated, the manipulation angle of arms and legs for three anthropometric heights of workers (160 cm, 175 cm and 190 cm). The dimensions of manipulative body space have been established by computerised 3D anthropometric analysis of movement as for example, reach of arms, legs, head, back etc positions. In this process the dimensions of machine and working space surrounding it in respect to optimal utilisation have been put in accordance with the anthropometric size of a man/woman.

  15. Neural-networks-based feedback linearization versus model predictive control of continuous alcoholic fermentation process

    Energy Technology Data Exchange (ETDEWEB)

    Mjalli, F.S.; Al-Asheh, S. [Chemical Engineering Department, Qatar University, Doha (Qatar)

    2005-10-01

    In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves. (Abstract Copyright [2005], Wiley Periodicals, Inc.)

  16. Noncausal spatial prediction filtering based on an ARMA model

    Institute of Scientific and Technical Information of China (English)

    Liu Zhipeng; Chen Xiaohong; Li Jingye

    2009-01-01

    Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.

  17. Performance Predictable ServiceBSP Model for Grid Computing

    Institute of Scientific and Technical Information of China (English)

    TONG Weiqin; MIAO Weikai

    2007-01-01

    This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model,the agents carrying computing tasks are dispatched to the local domain of the selected computation services. By using the IP (integer program) approach, the Service Selection Agent selects the computation services with global optimized QoS (quality of service) consideration. The performance of a ServiceBSP application can be predicted according to the performance prediction model based on the QoS of the selected services. The performance prediction model can help users to analyze their applications and improve them by optimized the factors which affects the performance. The experiment shows that the Service Selection Agent can provide ServiceBSP users with satisfied QoS of applications.

  18. FOREST ECOSYSTEM DYNAMICS ASSESSMENT AND PREDICTIVE MODELLING IN EASTERN HIMALAYA

    Directory of Open Access Journals (Sweden)

    S. P. S. Kushwaha

    2012-09-01

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

  19. Two Predictions of a Compound Cue Model of Priming

    OpenAIRE

    Walenski, Matthew

    2003-01-01

    This paper examines two predictions of the compound cue model of priming (Ratcliff and McKoon, 1988). While this model has been used to provide an account of a wide range of priming effects, it may not actually predict priming in these or other circumstances. In order to predict priming effects, the compound cue model relies on an assumption that all items have the same number of associates. This assumption may be true in only a restricted number of cases. This paper demonstrates that when th...

  20. Aerodynamic Noise Prediction Using stochastic Turbulence Modeling

    Directory of Open Access Journals (Sweden)

    Arash Ahmadzadegan

    2008-01-01

    Full Text Available Amongst many approaches to determine the sound propagated from turbulent flows, hybrid methods, in which the turbulent noise source field is computed or modeled separately from the far field calculation, are frequently used. For basic estimation of sound propagation, less computationally intensive methods can be developed using stochastic models of the turbulent fluctuations (turbulent noise source field. A simple and easy to use stochastic model for generating turbulent velocity fluctuations called continuous filter white noise (CFWN model was used. This method based on the use of classical Langevian-equation to model the details of fluctuating field superimposed on averaged computed quantities. The resulting sound field due to the generated unsteady flow field was evaluated using Lighthill's acoustic analogy. Volume integral method used for evaluating the acoustic analogy. This formulation presents an advantage, as it confers the possibility to determine separately the contribution of the different integral terms and also integration regions to the radiated acoustic pressure. Our results validated by comparing the directivity and the overall sound pressure level (OSPL magnitudes with the available experimental results. Numerical results showed reasonable agreement with the experiments, both in maximum directivity and magnitude of the OSPL. This method presents a very suitable tool for the noise calculation of different engineering problems in early stages of the design process where rough estimates using cheaper methods are needed for different geometries.