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Sample records for making predictive models

  1. Models of Affective Decision Making: How Do Feelings Predict Choice?

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

    2016-06-01

    Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. © The Author(s) 2016.

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

    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.

  3. Event Prediction for Modeling Mental Simulation in Naturalistic Decision Making

    Kunde, Dietmar

    2005-01-01

    ... and increasingly important asymmetric warfare scenarios. Although improvements in computer technology support more and more detailed representations, human decision making is still far from being automated in a realistic way...

  4. Making detailed predictions makes (some) predictions worse

    Kelly, Theresa F.

    In this paper, we investigate whether making detailed predictions about an event makes other predictions worse. Across 19 experiments, 10,895 participants, and 415,960 predictions about 724 professional sports games, we find that people who made detailed predictions about sporting events (e.g., how many hits each baseball team would get) made worse predictions about more general outcomes (e.g., which team would win). We rule out that this effect is caused by inattention or fatigue, thinking too hard, or a differential reliance on holistic information about the teams. Instead, we find that thinking about game-relevant details before predicting winning teams causes people to give less weight to predictive information, presumably because predicting details makes information that is relatively useless for predicting the winning team more readily accessible in memory and therefore incorporated into forecasts. Furthermore, we show that this differential use of information can be used to predict what kinds of games will and will not be susceptible to the negative effect of making detailed predictions.

  5. Model : making

    Bottle, Neil

    2013-01-01

    The Model : making exhibition was curated by Brian Kennedy in collaboration with Allies & Morrison in September 2013. For the London Design Festival, the Model : making exhibition looked at the increased use of new technologies by both craft-makers and architectural model makers. In both practices traditional ways of making by hand are increasingly being combined with the latest technologies of digital imaging, laser cutting, CNC machining and 3D printing. This exhibition focussed on ...

  6. Decision Making in Reference to Model of Marketing Predictive Analytics – Theory and Practice

    Piotr Tarka

    2014-03-01

    Full Text Available Purpose: The objective of this paper is to describe concepts and assumptions of predictive marketing analytics in reference to decision making. In particular, we highlight issues pertaining to the importance of data and the modern approach to data analysis and processing with the purpose of solving real marketing problems that companies encounter in business. Methodology: In this paper authors provide two study cases showing how, and to what extent predictive marketing analytics work can be useful in practice e.g., investigation of the marketing environment. The two cases are based on organizations operating mainly on Web site domain. The fi rst part of this article, begins a discussion with the explanation of a general idea of predictive marketing analytics. The second part runs through opportunities it creates for companies in the process of building strong competitive advantage in the market. The paper article ends with a brief comparison of predictive analytics versus traditional marketing-mix analysis. Findings: Analytics play an extremely important role in the current process of business management based on planning, organizing, implementing and controlling marketing activities. Predictive analytics provides the actual and current picture of the external environment. They also explain what problems are faced with the company in business activities. Analytics tailor marketing solutions to the right time and place at minimum costs. In fact they control the effi ciency and simultaneously increases the effectiveness of the firm. Practical implications: Based on the study cases comparing two enterprises carrying business activities in different areas, one can say that predictive analytics has far more been embraces extensively than classical marketing-mix analyses. The predictive approach yields greater speed of data collection and analysis, stronger predictive accuracy, better obtained competitor data, and more transparent models where one can

  7. Discharge destination following lower limb fracture: development of a prediction model to assist with decision making.

    Kimmel, Lara A; Holland, Anne E; Edwards, Elton R; Cameron, Peter A; De Steiger, Richard; Page, Richard S; Gabbe, Belinda

    2012-06-01

    Accurate prediction of the likelihood of discharge to inpatient rehabilitation following lower limb fracture made on admission to hospital may assist patient discharge planning and decrease the burden on the hospital system caused by delays in decision making. To develop a prognostic model for discharge to inpatient rehabilitation. Isolated lower extremity fracture cases (excluding fractured neck of femur), captured by the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), were extracted for analysis. A training data set was created for model development and validation data set for evaluation. A multivariable logistic regression model was developed based on patient and injury characteristics. Models were assessed using measures of discrimination (C-statistic) and calibration (Hosmer-Lemeshow (H-L) statistic). A total of 1429 patients met the inclusion criteria and were randomly split into training and test data sets. Increasing age, more proximal fracture type, compensation or private fund source for the admission, metropolitan location of residence, not working prior to injury and having a self-reported pre-injury disability were included in the final prediction model. The C-statistic for the model was 0.92 (95% confidence interval (CI) 0.88, 0.95) with an H-L statistic of χ(2)=11.62, p=0.17. For the test data set, the C-statistic was 0.86 (95% CI 0.83, 0.90) with an H-L statistic of χ(2)=37.98, plower limb fracture was developed with excellent discrimination although the calibration was reduced in the test data set. This model requires prospective testing but could form an integral part of decision making in regards to discharge disposition to facilitate timely and accurate referral to rehabilitation and optimise resource allocation. Copyright © 2011 Elsevier Ltd. All rights reserved.

  8. Predicting and understanding law-making with word vectors and an ensemble model.

    Nay, John J

    2017-01-01

    Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a high-dimensional, semantic-laden vector space. This language representation enables our investigation into which words increase the probability of enactment for any topic. To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill's sponsor is in the majority party. To test the effect of changes to bills after their introduction on our ability to predict their final outcome, we compared using the bill text and meta-data available at the time of introduction with using the most recent data. At the time of introduction context-only predictions outperform text-only, and with the newest data text-only outperforms context-only. Combining text and context always performs best. We conducted a global sensitivity analysis on the combined model to determine important variables predicting enactment.

  9. Eradicating BVD, reviewing Irish programme data and model predictions to support prospective decision making.

    Thulke, H-H; Lange, M; Tratalos, J A; Clegg, T A; McGrath, G; O'Grady, L; O'Sullivan, P; Doherty, M L; Graham, D A; More, S J

    2018-02-01

    Bovine Viral Diarrhoea is an infectious production disease of major importance in many cattle sectors of the world. The infection is predominantly transmitted by animal contact. Postnatal infections are transient, leading to immunologically protected cattle. However, for a certain window of pregnancy, in utero infection of the foetus results in persistently infected (PI) calves being the major risk of BVD spread, but also an efficient target for controlling the infection. There are two acknowledged strategies to identify PI animals for removal: tissue tag testing (direct; also known as the Swiss model) and serological screening (indirect by interpreting the serological status of the herd; the Scandinavian model). Both strategies are effective in reducing PI prevalence and herd incidence. During the first four years of the Irish national BVD eradication programme (2013-16), it has been mandatory for all newborn calves to be tested using tissue tag testing. During this period, PI incidence has substantially declined. In recent times, there has been interest among stakeholders in a change to an indirect testing strategy, with potential benefit to the overall programme, particularly with respect to cost to farmers. Advice was sought on the usefulness of implementing the necessary changes. Here we review available data from the national eradication programme and strategy performance predictions from an expert system model to quantify expected benefits of the strategy change from strategic, budgetary and implementation points of view. Key findings from our work include (i) drawbacks associated with changes to programme implementation, in particular the loss of epidemiological information to allow real-time monitoring of eradication progress or to reliably predict time to eradication, (ii) the fact that only 25% of the herds in the Irish cattle sector (14% beef, 78% dairy herds) would benefit financially from a change to serosurveillance, with half of these participants

  10. Predicting IT Governance Performance : A Method for Model-Based Decision Making

    Simonsson, Mårten

    2008-01-01

    Contemporary enterprises are largely dependent on Information Technology (IT), which makes decision making on IT matters important. There are numerous issues that confuse IT decision making, including contradictive business needs, financial constraints, lack of communication between business and IT stakeholders and difficulty in understanding the often heterogeneous and integrated IT systems. The discipline of IT governance aims at providing the decision making structures, processes, and rela...

  11. Making predictions in the multiverse

    Freivogel, Ben

    2011-01-01

    I describe reasons to think we are living in an eternally inflating multiverse where the observable 'constants' of nature vary from place to place. The major obstacle to making predictions in this context is that we must regulate the infinities of eternal inflation. I review a number of proposed regulators, or measures. Recent work has ruled out a number of measures by showing that they conflict with observation, and focused attention on a few proposals. Further, several different measures have been shown to be equivalent. I describe some of the many nontrivial tests these measures will face as we learn more from theory, experiment and observation.

  12. Making predictions in the multiverse

    Freivogel, Ben, E-mail: benfreivogel@gmail.com [Center for Theoretical Physics and Laboratory for Nuclear Science, Massachusetts Institute of Technology, Cambridge, MA 02139 (United States)

    2011-10-21

    I describe reasons to think we are living in an eternally inflating multiverse where the observable 'constants' of nature vary from place to place. The major obstacle to making predictions in this context is that we must regulate the infinities of eternal inflation. I review a number of proposed regulators, or measures. Recent work has ruled out a number of measures by showing that they conflict with observation, and focused attention on a few proposals. Further, several different measures have been shown to be equivalent. I describe some of the many nontrivial tests these measures will face as we learn more from theory, experiment and observation.

  13. Making ecological models adequate

    Getz, Wayne M.; Marshall, Charles R.; Carlson, Colin J.; Giuggioli, Luca; Ryan, Sadie J.; Romañach, Stephanie; Boettiger, Carl; Chamberlain, Samuel D.; Larsen, Laurel; D'Odorico, Paolo; O'Sullivan, David

    2018-01-01

    Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems’ responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.

  14. A predictive model of nuclear power plant crew decision-making and performance in a dynamic simulation environment

    Coyne, Kevin Anthony

    The safe operation of complex systems such as nuclear power plants requires close coordination between the human operators and plant systems. In order to maintain an adequate level of safety following an accident or other off-normal event, the operators often are called upon to perform complex tasks during dynamic situations with incomplete information. The safety of such complex systems can be greatly improved if the conditions that could lead operators to make poor decisions and commit erroneous actions during these situations can be predicted and mitigated. The primary goal of this research project was the development and validation of a cognitive model capable of simulating nuclear plant operator decision-making during accident conditions. Dynamic probabilistic risk assessment methods can improve the prediction of human error events by providing rich contextual information and an explicit consideration of feedback arising from man-machine interactions. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) shows promise for predicting situational contexts that might lead to human error events, particularly knowledge driven errors of commission. ADS-IDAC generates a discrete dynamic event tree (DDET) by applying simple branching rules that reflect variations in crew responses to plant events and system status changes. Branches can be generated to simulate slow or fast procedure execution speed, skipping of procedure steps, reliance on memorized information, activation of mental beliefs, variations in control inputs, and equipment failures. Complex operator mental models of plant behavior that guide crew actions can be represented within the ADS-IDAC mental belief framework and used to identify situational contexts that may lead to human error events. This research increased the capabilities of ADS-IDAC in several key areas. The ADS-IDAC computer code was improved to support additional

  15. AN EXTENDED REINFORCEMENT LEARNING MODEL OF BASAL GANGLIA TO UNDERSTAND THE CONTRIBUTIONS OF SEROTONIN AND DOPAMINE IN RISK-BASED DECISION MAKING, REWARD PREDICTION, AND PUNISHMENT LEARNING

    Pragathi Priyadharsini Balasubramani

    2014-04-01

    Full Text Available Although empirical and neural studies show that serotonin (5HT plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL-framework. The model depicts the roles of dopamine (DA and serotonin (5HT in Basal Ganglia (BG. In this model, the DA signal is represented by the temporal difference error (δ, while the 5HT signal is represented by a parameter (α that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: 1 Risk-sensitive decision making, where 5HT controls risk assessment, 2 Temporal reward prediction, where 5HT controls time-scale of reward prediction, and 3 Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG.

  16. The mechanisms of feature inheritance as predicted by a systems-level model of visual attention and decision making.

    Hamker, Fred H

    2008-07-15

    Feature inheritance provides evidence that properties of an invisible target stimulus can be attached to a following mask. We apply a systemslevel model of attention and decision making to explore the influence of memory and feedback connections in feature inheritance. We find that the presence of feedback loops alone is sufficient to account for feature inheritance. Although our simulations do not cover all experimental variations and focus only on the general principle, our result appears of specific interest since the model was designed for a completely different purpose than to explain feature inheritance. We suggest that feedback is an important property in visual perception and provide a description of its mechanism and its role in perception.

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

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

    2017-10-01

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

  18. Issues and Importance of "Good" Starting Points for Nonlinear Regression for Mathematical Modeling with Maple: Basic Model Fitting to Make Predictions with Oscillating Data

    Fox, William

    2012-01-01

    The purpose of our modeling effort is to predict future outcomes. We assume the data collected are both accurate and relatively precise. For our oscillating data, we examined several mathematical modeling forms for predictions. We also examined both ignoring the oscillations as an important feature and including the oscillations as an important…

  19. Predictive Modeling of Physician-Patient Dynamics That Influence Sleep Medication Prescriptions and Clinical Decision-Making

    Beam, Andrew L.; Kartoun, Uri; Pai, Jennifer K.; Chatterjee, Arnaub K.; Fitzgerald, Timothy P.; Shaw, Stanley Y.; Kohane, Isaac S.

    2017-02-01

    Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data. Zolpidem and trazodone were the most widely prescribed initial sleep medication in a cohort of 1,105 patients. Some providers showed a historical preference for one medication, which was highly predictive of their future prescribing behavior. Using a predictive model (AUC = 0.77), physician preference largely determined which medication a patient received (OR = 3.13 p = 3 × 10-37). In addition to the dominant effect of empirically determined physician preference, discussion of depression in a patient’s note was found to have a statistically significant association with receiving a prescription for trazodone (OR = 1.38, p = 0.04). EMR data can yield insights into physician prescribing behavior based on real-world physician-patient interactions.

  20. "When does making detailed predictions make predictions worse?": Correction to Kelly and Simmons (2016).

    2016-10-01

    Reports an error in "When Does Making Detailed Predictions Make Predictions Worse" by Theresa F. Kelly and Joseph P. Simmons ( Journal of Experimental Psychology: General , Advanced Online Publication, Aug 8, 2016, np). In the article, the symbols in Figure 2 were inadvertently altered in production. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2016-37952-001.) In this article, we investigate whether making detailed predictions about an event worsens other predictions of the event. Across 19 experiments, 10,896 participants, and 407,045 predictions about 724 professional sports games, we find that people who made detailed predictions about sporting events (e.g., how many hits each baseball team would get) made worse predictions about more general outcomes (e.g., which team would win). We rule out that this effect is caused by inattention or fatigue, thinking too hard, or a differential reliance on holistic information about the teams. Instead, we find that thinking about game-relevant details before predicting winning teams causes people to give less weight to predictive information, presumably because predicting details makes useless or redundant information more accessible and thus more likely to be incorporated into forecasts. Furthermore, we show that this differential use of information can be used to predict what kinds of events will and will not be susceptible to the negative effect of making detailed predictions. PsycINFO Database Record (c) 2016 APA, all rights reserved

  1. Making business models

    Gudiksen, Sune Klok; Poulsen, Søren Bolvig; Buur, Jacob

    2014-01-01

    Well-established companies are currently struggling to secure profits due to the pressure from new players' business models as they take advantage of communication technology and new business-model configurations. Because of this, the business model research field flourishes currently; however, t...

  2. Model predictive control using fuzzy decision functions

    Kaymak, U.; Costa Sousa, da J.M.

    2001-01-01

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

  3. Predictive models of moth development

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

  4. Predictive modeling of complications.

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

    2016-09-01

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

  5. Archaeological predictive model set.

    2015-03-01

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

  6. Wind power prediction models

    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.

  7. Development of a Clinical Forecasting Model to Predict Comorbid Depression Among Diabetes Patients and an Application in Depression Screening Policy Making.

    Jin, Haomiao; Wu, Shinyi; Di Capua, Paul

    2015-09-03

    Depression is a common but often undiagnosed comorbid condition of people with diabetes. Mass screening can detect undiagnosed depression but may require significant resources and time. The objectives of this study were 1) to develop a clinical forecasting model that predicts comorbid depression among patients with diabetes and 2) to evaluate a model-based screening policy that saves resources and time by screening only patients considered as depressed by the clinical forecasting model. We trained and validated 4 machine learning models by using data from 2 safety-net clinical trials; we chose the one with the best overall predictive ability as the ultimate model. We compared model-based policy with alternative policies, including mass screening and partial screening, on the basis of depression history or diabetes severity. Logistic regression had the best overall predictive ability of the 4 models evaluated and was chosen as the ultimate forecasting model. Compared with mass screening, the model-based policy can save approximately 50% to 60% of provider resources and time but will miss identifying about 30% of patients with depression. Partial-screening policy based on depression history alone found only a low rate of depression. Two other heuristic-based partial screening policies identified depression at rates similar to those of the model-based policy but cost more in resources and time. The depression prediction model developed in this study has compelling predictive ability. By adopting the model-based depression screening policy, health care providers can use their resources and time better and increase their efficiency in managing their patients with depression.

  8. Fuzzy Predictions for Strategic Decision Making

    Hallin, Carina Antonia; Andersen, Torben Juul; Tveterås, Sigbjørn

    This article theorizes a new way to predict firm performance based on aggregation of sensing among frontline employees about changes in operational capabilities to update strategic action plans. We frame the approach in the context of first- and second-generation prediction markets and outline it...

  9. Inverse and Predictive Modeling

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

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

  10. Making Deformable Template Models Operational

    Fisker, Rune

    2000-01-01

    for estimation of the model parameters, which applies a combination of a maximum likelihood and minimum distance criterion. Another contribution is a very fast search based initialization algorithm using a filter interpretation of the likelihood model. These two methods can be applied to most deformable template......Deformable template models are a very popular and powerful tool within the field of image processing and computer vision. This thesis treats this type of models extensively with special focus on handling their common difficulties, i.e. model parameter selection, initialization and optimization....... A proper handling of the common difficulties is essential for making the models operational by a non-expert user, which is a requirement for intensifying and commercializing the use of deformable template models. The thesis is organized as a collection of the most important articles, which has been...

  11. Development of a Clinical Forecasting Model to Predict Comorbid Depression Among Diabetes Patients and an Application in Depression Screening Policy Making

    Jin, Haomiao; Wu, Shinyi; Di Capua, Paul

    2015-01-01

    Introduction Depression is a common but often undiagnosed comorbid condition of people with diabetes. Mass screening can detect undiagnosed depression but may require significant resources and time. The objectives of this study were 1) to develop a clinical forecasting model that predicts comorbid depression among patients with diabetes and 2) to evaluate a model-based screening policy that saves resources and time by screening only patients considered as depressed by the clinical forecasting...

  12. How to make predictions about future infectious disease risks

    Woolhouse, Mark

    2011-01-01

    Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models. PMID:21624924

  13. Cultural Resource Predictive Modeling

    2017-10-01

    CR cultural resource CRM cultural resource management CRPM Cultural Resource Predictive Modeling DoD Department of Defense ESTCP Environmental...resource management ( CRM ) legal obligations under NEPA and the NHPA, military installations need to demonstrate that CRM decisions are based on objective...maxim “one size does not fit all,” and demonstrate that DoD installations have many different CRM needs that can and should be met through a variety

  14. Candidate Prediction Models and Methods

    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....

  15. Making the most of sparse clinical data by using a predictive-model-based analysis, illustrated with a stavudine pharmacokinetic study.

    Zhang, L; Price, R; Aweeka, F; Bellibas, S E; Sheiner, L B

    2001-02-01

    A small-scale clinical investigation was done to quantify the penetration of stavudine (D4T) into cerebrospinal fluid (CSF). A model-based analysis estimates the steady-state ratio of AUCs of CSF and plasma concentrations (R(AUC)) to be 0.270, and the mean residence time of drug in the CSF to be 7.04 h. The analysis illustrates the advantages of a causal (scientific, predictive) model-based approach to analysis over a noncausal (empirical, descriptive) approach when the data, as here, demonstrate certain problematic features commonly encountered in clinical data, namely (i) few subjects, (ii) sparse sampling, (iii) repeated measures, (iv) imbalance, and (v) individual design variation. These features generally require special attention in data analysis. The causal-model-based analysis deals with features (i) and (ii), both of which reduce efficiency, by combining data from different studies and adding subject-matter prior information. It deals with features (iii)--(v), all of which prevent 'averaging' individual data points directly, first, by adjusting in the model for interindividual data differences due to design differences, secondly, by explicitly differentiating between interpatient, interoccasion, and measurement error variation, and lastly, by defining a scientifically meaningful estimand (R(AUC)) that is independent of design.

  16. Decision-making in schizophrenia: A predictive-coding perspective.

    Sterzer, Philipp; Voss, Martin; Schlagenhauf, Florian; Heinz, Andreas

    2018-05-31

    Dysfunctional decision-making has been implicated in the positive and negative symptoms of schizophrenia. Decision-making can be conceptualized within the framework of hierarchical predictive coding as the result of a Bayesian inference process that uses prior beliefs to infer states of the world. According to this idea, prior beliefs encoded at higher levels in the brain are fed back as predictive signals to lower levels. Whenever these predictions are violated by the incoming sensory data, a prediction error is generated and fed forward to update beliefs encoded at higher levels. Well-documented impairments in cognitive decision-making support the view that these neural inference mechanisms are altered in schizophrenia. There is also extensive evidence relating the symptoms of schizophrenia to aberrant signaling of prediction errors, especially in the domain of reward and value-based decision-making. Moreover, the idea of altered predictive coding is supported by evidence for impaired low-level sensory mechanisms and motor processes. We review behavioral and neural findings from these research areas and provide an integrated view suggesting that schizophrenia may be related to a pervasive alteration in predictive coding at multiple hierarchical levels, including cognitive and value-based decision-making processes as well as sensory and motor systems. We relate these findings to decision-making processes and propose that varying degrees of impairment in the implicated brain areas contribute to the variety of psychotic experiences. Copyright © 2018 Elsevier Inc. All rights reserved.

  17. An ecologically based model of alcohol-consumption decision making: evidence for the discriminative and predictive role of contextual reward and punishment information.

    Bogg, Tim; Finn, Peter R

    2009-05-01

    Using insights from Ecological Systems Theory and Reinforcement Sensitivity Theory, the current study assessed the utility of a series of hypothetical role-based alcohol-consumption scenarios that varied in their presentation of rewarding and punishing information. The scenarios, along with measures of impulsive sensation seeking and a self-report of weekly alcohol consumption, were administered to a sample of alcohol-dependent and non-alcohol-dependent college-age individuals (N = 170). The results showed scenario attendance decisions were largely unaffected by alcohol-dependence status and variations in contextual reward and punishment information. In contrast to the attendance findings, the results for the alcohol-consumption decisions showed alcohol-dependent individuals reported a greater frequency of deciding to drink, as well as indicating greater alcohol consumption in the contexts of complementary rewarding or nonpunishing information. Regression results provided evidence for the criterion-related validity of scenario outcomes in an account of diagnostic alcohol problems. The results are discussed in terms of the conceptual and predictive gains associated with an assessment approach to alcohol-consumption decision making that combines situational information organized and balanced through the frameworks of Ecological Systems Theory and Reinforcement Sensitivity Theory.

  18. Predictive Surface Complexation Modeling

    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.

  19. Water Habitat Study: Prediction Makes It More Meaningful.

    Glasgow, Dennis R.

    1982-01-01

    Suggests a teaching strategy for water habitat studies to help students make a meaningful connection between physiochemical data (dissolved oxygen content, pH, and water temperature) and biological specimens they collect. Involves constructing a poster and using it to make predictions. Provides sample poster. (DC)

  20. Does future-oriented thinking predict adolescent decision making?

    Eskritt, Michelle; Doucette, Jesslyn; Robitaille, Lori

    2014-01-01

    A number of theorists, as well as plain common sense, suggest that future-oriented thinking (FOT) should be involved in decision making; therefore, the development of FOT should be related to better quality decision making. FOT and quality of the decision making were measured in adolescents as well as adults in 2 different experiments. Though the results of the first experiment revealed an increase in quality of decision making across adolescence into adulthood, there was no relationship between FOT and decision making. In the second experiment, FOT predicted performance on a more deliberative decision-making task independent of age, but not performance on the Iowa Gambling Task (IGT). Performance on the IGT was instead related to emotion regulation. The study's findings suggest that FOT can be related to reflective decision making but not necessarily decision making that is more intuitive.

  1. [Mathematical models of decision making and learning].

    Ito, Makoto; Doya, Kenji

    2008-07-01

    Computational models of reinforcement learning have recently been applied to analysis of brain imaging and neural recording data to identity neural correlates of specific processes of decision making, such as valuation of action candidates and parameters of value learning. However, for such model-based analysis paradigms, selecting an appropriate model is crucial. In this study we analyze the process of choice learning in rats using stochastic rewards. We show that "Q-learning," which is a standard reinforcement learning algorithm, does not adequately reflect the features of choice behaviors. Thus, we propose a generalized reinforcement learning (GRL) algorithm that incorporates the negative reward effect of reward loss and forgetting of values of actions not chosen. Using the Bayesian estimation method for time-varying parameters, we demonstrated that the GRL algorithm can predict an animal's choice behaviors as efficiently as the best Markov model. The results suggest the usefulness of the GRL for the model-based analysis of neural processes involved in decision making.

  2. Extracting falsifiable predictions from sloppy models.

    Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

    2007-12-01

    Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

  3. Modeling Human Elements of Decision-Making

    2002-06-01

    include factors such as personality, emotion , and level of expertise, which vary from individual to individual. The process of decision - making during... rational choice theories such as utility theory, to more descriptive psychological models that focus more on the process of decision - making ...descriptive nature, they provide a more realistic representation of human decision - making than the rationally based models. However these models do

  4. Deep Predictive Models in Interactive Music

    Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

    2018-01-01

    Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

  5. What makes process models understandable?

    Mendling, J.; Reijers, H.A.; Cardoso, J.; Alonso, G.; Dadam, P.; Rosemann, M.

    2007-01-01

    Despite that formal and informal quality aspects are of significant importance to business process modeling, there is only little empirical work reported on process model quality and its impact factors. In this paper we investigate understandability as a proxy for quality of process models and focus

  6. Confidence scores for prediction models

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

  7. Rule-based decision making model

    Sirola, Miki

    1998-01-01

    A rule-based decision making model is designed in G2 environment. A theoretical and methodological frame for the model is composed and motivated. The rule-based decision making model is based on object-oriented modelling, knowledge engineering and decision theory. The idea of safety objective tree is utilized. Advanced rule-based methodologies are applied. A general decision making model 'decision element' is constructed. The strategy planning of the decision element is based on e.g. value theory and utility theory. A hypothetical process model is built to give input data for the decision element. The basic principle of the object model in decision making is division in tasks. Probability models are used in characterizing component availabilities. Bayes' theorem is used to recalculate the probability figures when new information is got. The model includes simple learning features to save the solution path. A decision analytic interpretation is given to the decision making process. (author)

  8. Predicting preferences: a neglected aspect of shared decision‐making

    Sevdalis, Nick; Harvey, Nigel

    2006-01-01

    Abstract In recent years, shared decision‐making between patients and doctors regarding choice of treatment has become an issue of priority. Although patients’ preferences lie at the core of the literature on shared decision‐making, there has not been any attempt so far to link the concept of shared decision‐making with the extensive behavioural literature on people's self‐predictions of their future preferences. The aim of the present review is to provide this link. First, we summarize behavioural research that suggests that people mispredict their future preferences and feelings. Secondly, we provide the main psychological accounts for people's mispredictions. Thirdly, we suggest three main empirical questions for inclusion in a programme aimed at enriching our understanding of shared decision‐making and improving the procedures used for putting it into practice. PMID:16911138

  9. PREDICTED PERCENTAGE DISSATISFIED (PPD) MODEL ...

    HOD

    their low power requirements, are relatively cheap and are environment friendly. ... PREDICTED PERCENTAGE DISSATISFIED MODEL EVALUATION OF EVAPORATIVE COOLING ... The performance of direct evaporative coolers is a.

  10. Predictive modelling: parents’ decision making to use online child health information to increase their understanding and/or diagnose or treat their child’s health

    Walsh Anne M

    2012-12-01

    Full Text Available Abstract Background The quantum increases in home Internet access and available online health information with limited control over information quality highlight the necessity of exploring decision making processes in accessing and using online information, specifically in relation to children who do not make their health decisions. The aim of this study was to understand the processes explaining parents’ decisions to use online health information for child health care. Methods Parents (N = 391 completed an initial questionnaire assessing the theory of planned behaviour constructs of attitude, subjective norm, and perceived behavioural control, as well as perceived risk, group norm, and additional demographic factors. Two months later, 187 parents completed a follow-up questionnaire assessing their decisions to use online information for their child’s health care, specifically to 1 diagnose and/or treat their child’s suspected medical condition/illness and 2 increase understanding about a diagnosis or treatment recommended by a health professional. Results Hierarchical multiple regression showed that, for both behaviours, attitude, subjective norm, perceived behavioural control, (less perceived risk, group norm, and (non medical background were the significant predictors of intention. For parents’ use of online child health information, for both behaviours, intention was the sole significant predictor of behaviour. The findings explain 77% of the variance in parents’ intention to treat/diagnose a child health problem and 74% of the variance in their intentions to increase their understanding about child health concerns. Conclusions Understanding parents’ socio-cognitive processes that guide their use of online information for child health care is important given the increase in Internet usage and the sometimes-questionable quality of health information provided online. Findings highlight parents’ thirst for information; there is an

  11. Model predictive Controller for Mobile Robot

    Alireza Rezaee

    2017-01-01

    This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...

  12. Arational heuristic model of economic decision making

    Grandori, Anna

    2010-01-01

    The article discuss the limits of both the rational actor and the behavioral paradigms in explaining and guiding innovative decision making and outlines a model of economic decision making that in the course of being 'heuristic' (research and discovery oriented) is also 'rational' (in the broad sense of following correct reasoning and scientific methods, non 'biasing'). The model specifies a set of 'rational heuristics' for innovative decision making, for the various sub-processes of problem ...

  13. A neural model of decision making

    Larsen, Torben

    2008-01-01

    Background: A descriptive neuroeconomic model is aimed for relativity of the concept of economic man to empirical science.Method: A 4-level client-server-integrator model integrating the brain models of McLean and Luria is the general framework for the model of empirical findings.Results: Decision making relies on integration across brain levels of emotional intelligence (LU) and logico-matematico intelligence (RIA), respectively. The integrated decision making formula approaching zero by bot...

  14. Massive Predictive Modeling using Oracle R Enterprise

    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...

  15. Subjective Expected Utility: A Model of Decision-Making.

    Fischoff, Baruch; And Others

    1981-01-01

    Outlines a model of decision making known to researchers in the field of behavioral decision theory (BDT) as subjective expected utility (SEU). The descriptive and predictive validity of the SEU model, probability and values assessment using SEU, and decision contexts are examined, and a 54-item reference list is provided. (JL)

  16. Bootstrap prediction and Bayesian prediction under misspecified models

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  17. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    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.

  18. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    2012-07-02

    Jul 2, 2012 ... signal based on a process model, coping with constraints on inputs and ... paper, we will present an introduction to the theory and application of MPC with Matlab codes ... section 5 presents the simulation results and section 6.

  19. Intuitionistic preference modeling and interactive decision making

    Xu, Zeshui

    2014-01-01

    This book offers an in-depth and comprehensive introduction to the priority methods of intuitionistic preference relations, the consistency and consensus improving procedures for intuitionistic preference relations, the approaches to group decision making based on intuitionistic preference relations, the approaches and models for interactive decision making with intuitionistic fuzzy information, and the extended results in interval-valued intuitionistic fuzzy environments.

  20. Melanoma Risk Prediction Models

    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.

  1. Modelling bankruptcy prediction models in Slovak companies

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  2. Predictive analytics can support the ACO model.

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  3. Predictive performance models and multiple task performance

    Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

    1989-01-01

    Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

  4. Predictive Models and Computational Embryology

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  5. Modelling decision-making by pilots

    Patrick, Nicholas J. M.

    1993-01-01

    Our scientific goal is to understand the process of human decision-making. Specifically, a model of human decision-making in piloting modern commercial aircraft which prescribes optimal behavior, and against which we can measure human sub-optimality is sought. This model should help us understand such diverse aspects of piloting as strategic decision-making, and the implicit decisions involved in attention allocation. Our engineering goal is to provide design specifications for (1) better computer-based decision-aids, and (2) better training programs for the human pilot (or human decision-maker, DM).

  6. Predictive Modeling in Race Walking

    Krzysztof Wiktorowicz

    2015-01-01

    Full Text Available This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.

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

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

    2015-07-01

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

  8. Making eco logic and models work

    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

  9. Reviewing model application to support animal health decision making.

    Singer, Alexander; Salman, Mo; Thulke, Hans-Hermann

    2011-04-01

    Animal health is of societal importance as it affects human welfare, and anthropogenic interests shape decision making to assure animal health. Scientific advice to support decision making is manifold. Modelling, as one piece of the scientific toolbox, is appreciated for its ability to describe and structure data, to give insight in complex processes and to predict future outcome. In this paper we study the application of scientific modelling to support practical animal health decisions. We reviewed the 35 animal health related scientific opinions adopted by the Animal Health and Animal Welfare Panel of the European Food Safety Authority (EFSA). Thirteen of these documents were based on the application of models. The review took two viewpoints, the decision maker's need and the modeller's approach. In the reviewed material three types of modelling questions were addressed by four specific model types. The correspondence between tasks and models underpinned the importance of the modelling question in triggering the modelling approach. End point quantifications were the dominating request from decision makers, implying that prediction of risk is a major need. However, due to knowledge gaps corresponding modelling studies often shed away from providing exact numbers. Instead, comparative scenario analyses were performed, furthering the understanding of the decision problem and effects of alternative management options. In conclusion, the most adequate scientific support for decision making - including available modelling capacity - might be expected if the required advice is clearly stated. Copyright © 2011 Elsevier B.V. All rights reserved.

  10. Modelling the Reduction of Project Making Duration

    Oleinik Pavel

    2017-01-01

    Full Text Available The article points out why earlier patterns of investment process were ineffective in developing the construction projects and shows sources for reducing of its total duration. It describes the procedure of statistical modeling and obtaining medium-term time parameters required for modern pattern of project-making; offers design formulas for assessment of total time required for project-making as well as for its main stages; reveals advantage of modern system of project-making against traditional one by comparing indicators of their duration.

  11. Making oneself predictable: Reduced temporal variability facilitates joint action coordination

    Vesper, Cordula; van der Wel, Robrecht; Knoblich, Günther

    2011-01-01

    Performing joint actions often requires precise temporal coordination of individual actions. The present study investigated how people coordinate their actions at discrete points in time when continuous or rhythmic information about others’ actions is not available. In particular, we tested...... the hypothesis that making oneself predictable is used as a coordination strategy. Pairs of participants were instructed to coordinate key presses in a two-choice reaction time task, either responding in synchrony (Experiments 1 and 2) or in close temporal succession (Experiment 3). Across all experiments, we...... found that coactors reduced the variability of their actions in the joint context compared with the same task performed individually. Correlation analyses indicated that the less variable the actions were, the better was interpersonal coordination. The relation between reduced variability and improved...

  12. Trustworthiness and Negative Affect Predict Economic Decision-Making

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

    2011-01-01

    The Ultimatum Game (UG) is a widely used and well-studied laboratory model of economic decision-making. Here, we studied 129 healthy adults and compared demographic (i.e., age, gender, education), cognitive (i.e., intelligence, attention/working memory, speed, language, visuospatial, memory, executive functions), and personality (i.e., “Big Five”, positive affect, negative affect) variables between those with a “rational” versus an “irrational” response pattern on the UG. Our data indicated t...

  13. Modeling as a Decision-Making Process

    Bleiler-Baxter, Sarah K.; Stephens, D. Christopher; Baxter, Wesley A.; Barlow, Angela T.

    2017-01-01

    The goal in this article is to support teachers in better understanding what it means to model with mathematics by focusing on three key decision-making processes: Simplification, Relationship Mapping, and Situation Analysis. The authors use the Theme Park task to help teachers develop a vision of how students engage in these three decision-making…

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

    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.

  15. Prediction Models for Dynamic Demand Response

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  16. Factors Influencing the Predictive Power of Models for Predicting Mortality and/or Heart Failure Hospitalization in Patients With Heart Failure

    Ouwerkerk, Wouter; Voors, Adriaan A.; Zwinderman, Aeilko H.

    2014-01-01

    The present paper systematically reviews and compares existing prediction models in order to establish the strongest variables, models, and model characteristics in patients with heart failure predicting outcome. To improve decision making accurately predicting mortality and heart-failure

  17. Decision-Making Competence Predicts Domain-Specific Risk Attitudes

    Joshua eWeller

    2015-05-01

    Full Text Available Decision Making Competence (DMC reflects individual differences in rational responding across several classic behavioral decision-making tasks. Although it has been associated with real-world risk behavior, less is known about the degree to which DMC contributes to specific components of risk attitudes. Utilizing a psychological risk-return framework, we examined the associations between risk attitudes and DMC. Italian community residents (n = 804 completed an online DMC measure, using a subset of the original Adult-DMC battery (A-DMC; Bruine de Bruin, Parker, & Fischhoff, 2007. Participants also completed a self-reported risk attitude measure for three components of risk attitudes (risk-taking, risk perceptions, and expected benefits across six risk domains. Overall, greater performance on the DMC component scales were inversely, albeit modestly, associated with risk-taking tendencies. Structural equation modeling results revealed that DMC was associated with lower perceived expected benefits for all domains. In contrast, its association with perceived risks was more domain-specific. These analyses also revealed stronger indirect effects for the DMC  expected benefits  risk-taking than the DMC  perceived risk  risk-taking path, especially for risk behaviors that may be considered more antisocial in nature. These results suggest that DMC performance differentially impacts specific components of risk attitudes, and may be more strongly related to the evaluation of expected value of the given behavior.

  18. Trustworthiness and Negative Affect Predict Economic Decision-Making.

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

    2011-09-01

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

  19. Predicting extinction rates in stochastic epidemic models

    Schwartz, Ira B; Billings, Lora; Dykman, Mark; Landsman, Alexandra

    2009-01-01

    We investigate the stochastic extinction processes in a class of epidemic models. Motivated by the process of natural disease extinction in epidemics, we examine the rate of extinction as a function of disease spread. We show that the effective entropic barrier for extinction in a susceptible–infected–susceptible epidemic model displays scaling with the distance to the bifurcation point, with an unusual critical exponent. We make a direct comparison between predictions and numerical simulations. We also consider the effect of non-Gaussian vaccine schedules, and show numerically how the extinction process may be enhanced when the vaccine schedules are Poisson distributed

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

    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.

  1. Quantum-Like Bayesian Networks for Modeling Decision Making

    Catarina eMoreira

    2016-01-01

    Full Text Available In this work, we explore an alternative quantum structure to perform quantum probabilistic inferences to accommodate the paradoxical findings of the Sure Thing Principle. We propose a Quantum-Like Bayesian Network, which consists in replacing classical probabilities by quantum probability amplitudes. However, since this approach suffers from the problem of exponential growth of quantum parameters, we also propose a similarity heuristic that automatically fits quantum parameters through vector similarities. This makes the proposed model general and predictive in contrast to the current state of the art models, which cannot be generalized for more complex decision scenarios and that only provide an explanatory nature for the observed paradoxes. In the end, the model that we propose consists in a nonparametric method for estimating inference effects from a statistical point of view. It is a statistical model that is simpler than the previous quantum dynamic and quantum-like models proposed in the literature. We tested the proposed network with several empirical data from the literature, mainly from the Prisoner's Dilemma game and the Two Stage Gambling game. The results obtained show that the proposed quantum Bayesian Network is a general method that can accommodate violations of the laws of classical probability theory and make accurate predictions regarding human decision-making in these scenarios.

  2. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

  3. Insights from triangulation of two purchase choice elicitation methods to predict social decision making in healthcare.

    Whitty, Jennifer A; Rundle-Thiele, Sharyn R; Scuffham, Paul A

    2012-03-01

    Discrete choice experiments (DCEs) and the Juster scale are accepted methods for the prediction of individual purchase probabilities. Nevertheless, these methods have seldom been applied to a social decision-making context. To gain an overview of social decisions for a decision-making population through data triangulation, these two methods were used to understand purchase probability in a social decision-making context. We report an exploratory social decision-making study of pharmaceutical subsidy in Australia. A DCE and selected Juster scale profiles were presented to current and past members of the Australian Pharmaceutical Benefits Advisory Committee and its Economic Subcommittee. Across 66 observations derived from 11 respondents for 6 different pharmaceutical profiles, there was a small overall median difference of 0.024 in the predicted probability of public subsidy (p = 0.003), with the Juster scale predicting the higher likelihood. While consistency was observed at the extremes of the probability scale, the funding probability differed over the mid-range of profiles. There was larger variability in the DCE than Juster predictions within each individual respondent, suggesting the DCE is better able to discriminate between profiles. However, large variation was observed between individuals in the Juster scale but not DCE predictions. It is important to use multiple methods to obtain a complete picture of the probability of purchase or public subsidy in a social decision-making context until further research can elaborate on our findings. This exploratory analysis supports the suggestion that the mixed logit model, which was used for the DCE analysis, may fail to adequately account for preference heterogeneity in some contexts.

  4. Model Driven Integrated Decision-Making in Manufacturing Enterprises

    Richard H. Weston

    2012-01-01

    Full Text Available Decision making requirements and solutions are observed in four world class Manufacturing Enterprises (MEs. Observations made focus on deployed methods of complexity handling that facilitate multi-purpose, distributed decision making. Also observed are examples of partially deficient “integrated decision making” which stem from lack of understanding about how ME structural relations enable and/or constrain reachable ME behaviours. To begin to address this deficiency the paper outlines the use of a “reference model of ME decision making” which can inform the structural design of decision making systems in MEs. Also outlined is a “systematic model driven approach to modelling ME systems” which can particularise the reference model in specific case enterprises and thereby can “underpin integrated ME decision making”. Coherent decomposition and representational mechanisms have been incorporated into the model driven approach to systemise complexity handling. The paper also describes in outline an application of the modelling method in a case study ME and explains how its use has improved the integration of previously distinct planning functions. The modelling approach is particularly innovative in respect to the way it structures the coherent creation and experimental re-use of “fit for purpose” discrete event (predictive simulation models at the multiple levels of abstraction.

  5. Ground Motion Prediction Models for Caucasus Region

    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.

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

    Kiviniemi, Marc T; Brown-Kramer, Carolyn R

    2015-05-01

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

  7. Comprehensible knowledge model creation for cancer treatment decision making.

    Afzal, Muhammad; Hussain, Maqbool; Ali Khan, Wajahat; Ali, Taqdir; Lee, Sungyoung; Huh, Eui-Nam; Farooq Ahmad, Hafiz; Jamshed, Arif; Iqbal, Hassan; Irfan, Muhammad; Abbas Hydari, Manzar

    2017-03-01

    A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making. An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality reduction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services: clinical decisions and education support. Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules. Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CKM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Distributed model predictive control made easy

    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 ...

  9. An analytical model for climatic predictions

    Njau, E.C.

    1990-12-01

    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  10. Breast cancer risks and risk prediction models.

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  11. Episodic memories predict adaptive value-based decision-making

    Murty, Vishnu; FeldmanHall, Oriel; Hunter, Lindsay E.; Phelps, Elizabeth A; Davachi, Lila

    2016-01-01

    Prior research illustrates that memory can guide value-based decision-making. For example, previous work has implicated both working memory and procedural memory (i.e., reinforcement learning) in guiding choice. However, other types of memories, such as episodic memory, may also influence decision-making. Here we test the role for episodic memory—specifically item versus associative memory—in supporting value-based choice. Participants completed a task where they first learned the value associated with trial unique lotteries. After a short delay, they completed a decision-making task where they could choose to re-engage with previously encountered lotteries, or new never before seen lotteries. Finally, participants completed a surprise memory test for the lotteries and their associated values. Results indicate that participants chose to re-engage more often with lotteries that resulted in high versus low rewards. Critically, participants not only formed detailed, associative memories for the reward values coupled with individual lotteries, but also exhibited adaptive decision-making only when they had intact associative memory. We further found that the relationship between adaptive choice and associative memory generalized to more complex, ecologically valid choice behavior, such as social decision-making. However, individuals more strongly encode experiences of social violations—such as being treated unfairly, suggesting a bias for how individuals form associative memories within social contexts. Together, these findings provide an important integration of episodic memory and decision-making literatures to better understand key mechanisms supporting adaptive behavior. PMID:26999046

  12. Risk prediction model: Statistical and artificial neural network approach

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

    2017-04-01

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

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

    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.

  14. A naturalistic decision making model for simulated human combatants

    HUNTER, KEITH O.; HART, WILLIAM E.; FORSYTHE, JAMES C.

    2000-01-01

    The authors describe a naturalistic behavioral model for the simulation of small unit combat. This model, Klein's recognition-primed decision making (RPD) model, is driven by situational awareness rather than a rational process of selecting from a set of action options. They argue that simulated combatants modeled with RPD will have more flexible and realistic responses to a broad range of small-scale combat scenarios. Furthermore, they note that the predictability of a simulation using an RPD framework can be easily controlled to provide multiple evaluations of a given combat scenario. Finally, they discuss computational issues for building an RPD-based behavior engine for fully automated combatants in small conflict scenarios, which are being investigated within Sandia's Next Generation Site Security project

  15. Making Predictions about Chemical Reactivity: Assumptions and Heuristics

    Maeyer, Jenine; Talanquer, Vicente

    2013-01-01

    Diverse implicit cognitive elements seem to support but also constrain reasoning in different domains. Many of these cognitive constraints can be thought of as either implicit assumptions about the nature of things or reasoning heuristics for decision-making. In this study we applied this framework to investigate college students' understanding of…

  16. Prediction of bread-making quality using size exclusion high ...

    Variation in the distribution of protein molecular weight in wheat (Triticum aestivum), influences breadmaking quality of wheat cultivars, resulting in either poor or good bread. The objective of this study was to predict breadmaking quality of wheat cultivars using size exclusion high performance liquid chromatography.

  17. What to make of Mendeleev’s predictions?

    Wray, K. Brad

    2018-01-01

    I critically examine Stewart’s (Found Chem, 2018) suggestion that we should weigh the various predictions Mendeleev made differently. I argue that in his effort to justify discounting the weight of some of Mendeleev’s failures, Stewart invokes a principle that will, in turn, reduce the weight of ...

  18. A dynamic dual process model of risky decision making.

    Diederich, Adele; Trueblood, Jennifer S

    2018-03-01

    Many phenomena in judgment and decision making are often attributed to the interaction of 2 systems of reasoning. Although these so-called dual process theories can explain many types of behavior, they are rarely formalized as mathematical or computational models. Rather, dual process models are typically verbal theories, which are difficult to conclusively evaluate or test. In the cases in which formal (i.e., mathematical) dual process models have been proposed, they have not been quantitatively fit to experimental data and are often silent when it comes to the timing of the 2 systems. In the current article, we present a dynamic dual process model framework of risky decision making that provides an account of the timing and interaction of the 2 systems and can explain both choice and response-time data. We outline several predictions of the model, including how changes in the timing of the 2 systems as well as time pressure can influence behavior. The framework also allows us to explore different assumptions about how preferences are constructed by the 2 systems as well as the dynamic interaction of the 2 systems. In particular, we examine 3 different possible functional forms of the 2 systems and 2 possible ways the systems can interact (simultaneously or serially). We compare these dual process models with 2 single process models using risky decision making data from Guo, Trueblood, and Diederich (2017). Using this data, we find that 1 of the dual process models significantly outperforms the other models in accounting for both choices and response times. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  19. Tectonic predictions with mantle convection models

    Coltice, Nicolas; Shephard, Grace E.

    2018-04-01

    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

  20. Personnel Recovery: Using Game Theory to Model Strategic Decision Making in the Contemporary Operating Environment

    Ecklund, Marshall V

    2005-01-01

    .... Thus, the central research question is as follows: Given a report of the physical location of an evader, is the military using the most rational decision-making model to offset the predictable nature of traditional recovery activities...

  1. Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

    Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

    2014-01-01

    In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for

  2. Emergent collective decision-making: Control, model and behavior

    Shen, Tian

    In this dissertation we study emergent collective decision-making in social groups with time-varying interactions and heterogeneously informed individuals. First we analyze a nonlinear dynamical systems model motivated by animal collective motion with heterogeneously informed subpopulations, to examine the role of uninformed individuals. We find through formal analysis that adding uninformed individuals in a group increases the likelihood of a collective decision. Secondly, we propose a model for human shared decision-making with continuous-time feedback and where individuals have little information about the true preferences of other group members. We study model equilibria using bifurcation analysis to understand how the model predicts decisions based on the critical threshold parameters that represent an individual's tradeoff between social and environmental influences. Thirdly, we analyze continuous-time data of pairs of human subjects performing an experimental shared tracking task using our second proposed model in order to understand transient behavior and the decision-making process. We fit the model to data and show that it reproduces a wide range of human behaviors surprisingly well, suggesting that the model may have captured the mechanisms of observed behaviors. Finally, we study human behavior from a game-theoretic perspective by modeling the aforementioned tracking task as a repeated game with incomplete information. We show that the majority of the players are able to converge to playing Nash equilibrium strategies. We then suggest with simulations that the mean field evolution of strategies in the population resemble replicator dynamics, indicating that the individual strategies may be myopic. Decisions form the basis of control and problems involving deciding collectively between alternatives are ubiquitous in nature and in engineering. Understanding how multi-agent systems make decisions among alternatives also provides insight for designing

  3. A burnout prediction model based around char morphology

    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.

  4. Inferential ecosystem models, from network data to prediction

    James S. Clark; Pankaj Agarwal; David M. Bell; Paul G. Flikkema; Alan Gelfand; Xuanlong Nguyen; Eric Ward; Jun Yang

    2011-01-01

    Recent developments suggest that predictive modeling could begin to play a larger role not only for data analysis, but also for data collection. We address the example of efficient wireless sensor networks, where inferential ecosystem models can be used to weigh the value of an observation against the cost of data collection. Transmission costs make observations ‘‘...

  5. Iowa calibration of MEPDG performance prediction models.

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  6. Model complexity control for hydrologic prediction

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  7. Dissolving decision making? : Models and their roles in decision-making processes and policy at large

    Zeiss, Ragna; van Egmond, S.

    2014-01-01

    This article studies the roles three science-based models play in Dutch policy and decision making processes. Key is the interaction between model construction and environment. Their political and scientific environments form contexts that shape the roles of models in policy decision making.

  8. Rodent models of adaptive decision making.

    Izquierdo, Alicia; Belcher, Annabelle M

    2012-01-01

    Adaptive decision making affords the animal the ability to respond quickly to changes in a dynamic environment: one in which attentional demands, cost or effort to procure the reward, and reward contingencies change frequently. The more flexible the organism is in adapting choice behavior, the more command and success the organism has in navigating its environment. Maladaptive decision making is at the heart of much neuropsychiatric disease, including addiction. Thus, a better understanding of the mechanisms that underlie normal, adaptive decision making helps achieve a better understanding of certain diseases that incorporate maladaptive decision making as a core feature. This chapter presents three general domains of methods that the experimenter can manipulate in animal decision-making tasks: attention, effort, and reward contingency. Here, we present detailed methods of rodent tasks frequently employed within these domains: the Attentional Set-Shift Task, Effortful T-maze Task, and Visual Discrimination Reversal Learning. These tasks all recruit regions within the frontal cortex and the striatum, and performance is heavily modulated by the neurotransmitter dopamine, making these assays highly valid measures in the study of psychostimulant addiction.

  9. Nonlinear chaotic model for predicting storm surges

    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.

  10. Staying Power of Churn Prediction Models

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  11. Predictive user modeling with actionable attributes

    Zliobaite, I.; Pechenizkiy, M.

    2013-01-01

    Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target

  12. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    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, which...

  13. Predicting of Physiological Changes through Personality Traits and Decision Making Styles

    Saeed Imani

    2016-12-01

    Full Text Available Background and Objective: One of the important concepts of social psychology is cognitive dissonance. When our practice is in conflict with our previous attitudes often change our attitude so that we will operate in concert with; this is cognitive dissonance. The aim of this study was evaluation of relation between decision making styles, personality traits and physiological components of cognitive dissonance and also offering a statistical model about them.Materials and Methods: In this correlation study, 130 students of Elmi-Karbordi University of Safadasht were invited and they were asked to complete Scott & Bruce Decision-Making Styles Questionnaire and Gray-Wilson Personality Questionnaire. Before and after distributing those questionnaires, their physiological conditions were receded. Cognitive dissonance was induced by writing about reducing amount of budget which deserved to orphans and rating the reduction of interest of lovely character that ignore his or her fans. Data analysis conducted through regression and multi vitiate covariance.Results: There were correlation between cognitive styles (Avoidant, dependent, logical and intuitive and also personality variables (Flight and Approach, active avoidance, Fight and Extinction with cognitive dissonance. The effect of cognitive (decision making styles and personality variables on physiological components was mediate indirectly through cognitive dissonance, in levels of P=0.01 and P=0.05 difference, was significant. Conclusion: Decision making styles and personality traits are related to cognitive dissonance and its physiological components, and also predict physiological components of cognitive dissonance.

  14. A burnout prediction model based around char morphology

    Tao Wu; Edward Lester; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical, Environmental and Mining Engineering

    2006-05-15

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

  15. Predictive modeling of emergency cesarean delivery.

    Carlos Campillo-Artero

    Full Text Available To increase discriminatory accuracy (DA for emergency cesarean sections (ECSs.We prospectively collected data on and studied all 6,157 births occurring in 2014 at four public hospitals located in three different autonomous communities of Spain. To identify risk factors (RFs for ECS, we used likelihood ratios and logistic regression, fitted a classification tree (CTREE, and analyzed a random forest model (RFM. We used the areas under the receiver-operating-characteristic (ROC curves (AUCs to assess their DA.The magnitude of the LR+ for all putative individual RFs and ORs in the logistic regression models was low to moderate. Except for parity, all putative RFs were positively associated with ECS, including hospital fixed-effects and night-shift delivery. The DA of all logistic models ranged from 0.74 to 0.81. The most relevant RFs (pH, induction, and previous C-section in the CTREEs showed the highest ORs in the logistic models. The DA of the RFM and its most relevant interaction terms was even higher (AUC = 0.94; 95% CI: 0.93-0.95.Putative fetal, maternal, and contextual RFs alone fail to achieve reasonable DA for ECS. It is the combination of these RFs and the interactions between them at each hospital that make it possible to improve the DA for the type of delivery and tailor interventions through prediction to improve the appropriateness of ECS indications.

  16. Exploring Best Practice Skills to Predict Uncertainties in Venture Capital Investment Decision-Making

    Blum, David Arthur

    Algae biodiesel is the sole sustainable and abundant transportation fuel source that can replace petrol diesel use; however, high competition and economic uncertainties exist, influencing independent venture capital decision making. Technology, market, management, and government action uncertainties influence competition and economic uncertainties in the venture capital industry. The purpose of this qualitative case study was to identify the best practice skills at IVC firms to predict uncertainty between early and late funding stages. The basis of the study was real options theory, a framework used to evaluate and understand the economic and competition uncertainties inherent in natural resource investment and energy derived from plant-based oils. Data were collected from interviews of 24 venture capital partners based in the United States who invest in algae and other renewable energy solutions. Data were analyzed by coding and theme development interwoven with the conceptual framework. Eight themes emerged: (a) expected returns model, (b) due diligence, (c) invest in specific sectors, (d) reduced uncertainty-late stage, (e) coopetition, (f) portfolio firm relationships, (g) differentiation strategy, and (h) modeling uncertainty and best practice. The most noteworthy finding was that predicting uncertainty at the early stage was impractical; at the expansion and late funding stages, however, predicting uncertainty was possible. The implications of these findings will affect social change by providing independent venture capitalists with best practice skills to increase successful exits, lessen uncertainty, and encourage increased funding of renewable energy firms, contributing to cleaner and healthier communities throughout the United States..

  17. Making ethical choices: a comprehensive decision-making model for Canadian psychologists.

    Hadjistavropoulos, T; Malloy, D C

    2000-05-01

    This paper proposes a theoretical augmentation of the seven-step decision-making model outlined in the Canadian Code of Ethics for Psychologists. We propose that teleological, deontological, and existential ethical perspectives should be taken into account in the decision-making process. We also consider the influence of individual, issue-specific, significant-other, situational, and external factors on ethical decision-making. This theoretical analysis demonstrates the richness and complexity of ethical decision-making.

  18. Robust predictions of the interacting boson model

    Casten, R.F.; Koeln Univ.

    1994-01-01

    While most recognized for its symmetries and algebraic structure, the IBA model has other less-well-known but equally intrinsic properties which give unavoidable, parameter-free predictions. These predictions concern central aspects of low-energy nuclear collective structure. This paper outlines these ''robust'' predictions and compares them with the data

  19. Comparison of Prediction-Error-Modelling Criteria

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    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 is a r...

  20. How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

    Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C

    2014-03-01

    Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward

  1. The prediction of epidemics through mathematical modeling.

    Schaus, Catherine

    2014-01-01

    Mathematical models may be resorted to in an endeavor to predict the development of epidemics. The SIR model is one of the applications. Still too approximate, the use of statistics awaits more data in order to come closer to reality.

  2. Calibration of PMIS pavement performance prediction models.

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  3. The Limitations of Applying Rational Decision-Making Models

    decision-making models as applied to child spacing and more. specificaDy to the use .... also assumes that the individual operates as a rational decision- making organism in ..... work involves: Motivation; Counselling; Distribution ofIEC mate-.

  4. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

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

    2017-12-01

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

  5. MULTIVARIATE MODEL FOR CORPORATE BANKRUPTCY PREDICTION IN ROMANIA

    Daniel BRÎNDESCU – OLARIU

    2016-01-01

    The current paper proposes a methodology for bankruptcy prediction applicable for Romanian companies. Low bankruptcy frequencies registered in the past have limited the importance of bankruptcy prediction in Romania. The changes in the economic environment brought by the economic crisis, as well as by the entrance in the European Union, make the availability of performing bankruptcy assessment tools more important than ever before. The proposed methodology is centred on a multivariate model, ...

  6. A Model of Dynamic Strategy-Making

    Andersen, Torben Juul; Hallin, Carina Antonia; Li, Xin

    The organizational capacity to cope with unexpected changes remains a fundamental challenge in strategy as global competition and technological innovation increase environmental uncertainty. Conventional strategy-making is often conceived as a sequential linear process where we see it as a non......-linear interaction between top-down and bottom-up mechanisms dealing with multiple actions taken throughout the organization over time. It is driven by intension but with a flexible balance between centralized (planned) and decentralized (spontaneous) activities. We adopt the principles of complementary Yin......-Yang elements and Zhong Yong balance to explain the time bound interaction between these opposing yet complementary strategy-making mechanisms where tradeoffs and synergies are balanced across hierarchical levels....

  7. DECISION MAKING MODELING OF CONCRETE REQUIREMENTS

    Suhartono Irawan

    2001-01-01

    Full Text Available This paper presents the results of an experimental evaluation between predicted and practice concrete strength. The scope of the evaluation is the optimisation of the cement content for different concrete grades as a result of bringing the target mean value of tests cubes closer to the required characteristic strength value by reducing the standard deviation. Abstract in Bahasa Indonesia : concrete+mix+design%2C+acceptance+control%2C+optimisation%2C+cement+content.

  8. Case studies in archaeological predictive modelling

    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

  9. An evolutionary behavioral model for decision making

    Romero Lopez, Dr Oscar Javier

    2011-01-01

    For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process i...

  10. Management decision making for fisher populations informed by occupancy modeling

    Fuller, Angela K.; Linden, Daniel W.; Royle, J. Andrew

    2016-01-01

    Harvest data are often used by wildlife managers when setting harvest regulations for species because the data are regularly collected and do not require implementation of logistically and financially challenging studies to obtain the data. However, when harvest data are not available because an area had not previously supported a harvest season, alternative approaches are required to help inform management decision making. When distribution or density data are required across large areas, occupancy modeling is a useful approach, and under certain conditions, can be used as a surrogate for density. We collaborated with the New York State Department of Environmental Conservation (NYSDEC) to conduct a camera trapping study across a 70,096-km2 region of southern New York in areas that were currently open to fisher (Pekania [Martes] pennanti) harvest and those that had been closed to harvest for approximately 65 years. We used detection–nondetection data at 826 sites to model occupancy as a function of site-level landscape characteristics while accounting for sampling variation. Fisher occupancy was influenced positively by the proportion of conifer and mixed-wood forest within a 15-km2 grid cell and negatively associated with road density and the proportion of agriculture. Model-averaged predictions indicated high occupancy probabilities (>0.90) when road densities were low (0.50). Predicted occupancy ranged 0.41–0.67 in wildlife management units (WMUs) currently open to trapping, which could be used to guide a minimum occupancy threshold for opening new areas to trapping seasons. There were 5 WMUs that had been closed to trapping but had an average predicted occupancy of 0.52 (0.07 SE), and above the threshold of 0.41. These areas are currently under consideration by NYSDEC for opening a conservative harvest season. We demonstrate the use of occupancy modeling as an aid to management decision making when harvest-related data are unavailable and when budgetary

  11. The four principles: can they be measured and do they predict ethical decision making?

    Page, Katie

    2012-05-20

    The four principles of Beauchamp and Childress--autonomy, non-maleficence, beneficence and justice--have been extremely influential in the field of medical ethics, and are fundamental for understanding the current approach to ethical assessment in health care. This study tests whether these principles can be quantitatively measured on an individual level, and then subsequently if they are used in the decision making process when individuals are faced with ethical dilemmas. The Analytic Hierarchy Process was used as a tool for the measurement of the principles. Four scenarios, which involved conflicts between the medical ethical principles, were presented to participants who then made judgments about the ethicality of the action in the scenario, and their intentions to act in the same manner if they were in the situation. Individual preferences for these medical ethical principles can be measured using the Analytic Hierarchy Process. This technique provides a useful tool in which to highlight individual medical ethical values. On average, individuals have a significant preference for non-maleficence over the other principles, however, and perhaps counter-intuitively, this preference does not seem to relate to applied ethical judgements in specific ethical dilemmas. People state they value these medical ethical principles but they do not actually seem to use them directly in the decision making process. The reasons for this are explained through the lack of a behavioural model to account for the relevant situational factors not captured by the principles. The limitations of the principles in predicting ethical decision making are discussed.

  12. Making the Most of Modeling Tasks

    Wernet, Jamie L.; Lawrence, Kevin A.; Gilbertson, Nicholas J.

    2015-01-01

    While there is disagreement among mathematics educators about some aspects of its meaning, mathematical modeling generally involves taking a real-world scenario and translating it into the mathematical world (Niss, Blum, and Galbraith 2007). The complete modeling process involves describing situations posed in problems with mathematical concepts,…

  13. Sectioning Clay Models Makes Anatomy & Development Tangible

    Howell, Carina Endres; Howell, James Endres

    2010-01-01

    Clay models have proved to be useful teaching aids for many topics in biology that depend on three-dimensional reasoning. Students studying embryonic development struggle to mentally reconstruct the three-dimensional structure of embryos and larvae by observing prepared slides of cross-sectional slices. Students who build clay models of embryos…

  14. Incorporating uncertainty in predictive species distribution modelling.

    Beale, Colin M; Lennon, Jack J

    2012-01-19

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

  15. Model Predictive Control for Smart Energy Systems

    Halvgaard, Rasmus

    pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...

  16. FUZZY DECISION MAKING MODEL FOR BYZANTINE AGREEMENT

    S. MURUGAN

    2014-04-01

    Full Text Available Byzantine fault tolerance is of high importance in the distributed computing environment where malicious attacks and software errors are common. A Byzantine process sends arbitrary messages to every other process. An effective fuzzy decision making approach is proposed to eliminate the Byzantine behaviour of the services in the distributed environment. It is proposed to derive a fuzzy decision set in which the alternatives are ranked with grade of membership and based on that an appropriate decision can be arrived on the messages sent by the different services. A balanced decision is to be taken from the messages received across the services. To accomplish this, Hurwicz criterion is used to balance the optimistic and pessimistic views of the decision makers on different services. Grades of membership for the services are assessed using the non-functional Quality of Service parameters and have been estimated using fuzzy entropy measure which logically ranks the participant services. This approach for decision making is tested by varying the number of processes, varying the number of faulty services, varying the message values sent to different services and considering the variation in the views of the decision makers about the services. The experimental result shows that the decision reached is an enhanced one and in case of conflict, the proposed approach provides a concrete result, whereas decision taken using the Lamport’s algorithm is an arbitrary one.

  17. Evaluating the Predictive Value of Growth Prediction Models

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

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

  18. Models in environmental regulatory decision making

    Committee on Models in the Regulatory Decision Process, National Research Council

    2007-01-01

    .... Models help EPA explain environmental phenomena in settings where direct observations are limited or unavailable, and anticipate the effects of agency policies on the environment, human health and the economy...

  19. Stochastic models for predicting environmental impact in aquatic ecosystems

    Stewart-Oaten, A.

    1986-01-01

    The purpose of stochastic predictions are discussed in relation to the environmental impacts of nuclear power plants on aquatic ecosystems. One purpose is to aid in making rational decisions about whether a power plant should be built, where, and how it should be designed. The other purpose is to check on the models themselves in the light of what eventually happens. The author discusses the role or statistical decision theory in the decision-making problem. Various types of stochastic models and their problems are presented. In addition some suggestions are made for generating usable stochastic models, and checking and improving on them. 12 references

  20. Model predictive control classical, robust and stochastic

    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...

  1. Modeling, robust and distributed model predictive control for freeway networks

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  2. Urban drainage models - making uncertainty analysis simple

    Vezzaro, Luca; Mikkelsen, Peter Steen; Deletic, Ana

    2012-01-01

    in each measured/observed datapoint; an issue which is commonly overlook in the uncertainty analysis of urban drainage models. This comparison allows the user to intuitively estimate the optimum number of simulations required to conduct uncertainty analyses. The output of the method includes parameter......There is increasing awareness about uncertainties in modelling of urban drainage systems and, as such, many new methods for uncertainty analyses have been developed. Despite this, all available methods have limitations which restrict their widespread application among practitioners. Here...

  3. Data driven mathematical models for policy making

    Nannyonga, Betty

    2011-01-01

    This thesis consists of two papers. 1. Betty Nannyonga, D.J.T. Sumpter, J.Y.T. Mugisha and L.S. Luboobi: The Dynamics,causes and possible prevention of Hepaititis E outbreaks. 2. Betty Nannyonga, D.J.T. Sumpter, andStam Nicolis: A dynamical systems approach tosocial and economic development. The first paper deals with a deterministic approach of modelling a Hepatitis E outbreak whenmalaria is endemic in a population. We design three models based on the epidemiology ofHepatitis E, malaria, and...

  4. Models in environmental regulatory decision making

    Committee on Models in the Regulatory Decision Process; National Research Council; Division on Earth and Life Studies; National Research Council

    2007-01-01

    .... The centerpiece of the book's recommended vision is a life-cycle approach to model evaluation which includes peer review, corroboration of results, and other activities. This will enhance the agency's ability to respond to requirements from a 2001 law on information quality and improve policy development and implementation.

  5. Unreachable Setpoints in Model Predictive Control

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

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... 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...

  6. Bayesian Predictive Models for Rayleigh Wind Speed

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior...... and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented....

  7. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

    Lin, Frank P Y; Pokorny, Adrian; Teng, Christina; Dear, Rachel; Epstein, Richard J

    2016-12-01

    Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations. The predictions so derived were duly compared with those based on published (ESMO and NCCN) cancer guidelines. Machine learning more accurately predicted adjuvant chemotherapy MDT decisions than did simple application of guidelines. No differences were found between MDT- vs. ESMO/NCCN- based decisions to prescribe either adjuvant endocrine (97%, p = 0.44/0.74) or biologic/targeted therapies (98%, p = 0.82/0.59). In contrast, significant discrepancies were evident between MDT- and guideline-based decisions to prescribe chemotherapy (87%, p machine learning models. A machine learning approach based on clinicopathologic characteristics can predict MDT decisions about adjuvant breast cancer drug therapies. The discrepancy between MDT- and guideline-based decisions regarding adjuvant chemotherapy implies that certain non-clincopathologic criteria, such as patient preference and resource availability, are factored into clinical decision-making by local experts but not captured by guidelines.

  8. Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

    David Ebert

    2006-01-01

    One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...

  9. Fingerprint verification prediction model in hand dermatitis.

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  10. Multi-model analysis in hydrological prediction

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

    2017-12-01

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

  11. Prostate Cancer Risk Prediction Models

    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.

  12. Colorectal Cancer Risk Prediction Models

    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.

  13. Esophageal Cancer Risk Prediction Models

    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.

  14. Bladder Cancer Risk Prediction Models

    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.

  15. Lung Cancer Risk Prediction Models

    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.

  16. Breast Cancer Risk Prediction Models

    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.

  17. Pancreatic Cancer Risk Prediction Models

    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.

  18. Ovarian Cancer Risk Prediction Models

    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.

  19. Liver Cancer Risk Prediction Models

    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.

  20. Testicular Cancer Risk Prediction Models

    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.

  1. Cervical Cancer Risk Prediction Models

    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.

  2. Modeling and Prediction Using Stochastic Differential Equations

    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...... 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...

  3. Predictive Model of Systemic Toxicity (SOT)

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

  4. Economic modelling for life extension decision making

    Farber, M.A.; Harrison, D.L.; Carlson, D.D.

    1987-01-01

    This paper presents a methodology for the economic and financial analysis of nuclear plant life extension under uncertainty and demonstrates its use in a case analysis. While the economic and financial evaluation of life extension does not require new analytical tools, such studies should be based on the following three premises. First, the methodology should examine effects at the level of the company or utility system, because the most important economic implications of life extension relate to the altered generation system expansion plan. Second, it should focus on the implications of uncertainty in order to understand the factors that most affect life extension benefits and identify risk management efforts. Third, the methodology should address multiple objectives, at a minimum, both economic and financial objectives. An analysis of the role of life extension for Virginia Power's generating system was performed using the MIDAS model, developed by the Electric Power Research Institute. MIDAS is particularly well suited to this type of study because of its decision analysis framework. The model incorporates modules for load analysis, capacity expansion, production costing, financial analysis, and rates. The decision tree structure facilitates the multiple-scenario analysis of uncertainty. The model's output includes many economic and financial measures, including capital expenditures, fuel and purchases power costs, revenue requirements, average rates, external financing requirements, and coverage ratio. Based on findings for Virginia Power's Surry 1 plant, nuclear plant life extension has economic benefits for a utility's customers and financial benefits for the utility's investors. These benefits depend on a number of economic, technical and regulatory factors. The economic analysis presented in this paper identifies many of the key factors and issues relevant to life extension planning

  5. Spent fuel: prediction model development

    Almassy, M.Y.; Bosi, D.M.; Cantley, D.A.

    1979-07-01

    The need for spent fuel disposal performance modeling stems from a requirement to assess the risks involved with deep geologic disposal of spent fuel, and to support licensing and public acceptance of spent fuel repositories. Through the balanced program of analysis, diagnostic testing, and disposal demonstration tests, highlighted in this presentation, the goal of defining risks and of quantifying fuel performance during long-term disposal can be attained

  6. Navy Recruit Attrition Prediction Modeling

    2014-09-01

    have high correlation with attrition, such as age, job characteristics, command climate, marital status, behavior issues prior to recruitment, and the...the additive model. glm(formula = Outcome ~ Age + Gender + Marital + AFQTCat + Pay + Ed + Dep, family = binomial, data = ltraining) Deviance ...0.1 ‘ ‘ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance : 105441 on 85221 degrees of freedom Residual deviance

  7. Predicting and Modeling RNA Architecture

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  8. Predictive Models and Computational Toxicology (II IBAMTOX)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  9. Finding furfural hydrogenation catalysts via predictive modelling

    Strassberger, Z.; Mooijman, M.; Ruijter, E.; Alberts, A.H.; Maldonado, A.G.; Orru, R.V.A.; Rothenberg, G.

    2010-01-01

    We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes

  10. FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...

    FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL STRESSES IN ... the transverse residual stress in the x-direction (σx) had a maximum value of 375MPa ... the finite element method are in fair agreement with the experimental results.

  11. Evaluation of CASP8 model quality predictions

    Cozzetto, Domenico; Kryshtafovych, Andriy; Tramontano, Anna

    2009-01-01

    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

  12. On Extrapolating Past the Range of Observed Data When Making Statistical Predictions in Ecology.

    Paul B Conn

    Full Text Available Ecologists are increasingly using statistical models to predict animal abundance and occurrence in unsampled locations. The reliability of such predictions depends on a number of factors, including sample size, how far prediction locations are from the observed data, and similarity of predictive covariates in locations where data are gathered to locations where predictions are desired. In this paper, we propose extending Cook's notion of an independent variable hull (IVH, developed originally for application with linear regression models, to generalized regression models as a way to help assess the potential reliability of predictions in unsampled areas. Predictions occurring inside the generalized independent variable hull (gIVH can be regarded as interpolations, while predictions occurring outside the gIVH can be regarded as extrapolations worthy of additional investigation or skepticism. We conduct a simulation study to demonstrate the usefulness of this metric for limiting the scope of spatial inference when conducting model-based abundance estimation from survey counts. In this case, limiting inference to the gIVH substantially reduces bias, especially when survey designs are spatially imbalanced. We also demonstrate the utility of the gIVH in diagnosing problematic extrapolations when estimating the relative abundance of ribbon seals in the Bering Sea as a function of predictive covariates. We suggest that ecologists routinely use diagnostics such as the gIVH to help gauge the reliability of predictions from statistical models (such as generalized linear, generalized additive, and spatio-temporal regression models.

  13. Human-centric decision-making models for social sciences

    Pedrycz, Witold

    2014-01-01

    The volume delivers a wealth of effective methods to deal with various types of uncertainty inherently existing in human-centric decision problems. It elaborates on  comprehensive decision frameworks to handle different decision scenarios, which help use effectively the explicit and tacit knowledge and intuition, model perceptions and preferences in a more human-oriented style. The book presents original approaches and delivers new results on fundamentals and applications related to human-centered decision making approaches to business, economics and social systems. Individual chapters cover multi-criteria (multiattribute) decision making, decision making with prospect theory, decision making with incomplete probabilistic information, granular models of decision making and decision making realized with the use of non-additive measures. New emerging decision theories being presented as along with a wide spectrum of ongoing research make the book valuable to all interested in the field of advanced decision-mak...

  14. The Cognitive Processes underlying Affective Decision-making Predicting Adolescent Smoking Behaviors in a Longitudinal Study

    Lin eXiao

    2013-10-01

    Full Text Available This study investigates the relationship between three different cognitive processes underlying the Iowa Gambling Task (IGT and adolescent smoking behaviors in a longitudinal study. We conducted a longitudinal study of 181 Chinese adolescents in Chengdu City, China. The participants were followed from 10th grade to 11th grade. When they were in the 10th grade (Time 1, we tested these adolescents’ decision-making using the Iowa Gambling Task and working memory capacity using the Self-ordered Pointing Test (SOPT. Self-report questionnaires were used to assess school academic performance and smoking behaviors. The same questionnaires were completed again at the one-year follow-up (Time 2. The Expectancy-Valence (EV Model was applied to distill the IGT performance into three different underlying psychological components: (i a motivational component which indicates the subjective weight the adolescents assign to gains versus losses; (ii a learning-rate component which indicates the sensitivity to recent outcomes versus past experiences; and (iii a response component which indicates how consistent the adolescents are between learning and responding. The subjective weight to gains vs. losses at Time 1 significantly predicted current smokers and current smoking levels at Time 2, controlling for demographic variables and baseline smoking behaviors. Therefore, by decomposing the IGT into three different psychological components, we found that the motivational process of weight gain vs. losses may serve as a neuropsychological marker to predict adolescent smoking behaviors in a general youth population.

  15. Mental models accurately predict emotion transitions.

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  16. Mental models accurately predict emotion transitions

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  17. Return Predictability, Model Uncertainty, and Robust Investment

    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...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  18. Spatial Economics Model Predicting Transport Volume

    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.

  19. Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction.

    Soleimani, Hossein; Hensman, James; Saria, Suchi

    2017-08-21

    Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations. These approaches, however, make strong parametric assumptions and do not easily scale to multivariate signals with many observations. Our proposed approach consists of several key innovations. First, we develop a flexible and scalable joint model based upon sparse multiple-output Gaussian processes. Unlike state-of-the-art joint models, the proposed model can explain highly challenging structure including non-Gaussian noise while scaling to large data. Second, we derive an optimal policy for predicting events using the distribution of the event occurrence estimated by the joint model. The derived policy trades-off the cost of a delayed detection versus incorrect assessments and abstains from making decisions when the estimated event probability does not satisfy the derived confidence criteria. Experiments on a large dataset show that the proposed framework significantly outperforms state-of-the-art techniques in event prediction.

  20. Interpreting Disruption Prediction Models to Improve Plasma Control

    Parsons, Matthew

    2017-10-01

    In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.

  1. Accuracy assessment of landslide prediction models

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  2. The four principles: Can they be measured and do they predict ethical decision making?

    Page Katie

    2012-05-01

    Full Text Available Abstract Background The four principles of Beauchamp and Childress - autonomy, non-maleficence, beneficence and justice - have been extremely influential in the field of medical ethics, and are fundamental for understanding the current approach to ethical assessment in health care. This study tests whether these principles can be quantitatively measured on an individual level, and then subsequently if they are used in the decision making process when individuals are faced with ethical dilemmas. Methods The Analytic Hierarchy Process was used as a tool for the measurement of the principles. Four scenarios, which involved conflicts between the medical ethical principles, were presented to participants who then made judgments about the ethicality of the action in the scenario, and their intentions to act in the same manner if they were in the situation. Results Individual preferences for these medical ethical principles can be measured using the Analytic Hierarchy Process. This technique provides a useful tool in which to highlight individual medical ethical values. On average, individuals have a significant preference for non-maleficence over the other principles, however, and perhaps counter-intuitively, this preference does not seem to relate to applied ethical judgements in specific ethical dilemmas. Conclusions People state they value these medical ethical principles but they do not actually seem to use them directly in the decision making process. The reasons for this are explained through the lack of a behavioural model to account for the relevant situational factors not captured by the principles. The limitations of the principles in predicting ethical decision making are discussed.

  3. The four principles: Can they be measured and do they predict ethical decision making?

    2012-01-01

    Background The four principles of Beauchamp and Childress - autonomy, non-maleficence, beneficence and justice - have been extremely influential in the field of medical ethics, and are fundamental for understanding the current approach to ethical assessment in health care. This study tests whether these principles can be quantitatively measured on an individual level, and then subsequently if they are used in the decision making process when individuals are faced with ethical dilemmas. Methods The Analytic Hierarchy Process was used as a tool for the measurement of the principles. Four scenarios, which involved conflicts between the medical ethical principles, were presented to participants who then made judgments about the ethicality of the action in the scenario, and their intentions to act in the same manner if they were in the situation. Results Individual preferences for these medical ethical principles can be measured using the Analytic Hierarchy Process. This technique provides a useful tool in which to highlight individual medical ethical values. On average, individuals have a significant preference for non-maleficence over the other principles, however, and perhaps counter-intuitively, this preference does not seem to relate to applied ethical judgements in specific ethical dilemmas. Conclusions People state they value these medical ethical principles but they do not actually seem to use them directly in the decision making process. The reasons for this are explained through the lack of a behavioural model to account for the relevant situational factors not captured by the principles. The limitations of the principles in predicting ethical decision making are discussed. PMID:22606995

  4. Predictive validation of an influenza spread model.

    Ayaz Hyder

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

  5. Predictive Validation of an Influenza Spread Model

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

    2013-01-01

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

  6. Finding Furfural Hydrogenation Catalysts via Predictive Modelling.

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

    2010-09-10

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

  7. Corporate prediction models, ratios or regression analysis?

    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

  8. Predicting Protein Secondary Structure with Markov Models

    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...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  9. Energy based prediction models for building acoustics

    Brunskog, Jonas

    2012-01-01

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

  10. Comparative Study of Bancruptcy Prediction Models

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  11. Mobility Modelling through Trajectory Decomposition and Prediction

    Faghihi, Farbod

    2017-01-01

    The ubiquity of mobile devices with positioning sensors make it possible to derive user's location at any time. However, constantly sensing the position in order to track the user's movement is not feasible, either due to the unavailability of sensors, or computational and storage burdens. In this thesis, we present and evaluate a novel approach for efficiently tracking user's movement trajectories using decomposition and prediction of trajectories. We facilitate tracking by taking advantage ...

  12. Simulation Models of Human Decision-Making Processes

    Nina RIZUN

    2014-10-01

    Full Text Available The main purpose of the paper is presentation of the new concept of human decision-making process modeling via using the analogy with Automatic Control Theory. From the author's point of view this concept allows to develop and improve the theory of decision-making in terms of the study and classification of specificity of the human intellectual processes in different conditions. It was proved that the main distinguishing feature between the Heuristic / Intuitive and Rational Decision-Making Models is the presence of so-called phenomenon of "enrichment" of the input information with human propensity, hobbies, tendencies, expectations, axioms and judgments, presumptions or bias and their justification. In order to obtain additional knowledge about the basic intellectual processes as well as the possibility of modeling the decision results in various parameters characterizing the decision-maker, the complex of the simulation models was developed. These models are based on the assumptions that:  basic intellectual processes of the Rational Decision-Making Model can be adequately simulated and identified by the transient processes of the proportional-integral-derivative controller; basic intellectual processes of the Bounded Rationality and Intuitive Models can be adequately simulated and identified by the transient processes of the nonlinear elements.The taxonomy of the most typical automatic control theory elements and their compliance with certain decision-making models with a point of view of decision-making process specificity and decision-maker behavior during a certain time of professional activity was obtained.

  13. Optimal model-free prediction from multivariate time series

    Runge, Jakob; Donner, Reik V.; Kurths, Jürgen

    2015-05-01

    Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.

  14. Evaluation of CASP8 model quality predictions

    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.

  15. Finding Furfural Hydrogenation Catalysts via Predictive Modelling

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

    2010-01-01

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

  16. A decision-making model for engineering designers

    Ahmed, S.; Hansen, Claus Thorp

    2002-01-01

    This paper describes research that combines the generic decision-making model of Hansen, together with design strategies employed by experienced engineering designers. The relationship between the six decision-making sub-activities and the eight design strategies are examined. By combining...

  17. Wind farm production prediction - The Zephyr model

    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)

  18. Embryo quality predictive models based on cumulus cells gene expression

    Devjak R

    2016-06-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  19. Model predictive controller design of hydrocracker reactors

    GÖKÇE, Dila

    2011-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  20. Modelling the Role of Cognitive Metaphors in Joint Decision Making

    van Ments, L.; Thilakarathne, D.J.; Treur, J.

    2016-01-01

    In this paper, a social agent model is presented for the influence of cognitive metaphors on joint decision making processes. The social agent model is based on mechanisms known from cognitive and social neuroscience and cognitive metaphor theory. The model was illustrated in particular for two

  1. Optimization for decision making linear and quadratic models

    Murty, Katta G

    2010-01-01

    While maintaining the rigorous linear programming instruction required, Murty's new book is unique in its focus on developing modeling skills to support valid decision-making for complex real world problems, and includes solutions to brand new algorithms.

  2. Models of sequential decision making in consumer lending

    Kanshukan Rajaratnam; Peter A. Beling; George A. Overstreet

    2016-01-01

    Abstract In this paper, we introduce models of sequential decision making in consumer lending. From the definition of adverse selection in static lending models, we show that homogenous borrowers take-up offers at different instances of time when faced with a sequence of loan offers. We postulate that bounded rationality and diverse decision heuristics used by consumers drive the decisions they make about credit offers. Under that postulate, we show how observation of early decisions in a seq...

  3. Dual processing model of medical decision-making

    Djulbegovic, Benjamin; Hozo, Iztok; Beckstead, Jason; Tsalatsanis, Athanasios; Pauker, Stephen G

    2012-01-01

    Abstract Background Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administe...

  4. Multi-Model Ensemble Wake Vortex Prediction

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  5. Risk terrain modeling predicts child maltreatment.

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

    2016-12-01

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

  6. Evolution of quantum-like modeling in decision making processes

    Khrennikova, Polina

    2012-12-01

    The application of the mathematical formalism of quantum mechanics to model behavioral patterns in social science and economics is a novel and constantly emerging field. The aim of the so called 'quantum like' models is to model the decision making processes in a macroscopic setting, capturing the particular 'context' in which the decisions are taken. Several subsequent empirical findings proved that when making a decision people tend to violate the axioms of expected utility theory and Savage's Sure Thing principle, thus violating the law of total probability. A quantum probability formula was devised to describe more accurately the decision making processes. A next step in the development of QL-modeling in decision making was the application of Schrödinger equation to describe the evolution of people's mental states. A shortcoming of Schrödinger equation is its inability to capture dynamics of an open system; the brain of the decision maker can be regarded as such, actively interacting with the external environment. Recently the master equation, by which quantum physics describes the process of decoherence as the result of interaction of the mental state with the environmental 'bath', was introduced for modeling the human decision making. The external environment and memory can be referred to as a complex 'context' influencing the final decision outcomes. The master equation can be considered as a pioneering and promising apparatus for modeling the dynamics of decision making in different contexts.

  7. Evolution of quantum-like modeling in decision making processes

    Khrennikova, Polina [School of Management, University of Leicester, University Road Leicester LE1 7RH (United Kingdom)

    2012-12-18

    The application of the mathematical formalism of quantum mechanics to model behavioral patterns in social science and economics is a novel and constantly emerging field. The aim of the so called 'quantum like' models is to model the decision making processes in a macroscopic setting, capturing the particular 'context' in which the decisions are taken. Several subsequent empirical findings proved that when making a decision people tend to violate the axioms of expected utility theory and Savage's Sure Thing principle, thus violating the law of total probability. A quantum probability formula was devised to describe more accurately the decision making processes. A next step in the development of QL-modeling in decision making was the application of Schroedinger equation to describe the evolution of people's mental states. A shortcoming of Schroedinger equation is its inability to capture dynamics of an open system; the brain of the decision maker can be regarded as such, actively interacting with the external environment. Recently the master equation, by which quantum physics describes the process of decoherence as the result of interaction of the mental state with the environmental 'bath', was introduced for modeling the human decision making. The external environment and memory can be referred to as a complex 'context' influencing the final decision outcomes. The master equation can be considered as a pioneering and promising apparatus for modeling the dynamics of decision making in different contexts.

  8. Evolution of quantum-like modeling in decision making processes

    Khrennikova, Polina

    2012-01-01

    The application of the mathematical formalism of quantum mechanics to model behavioral patterns in social science and economics is a novel and constantly emerging field. The aim of the so called 'quantum like' models is to model the decision making processes in a macroscopic setting, capturing the particular 'context' in which the decisions are taken. Several subsequent empirical findings proved that when making a decision people tend to violate the axioms of expected utility theory and Savage's Sure Thing principle, thus violating the law of total probability. A quantum probability formula was devised to describe more accurately the decision making processes. A next step in the development of QL-modeling in decision making was the application of Schrödinger equation to describe the evolution of people's mental states. A shortcoming of Schrödinger equation is its inability to capture dynamics of an open system; the brain of the decision maker can be regarded as such, actively interacting with the external environment. Recently the master equation, by which quantum physics describes the process of decoherence as the result of interaction of the mental state with the environmental 'bath', was introduced for modeling the human decision making. The external environment and memory can be referred to as a complex 'context' influencing the final decision outcomes. The master equation can be considered as a pioneering and promising apparatus for modeling the dynamics of decision making in different contexts.

  9. The origin of anomalous transport in porous media - is it possible to make a priori predictions?

    Bijeljic, Branko; Blunt, Martin

    2013-04-01

    at approximately the average flow speed; in the carbonate with the widest velocity distribution the stagnant concentration peak is persistent, while the emergence of a smaller secondary mobile peak is observed, leading to a highly anomalous behavior. This defines different generic nature of non-Fickian transport in the three media and quantifies the effect of pore structure on transport. Moreover, the propagators obtained by the model are in a very good agreement with the propagators measured on beadpack, Bentheimer sandstone and Portland carbonate cores in nuclear magnetic resonance experiments. These findings demonstrate that it is possible to make a priori predictions of anomalous transport in porous media. The importance of these findings for transport in complex carbonate rock micro-CT images is discussed, classifying them in terms of degree of anomalous transport that can have an impact at the field scale. Extensions to reactive transport will be discussed.

  10. Alcator C-Mod predictive modeling

    Pankin, Alexei; Bateman, Glenn; Kritz, Arnold; Greenwald, Martin; Snipes, Joseph; Fredian, Thomas

    2001-01-01

    Predictive simulations for the Alcator C-mod tokamak [I. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] are carried out using the BALDUR integrated modeling code [C. E. Singer et al., Comput. Phys. Commun. 49, 275 (1988)]. The results are obtained for temperature and density profiles using the Multi-Mode transport model [G. Bateman et al., Phys. Plasmas 5, 1793 (1998)] as well as the mixed-Bohm/gyro-Bohm transport model [M. Erba et al., Plasma Phys. Controlled Fusion 39, 261 (1997)]. The simulated discharges are characterized by very high plasma density in both low and high modes of confinement. The predicted profiles for each of the transport models match the experimental data about equally well in spite of the fact that the two models have different dimensionless scalings. Average relative rms deviations are less than 8% for the electron density profiles and 16% for the electron and ion temperature profiles

  11. Using plural modeling for predicting decisions made by adaptive adversaries

    Buede, Dennis M.; Mahoney, Suzanne; Ezell, Barry; Lathrop, John

    2012-01-01

    Incorporating an appropriate representation of the likelihood of terrorist decision outcomes into risk assessments associated with weapons of mass destruction attacks has been a significant problem for countries around the world. Developing these likelihoods gets at the heart of the most difficult predictive problems: human decision making, adaptive adversaries, and adversaries about which very little is known. A plural modeling approach is proposed that incorporates estimates of all critical uncertainties: who is the adversary and what skills and resources are available to him, what information is known to the adversary and what perceptions of the important facts are held by this group or individual, what does the adversary know about the countermeasure actions taken by the government in question, what are the adversary's objectives and the priorities of those objectives, what would trigger the adversary to start an attack and what kind of success does the adversary desire, how realistic is the adversary in estimating the success of an attack, how does the adversary make a decision and what type of model best predicts this decision-making process. A computational framework is defined to aggregate the predictions from a suite of models, based on this broad array of uncertainties. A validation approach is described that deals with a significant scarcity of data.

  12. Bayesian calibration of power plant models for accurate performance prediction

    Boksteen, Sowande Z.; Buijtenen, Jos P. van; Pecnik, Rene; Vecht, Dick van der

    2014-01-01

    Highlights: • Bayesian calibration is applied to power plant performance prediction. • Measurements from a plant in operation are used for model calibration. • A gas turbine performance model and steam cycle model are calibrated. • An integrated plant model is derived. • Part load efficiency is accurately predicted as a function of ambient conditions. - Abstract: Gas turbine combined cycles are expected to play an increasingly important role in the balancing of supply and demand in future energy markets. Thermodynamic modeling of these energy systems is frequently applied to assist in decision making processes related to the management of plant operation and maintenance. In most cases, model inputs, parameters and outputs are treated as deterministic quantities and plant operators make decisions with limited or no regard of uncertainties. As the steady integration of wind and solar energy into the energy market induces extra uncertainties, part load operation and reliability are becoming increasingly important. In the current study, methods are proposed to not only quantify various types of uncertainties in measurements and plant model parameters using measured data, but to also assess their effect on various aspects of performance prediction. The authors aim to account for model parameter and measurement uncertainty, and for systematic discrepancy of models with respect to reality. For this purpose, the Bayesian calibration framework of Kennedy and O’Hagan is used, which is especially suitable for high-dimensional industrial problems. The article derives a calibrated model of the plant efficiency as a function of ambient conditions and operational parameters, which is also accurate in part load. The article shows that complete statistical modeling of power plants not only enhances process models, but can also increases confidence in operational decisions

  13. Modelling the predictive performance of credit scoring

    Shi-Wei Shen

    2013-07-01

    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.

  14. MULTIVARIATE MODEL FOR CORPORATE BANKRUPTCY PREDICTION IN ROMANIA

    Daniel BRÎNDESCU – OLARIU

    2016-06-01

    Full Text Available The current paper proposes a methodology for bankruptcy prediction applicable for Romanian companies. Low bankruptcy frequencies registered in the past have limited the importance of bankruptcy prediction in Romania. The changes in the economic environment brought by the economic crisis, as well as by the entrance in the European Union, make the availability of performing bankruptcy assessment tools more important than ever before. The proposed methodology is centred on a multivariate model, developed through discriminant analysis. Financial ratios are employed as explanatory variables within the model. The study has included 53,252 yearly financial statements from the period 2007 – 2010, with the state of the companies being monitored until the end of 2012. It thus employs the largest sample ever used in Romanian research in the field of bankruptcy prediction, not targeting high levels of accuracy over isolated samples, but reliability and ease of use over the entire population.

  15. Making predictions in a changing world-inference, uncertainty, and learning.

    O'Reilly, Jill X

    2013-01-01

    To function effectively, brains need to make predictions about their environment based on past experience, i.e., they need to learn about their environment. The algorithms by which learning occurs are of interest to neuroscientists, both in their own right (because they exist in the brain) and as a tool to model participants' incomplete knowledge of task parameters and hence, to better understand their behavior. This review focusses on a particular challenge for learning algorithms-how to match the rate at which they learn to the rate of change in the environment, so that they use as much observed data as possible whilst disregarding irrelevant, old observations. To do this algorithms must evaluate whether the environment is changing. We discuss the concepts of likelihood, priors and transition functions, and how these relate to change detection. We review expected and estimation uncertainty, and how these relate to change detection and learning rate. Finally, we consider the neural correlates of uncertainty and learning. We argue that the neural correlates of uncertainty bear a resemblance to neural systems that are active when agents actively explore their environments, suggesting that the mechanisms by which the rate of learning is set may be subject to top down control (in circumstances when agents actively seek new information) as well as bottom up control (by observations that imply change in the environment).

  16. Comparison of two ordinal prediction models

    Kattan, Michael W; Gerds, Thomas A

    2015-01-01

    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  17. Prediction Approach of Critical Node Based on Multiple Attribute Decision Making for Opportunistic Sensor Networks

    Qifan Chen

    2016-01-01

    Full Text Available Predicting critical nodes of Opportunistic Sensor Network (OSN can help us not only to improve network performance but also to decrease the cost in network maintenance. However, existing ways of predicting critical nodes in static network are not suitable for OSN. In this paper, the conceptions of critical nodes, region contribution, and cut-vertex in multiregion OSN are defined. We propose an approach to predict critical node for OSN, which is based on multiple attribute decision making (MADM. It takes RC to present the dependence of regions on Ferry nodes. TOPSIS algorithm is employed to find out Ferry node with maximum comprehensive contribution, which is a critical node. The experimental results show that, in different scenarios, this approach can predict the critical nodes of OSN better.

  18. Data Quality Enhanced Prediction Model for Massive Plant Data

    Park, Moon-Ghu; Kang, Seong-Ki; Shin, Hajin

    2016-01-01

    This paper introduces an integrated signal preconditioning and model prediction mainly by kernel functions. The performance and benefits of the methods are demonstrated by a case study with measurement data from a power plant and its components transient data. The developed methods will be applied as a part of monitoring massive or big data platform where human experts cannot detect the fault behaviors due to too large size of the measurements. Recent extensive efforts for on-line monitoring implementation insists that a big surprise in the modeling for predicting process variables was the extent of data quality problems in measurement data especially for data-driven modeling. Bad data for training will be learned as normal and can make significant degrade in prediction performance. For this reason, the quantity and quality of measurement data in modeling phase need special care. Bad quality data must be removed from training sets to the bad data considered as normal system behavior. This paper presented an integrated structure of supervisory system for monitoring the plants or sensors performance. The quality of the data-driven model is improved with a bilateral kernel filter for preprocessing of the noisy data. The prediction module is also based on kernel regression having the same basis with noise filter. The model structure is optimized by a grouping process with nonlinear Hoeffding correlation function

  19. Data Quality Enhanced Prediction Model for Massive Plant Data

    Park, Moon-Ghu [Nuclear Engr. Sejong Univ., Seoul (Korea, Republic of); Kang, Seong-Ki [Monitoring and Diagnosis, Suwon (Korea, Republic of); Shin, Hajin [Saint Paul Preparatory Seoul, Seoul (Korea, Republic of)

    2016-10-15

    This paper introduces an integrated signal preconditioning and model prediction mainly by kernel functions. The performance and benefits of the methods are demonstrated by a case study with measurement data from a power plant and its components transient data. The developed methods will be applied as a part of monitoring massive or big data platform where human experts cannot detect the fault behaviors due to too large size of the measurements. Recent extensive efforts for on-line monitoring implementation insists that a big surprise in the modeling for predicting process variables was the extent of data quality problems in measurement data especially for data-driven modeling. Bad data for training will be learned as normal and can make significant degrade in prediction performance. For this reason, the quantity and quality of measurement data in modeling phase need special care. Bad quality data must be removed from training sets to the bad data considered as normal system behavior. This paper presented an integrated structure of supervisory system for monitoring the plants or sensors performance. The quality of the data-driven model is improved with a bilateral kernel filter for preprocessing of the noisy data. The prediction module is also based on kernel regression having the same basis with noise filter. The model structure is optimized by a grouping process with nonlinear Hoeffding correlation function.

  20. Modeling decision making as a support tool for policy making on renewable energy development

    Cannemi, Marco; García-Melón, Mónica; Aragonés-Beltrán, Pablo; Gómez-Navarro, Tomás

    2014-01-01

    This paper presents the findings of a study on decision making models for the analysis of capital-risk investors’ preferences on biomass power plants projects. The aim of the work is to improve the support tools for policy makers in the field of renewable energy development. Analytic Network Process (ANP) helps to better understand capital-risk investors preferences towards different kinds of biomass fueled power plants. The results of the research allow public administration to better foresee the investors’ reaction to the incentive system, or to modify the incentive system to better drive investors’ decisions. Changing the incentive system is seen as major risk by investors. Therefore, public administration must design better and longer-term incentive systems, forecasting market reactions. For that, two scenarios have been designed, one showing a typical decision making process and another proposing an improved decision making scenario. A case study conducted in Italy has revealed that ANP allows understanding how capital-risk investors interpret the situation and make decisions when investing on biomass power plants; the differences between the interests of public administrations’s and promoters’, how decision making could be influenced by adding new decision criteria, and which case would be ranked best according to the decision models. - Highlights: • We applied ANP to the investors’ preferences on biomass power plants projects. • The aim is to improve the advising tools for renewable energy policy making. • A case study has been carried out with the help of two experts. • We designed two scenarios: decision making as it is and how could it be improved. • Results prove ANP is a fruitful tool enhancing participation and transparency

  1. Model Predictive Control of Sewer Networks

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    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 cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  2. Distributed Model Predictive Control via Dual Decomposition

    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...

  3. Making Validated Educational Models Central in Preschool Standards.

    Schweinhart, Lawrence J.

    This paper presents some ideas to preschool educators and policy makers about how to make validated educational models central in standards for preschool education and care programs that are available to all 3- and 4-year-olds. Defining an educational model as a coherent body of program practices, curriculum content, program and child, and teacher…

  4. A stepwise model to predict monthly streamflow

    Mahmood Al-Juboori, Anas; Guven, Aytac

    2016-12-01

    In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.

  5. Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

    Su, X.

    2017-12-01

    A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

  6. A spiral model of musical decision-making.

    Bangert, Daniel; Schubert, Emery; Fabian, Dorottya

    2014-01-01

    This paper describes a model of how musicians make decisions about performing notated music. The model builds on psychological theories of decision-making and was developed from empirical studies of Western art music performance that aimed to identify intuitive and deliberate processes of decision-making, a distinction consistent with dual-process theories of cognition. The model proposes that the proportion of intuitive (Type 1) and deliberate (Type 2) decision-making processes changes with increasing expertise and conceptualizes this change as movement along a continually narrowing upward spiral where the primary axis signifies principal decision-making type and the vertical axis marks level of expertise. The model is intended to have implications for the development of expertise as described in two main phases. The first is movement from a primarily intuitive approach in the early stages of learning toward greater deliberation as analytical techniques are applied during practice. The second phase occurs as deliberate decisions gradually become automatic (procedural), increasing the role of intuitive processes. As a performer examines more issues or reconsiders decisions, the spiral motion toward the deliberate side and back to the intuitive is repeated indefinitely. With increasing expertise, the spiral tightens to signify greater control over decision type selection. The model draws on existing theories, particularly Evans' (2011) Intervention Model of dual-process theories, Cognitive Continuum Theory Hammond et al. (1987), Hammond (2007), Baylor's (2001) U-shaped model for the development of intuition by level of expertise. By theorizing how musical decision-making operates over time and with increasing expertise, this model could be used as a framework for future research in music performance studies and performance science more generally.

  7. A spiral model of musical decision-making

    Daniel eBangert

    2014-04-01

    Full Text Available This paper describes a model of how musicians make decisions about performing notated music. The model builds on psychological theories of decision-making and was developed from empirical studies of Western art music performance that aimed to identify intuitive and deliberate processes of decision-making, a distinction consistent with dual-process theories of cognition. The model proposes that the proportion of intuitive (Type 1 and deliberate (Type 2 decision-making processes changes with increasing expertise and conceptualises this change as movement along a continually narrowing upward spiral where the primary axis signifies principal decision-making type and the vertical axis marks level of expertise. The model is intended to have implications for the development of expertise as described in two main phases. The first is movement from a primarily intuitive approach in the early stages of learning towards greater deliberation as analytical techniques are applied during practice. The second phase occurs as deliberate decisions gradually become automatic (procedural, increasing the role of intuitive processes. As a performer examines more issues or reconsiders decisions, the spiral motion towards the deliberate side and back to the intuitive is repeated indefinitely. With increasing expertise, the spiral tightens to signify greater control over decision type selection. The model draws on existing theories, particularly Evans’ (2011 Intervention Model of dual-process theories, Cognitive Continuum Theory (Hammond et al., 1987; Hammond, 2007, and Baylor’s (2001 U-shaped model for the development of intuition by level of expertise. By theorising how musical decision-making operates over time and with increasing expertise, this model could be used as a framework for future research in music performance studies and performance science more generally.

  8. In silico modeling to predict drug-induced phospholipidosis

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G.; Sadrieh, Nakissa

    2013-01-01

    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

  9. New Guideline for the Reporting of Studies Developing, Validating, or Updating a Multivariable Clinical Prediction Model : The TRIPOD Statement

    Moons, Karel G. M.; Altman, Douglas G.; Reitsma, Johannes B.; Collins, Gary S.

    Prediction models are developed to aid health care providers in estimating the probability that a specific outcome or disease is present (diagnostic prediction models) or will occur in the future (prognostic prediction models), to inform their decision making. Prognostic models here also include

  10. Electrostatic ion thrusters - towards predictive modeling

    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.)

  11. Insights from quantum cognitive models for organizational decision making

    White, L.C.; Pothos, E. M.; Busemeyer, J. R.

    2015-01-01

    Organizational decision making is often explored with theories from the heuristics and biases research program, which have demonstrated great value as descriptions of how people in organizations make decisions. Nevertheless, rational analysis and classical probability theory are still seen by many as the best accounts of how decisions should be made and classical probability theory is the preferred framework for cognitive modelling for many researchers. The focus of this work is quantum proba...

  12. An Intelligent Model for Stock Market Prediction

    IbrahimM. Hamed

    2012-08-01

    Full Text Available This paper presents an intelligent model for stock market signal prediction using Multi-Layer Perceptron (MLP Artificial Neural Networks (ANN. Blind source separation technique, from signal processing, is integrated with the learning phase of the constructed baseline MLP ANN to overcome the problems of prediction accuracy and lack of generalization. Kullback Leibler Divergence (KLD is used, as a learning algorithm, because it converges fast and provides generalization in the learning mechanism. Both accuracy and efficiency of the proposed model were confirmed through the Microsoft stock, from wall-street market, and various data sets, from different sectors of the Egyptian stock market. In addition, sensitivity analysis was conducted on the various parameters of the model to ensure the coverage of the generalization issue. Finally, statistical significance was examined using ANOVA test.

  13. Predictive Models, How good are they?

    Kasch, Helge

    The WAD grading system has been used for more than 20 years by now. It has shown long-term viability, but with strengths and limitations. New bio-psychosocial assessment of the acute whiplash injured subject may provide better prediction of long-term disability and pain. Furthermore, the emerging......-up. It is important to obtain prospective identification of the relevant risk underreported disability could, if we were able to expose these hidden “risk-factors” during our consultations, provide us with better predictive models. New data from large clinical studies will present exciting new genetic risk markers...

  14. Using the domain identification model to study major and career decision-making processes

    Tendhar, Chosang; Singh, Kusum; Jones, Brett D.

    2018-03-01

    The purpose of this study was to examine the extent to which (1) a domain identification model could be used to predict students' engineering major and career intentions and (2) the MUSIC Model of Motivation components could be used to predict domain identification. The data for this study were collected from first-year engineering students. We used a structural equation model to test the hypothesised relationship between variables in the partial domain identification model. The findings suggested that engineering identification significantly predicted engineering major intentions and career intentions and had the highest effect on those two variables compared to other motivational constructs. Furthermore, results suggested that success, interest, and caring are plausible contributors to students' engineering identification. Overall, there is strong evidence that the domain identification model can be used as a lens to study career decision-making processes in engineering, and potentially, in other fields as well.

  15. Cortical Brain Activity Reflecting Attentional Biasing Toward Reward-Predicting Cues Covaries with Economic Decision-Making Performance.

    San Martín, René; Appelbaum, Lawrence G; Huettel, Scott A; Woldorff, Marty G

    2016-01-01

    Adaptive choice behavior depends critically on identifying and learning from outcome-predicting cues. We hypothesized that attention may be preferentially directed toward certain outcome-predicting cues. We studied this possibility by analyzing event-related potential (ERP) responses in humans during a probabilistic decision-making task. Participants viewed pairs of outcome-predicting visual cues and then chose to wager either a small (i.e., loss-minimizing) or large (i.e., gain-maximizing) amount of money. The cues were bilaterally presented, which allowed us to extract the relative neural responses to each cue by using a contralateral-versus-ipsilateral ERP contrast. We found an early lateralized ERP response, whose features matched the attention-shift-related N2pc component and whose amplitude scaled with the learned reward-predicting value of the cues as predicted by an attention-for-reward model. Consistently, we found a double dissociation involving the N2pc. Across participants, gain-maximization positively correlated with the N2pc amplitude to the most reliable gain-predicting cue, suggesting an attentional bias toward such cues. Conversely, loss-minimization was negatively correlated with the N2pc amplitude to the most reliable loss-predicting cue, suggesting an attentional avoidance toward such stimuli. These results indicate that learned stimulus-reward associations can influence rapid attention allocation, and that differences in this process are associated with individual differences in economic decision-making performance. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  16. Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce

    Wei-Chin Lin

    2009-04-01

    Full Text Available Greenhouse-grown butter lettuce (Lactuca sativa L. can potentially be stored for 21 days at constant 0°C. When storage temperature was increased to 5°C or 10°C, shelf life was shortened to 14 or 10 days, respectively, in our previous observations. Also, commercial shelf life of 7 to 10 days is common, due to postharvest temperature fluctuations. The objective of this study was to establish neural network (NN models to predict the remaining shelf life (RSL under fluctuating postharvest temperatures. A box of 12 - 24 lettuce heads constituted a sample unit. The end of the shelf life of each head was determined when it showed initial signs of decay or yellowing. Air temperatures inside a shipping box were recorded. Daily average temperatures in storage and averaged shelf life of each box were used as inputs, and the RSL was modeled as an output. An R2 of 0.57 could be observed when a simple NN structure was employed. Since the "future" (or remaining storage temperatures were unavailable at the time of making a prediction, a second NN model was introduced to accommodate a range of future temperatures and associated shelf lives. Using such 2-stage NN models, an R2 of 0.61 could be achieved for predicting RSL. This study indicated that NN modeling has potential for cold chain quality control and shelf life prediction.

  17. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    SILVA R. G.

    1999-01-01

    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.

  18. A statistical model for predicting muscle performance

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  19. A Model of Decision-Making Based on Critical Thinking

    Uluçınar, Ufuk; Aypay, Ahmet

    2016-01-01

    The aim of this study is to examine the causal relationships between high school students' inquisitiveness, open-mindedness, causal thinking, and rational and intuitive decision-making dispositions through an assumed model based on research data. This study was designed in correlational model. Confirmatory factor analysis and path analysis, which are structural equation modelling applications, were used to explain these relationships. The participants were 404 students studying in five high s...

  20. Decision Styles and Rationality: An Analysis of the Predictive Validity of the General Decision-Making Style Inventory

    Curseu, Petru Lucian; Schruijer, Sandra G. L.

    2012-01-01

    This study investigates the relationship between the five decision-making styles evaluated by the General Decision-Making Style Inventory, indecisiveness, and rationality in decision making. Using a sample of 102 middle-level managers, the results show that the rational style positively predicts rationality in decision making and negatively…

  1. Predictive Modelling Risk Calculators and the Non Dialysis Pathway.

    Robins, Jennifer; Katz, Ivor

    2013-04-16

    This guideline will review the current prediction models and survival/mortality scores available for decision making in patients with advanced kidney disease who are being considered for a non-dialysis treatment pathway. Risk prediction is gaining increasing attention with emerging literature suggesting improved patient outcomes through individualised risk prediction (1). Predictive models help inform the nephrologist and the renal palliative care specialists in their discussions with patients and families about suitability or otherwise of dialysis. Clinical decision making in the care of end stage kidney disease (ESKD) patients on a non-dialysis treatment pathway is currently governed by several observational trials (3). Despite the paucity of evidence based medicine in this field, it is becoming evident that the survival advantages associated with renal replacement therapy in these often elderly patients with multiple co-morbidities and limited functional status may be negated by loss of quality of life (7) (6), further functional decline (5, 8), increased complications and hospitalisations. This article is protected by copyright. All rights reserved.

  2. Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance

    Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos

    2016-01-01

    At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the model, or the effort required to ma...

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

    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

  4. Neuro-fuzzy modeling in bankruptcy prediction

    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.

  5. Risk approximation in decision making: approximative numeric abilities predict advantageous decisions under objective risk.

    Mueller, Silke M; Schiebener, Johannes; Delazer, Margarete; Brand, Matthias

    2018-01-22

    Many decision situations in everyday life involve mathematical considerations. In decisions under objective risk, i.e., when explicit numeric information is available, executive functions and abilities to handle exact numbers and ratios are predictors of objectively advantageous choices. Although still debated, exact numeric abilities, e.g., normative calculation skills, are assumed to be related to approximate number processing skills. The current study investigates the effects of approximative numeric abilities on decision making under objective risk. Participants (N = 153) performed a paradigm measuring number-comparison, quantity-estimation, risk-estimation, and decision-making skills on the basis of rapid dot comparisons. Additionally, a risky decision-making task with exact numeric information was administered, as well as tasks measuring executive functions and exact numeric abilities, e.g., mental calculation and ratio processing skills, were conducted. Approximative numeric abilities significantly predicted advantageous decision making, even beyond the effects of executive functions and exact numeric skills. Especially being able to make accurate risk estimations seemed to contribute to superior choices. We recommend approximation skills and approximate number processing to be subject of future investigations on decision making under risk.

  6. Predictive Models for Carcinogenicity and Mutagenicity ...

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  7. Application of structural reliability and risk assessment to life prediction and life extension decision making

    Meyer, T.A.; Balkey, K.R.; Bishop, B.A.

    1987-01-01

    There can be numerous uncertainties involved in performing component life assessments. In addition, sufficient data may be unavailable to make a useful life prediction. Structural Reliability and Risk Assessment (SRRA) is primarily an analytical methodology or tool that quantifies the impact of uncertainties on the structural life of plant components and can address the lack of data in component life prediction. As a prelude to discussing the technical aspects of SRRA, a brief review of general component life prediction methods is first made so as to better develop an understanding of the role of SRRA in such evaluations. SRRA is then presented as it is applied in component life evaluations with example applications being discussed for both nuclear and non-nuclear components

  8. Validation of models that predict Cesarean section after induction of labor

    Verhoeven, C. J. M.; Oudenaarden, A.; Hermus, M. A. A.; Porath, M. M.; Oei, S. G.; Mol, B. W. J.

    2009-01-01

    Objective Models for the prediction of Cesarean delivery after induction of labor can be used to improve clinical decision-making. The objective of this study was to validate two existing models, published by Peregrine et al. and Rane et al., for the prediction of Cesarean section after induction of

  9. Testing In College Admissions: An Alternative to the Traditional Predictive Model.

    Lunneborg, Clifford E.

    1982-01-01

    A decision-making or utility theory model (which deals effectively with affirmative action goals and allows standardized tests to be placed in the service of those goals) is discussed as an alternative to traditional predictive admissions. (Author/PN)

  10. Prediction of Geological Subsurfaces Based on Gaussian Random Field Models

    Abrahamsen, Petter

    1997-12-31

    During the sixties, random functions became practical tools for predicting ore reserves with associated precision measures in the mining industry. This was the start of the geostatistical methods called kriging. These methods are used, for example, in petroleum exploration. This thesis reviews the possibilities for using Gaussian random functions in modelling of geological subsurfaces. It develops methods for including many sources of information and observations for precise prediction of the depth of geological subsurfaces. The simple properties of Gaussian distributions make it possible to calculate optimal predictors in the mean square sense. This is done in a discussion of kriging predictors. These predictors are then extended to deal with several subsurfaces simultaneously. It is shown how additional velocity observations can be used to improve predictions. The use of gradient data and even higher order derivatives are also considered and gradient data are used in an example. 130 refs., 44 figs., 12 tabs.

  11. Advanced Computational Modeling Approaches for Shock Response Prediction

    Derkevorkian, Armen; Kolaini, Ali R.; Peterson, Lee

    2015-01-01

    Motivation: (1) The activation of pyroshock devices such as explosives, separation nuts, pin-pullers, etc. produces high frequency transient structural response, typically from few tens of Hz to several hundreds of kHz. (2) Lack of reliable analytical tools makes the prediction of appropriate design and qualification test levels a challenge. (3) In the past few decades, several attempts have been made to develop methodologies that predict the structural responses to shock environments. (4) Currently, there is no validated approach that is viable to predict shock environments overt the full frequency range (i.e., 100 Hz to 10 kHz). Scope: (1) Model, analyze, and interpret space structural systems with complex interfaces and discontinuities, subjected to shock loads. (2) Assess the viability of a suite of numerical tools to simulate transient, non-linear solid mechanics and structural dynamics problems, such as shock wave propagation.

  12. Validated predictive modelling of the environmental resistome.

    Amos, Gregory C A; Gozzard, Emma; Carter, Charlotte E; Mead, Andrew; Bowes, Mike J; Hawkey, Peter M; Zhang, Lihong; Singer, Andrew C; Gaze, William H; Wellington, Elizabeth M H

    2015-06-01

    Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.

  13. Nonlinear model predictive control theory and algorithms

    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...

  14. Baryogenesis model predicting antimatter in the Universe

    Kirilova, D.

    2003-01-01

    Cosmic ray and gamma-ray data do not rule out antimatter domains in the Universe, separated at distances bigger than 10 Mpc from us. Hence, it is interesting to analyze the possible generation of vast antimatter structures during the early Universe evolution. We discuss a SUSY-condensate baryogenesis model, predicting large separated regions of matter and antimatter. The model provides generation of the small locally observed baryon asymmetry for a natural initial conditions, it predicts vast antimatter domains, separated from the matter ones by baryonically empty voids. The characteristic scale of antimatter regions and their distance from the matter ones is in accordance with observational constraints from cosmic ray, gamma-ray and cosmic microwave background anisotropy data

  15. Finding Furfural Hydrogenation Catalysts via Predictive Modelling

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

    2010-01-01

    Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre t...

  16. Predictive Modeling in Actinide Chemistry and Catalysis

    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.

  17. Model Predictive Control of Mineral Column Flotation Process

    Yahui Tian

    2018-06-01

    Full Text Available Column flotation is an efficient method commonly used in the mineral industry to separate useful minerals from ores of low grade and complex mineral composition. Its main purpose is to achieve maximum recovery while ensuring desired product grade. This work addresses a model predictive control design for a mineral column flotation process modeled by a set of nonlinear coupled heterodirectional hyperbolic partial differential equations (PDEs and ordinary differential equations (ODEs, which accounts for the interconnection of well-stirred regions represented by continuous stirred tank reactors (CSTRs and transport systems given by heterodirectional hyperbolic PDEs, with these two regions combined through the PDEs’ boundaries. The model predictive control considers both optimality of the process operations and naturally present input and state/output constraints. For the discrete controller design, spatially varying steady-state profiles are obtained by linearizing the coupled ODE–PDE model, and then the discrete system is obtained by using the Cayley–Tustin time discretization transformation without any spatial discretization and/or without model reduction. The model predictive controller is designed by solving an optimization problem with input and state/output constraints as well as input disturbance to minimize the objective function, which leads to an online-solvable finite constrained quadratic regulator problem. Finally, the controller performance to keep the output at the steady state within the constraint range is demonstrated by simulation studies, and it is concluded that the optimal control scheme presented in this work makes this flotation process more efficient.

  18. Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model

    Yu-Ri Kim

    2016-03-01

    Full Text Available Recently, the Korean government has enforced disclosure of building energy performance, so that such information can help owners and prospective buyers to make suitable investment plans. Such a building energy performance policy of the government makes it mandatory for the building owners to obtain engineering audits and thereby evaluate the energy performance levels of their buildings. However, to calculate energy performance levels (i.e., asset rating methodology, a qualified expert needs to have access to at least the full project documentation and/or conduct an on-site inspection of the buildings. Energy performance certification costs a lot of time and money. Moreover, the database of certified buildings is still actually quite small. A need, therefore, is increasing for a simplified and user-friendly energy performance prediction tool for non-specialists. Also, a database which allows building owners and users to compare best practices is required. In this regard, the current study developed a simplified performance prediction model through experimental design, energy simulations and ANOVA (analysis of variance. Furthermore, using the new prediction model, a related mobile application was also developed.

  19. Translational Models of Gambling-Related Decision-Making.

    Winstanley, Catharine A; Clark, Luke

    Gambling is a harmless, recreational pastime that is ubiquitous across cultures. However, for some, gambling becomes a maladaptive and compulsive, and this syndrome is conceptualized as a behavioural addiction. Laboratory models that capture the key cognitive processes involved in gambling behaviour, and that can be translated across species, have the potential to make an important contribution to both decision neuroscience and the study of addictive disorders. The Iowa gambling task has been widely used to assess human decision-making under uncertainty, and this paradigm can be successfully modelled in rodents. Similar neurobiological processes underpin choice behaviour in humans and rats, and thus, a preference for the disadvantageous "high-risk, high-reward" options may reflect meaningful vulnerability for mental health problems. However, the choice behaviour operationalized by these tasks does not necessarily approximate the vulnerability to gambling disorder (GD) per se. We consider a number of psychological challenges that apply to modelling gambling in a translational way, and evaluate the success of the existing models. Heterogeneity in the structure of gambling games, as well as in the motivations of individuals with GD, is highlighted. The potential issues with extrapolating too directly from established animal models of drug dependency are discussed, as are the inherent difficulties in validating animal models of GD in the absence of any approved treatments for GD. Further advances in modelling the cognitive biases endemic in human decision-making, which appear to be exacerbated in GD, may be a promising line of research.

  20. Automatic evidence quality prediction to support evidence-based decision making.

    Sarker, Abeed; Mollá, Diego; Paris, Cécile

    2015-06-01

    Evidence-based medicine practice requires practitioners to obtain the best available medical evidence, and appraise the quality of the evidence when making clinical decisions. Primarily due to the plethora of electronically available data from the medical literature, the manual appraisal of the quality of evidence is a time-consuming process. We present a fully automatic approach for predicting the quality of medical evidence in order to aid practitioners at point-of-care. Our approach extracts relevant information from medical article abstracts and utilises data from a specialised corpus to apply supervised machine learning for the prediction of the quality grades. Following an in-depth analysis of the usefulness of features (e.g., publication types of articles), they are extracted from the text via rule-based approaches and from the meta-data associated with the articles, and then applied in the supervised classification model. We propose the use of a highly scalable and portable approach using a sequence of high precision classifiers, and introduce a simple evaluation metric called average error distance (AED) that simplifies the comparison of systems. We also perform elaborate human evaluations to compare the performance of our system against human judgments. We test and evaluate our approaches on a publicly available, specialised, annotated corpus containing 1132 evidence-based recommendations. Our rule-based approach performs exceptionally well at the automatic extraction of publication types of articles, with F-scores of up to 0.99 for high-quality publication types. For evidence quality classification, our approach obtains an accuracy of 63.84% and an AED of 0.271. The human evaluations show that the performance of our system, in terms of AED and accuracy, is comparable to the performance of humans on the same data. The experiments suggest that our structured text classification framework achieves evaluation results comparable to those of human performance

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

    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...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  2. Two stage neural network modelling for robust model predictive control.

    Patan, Krzysztof

    2018-01-01

    The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Predictive Modeling of the CDRA 4BMS

    Coker, Robert F.; Knox, James C.

    2016-01-01

    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) 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.

  4. Evidence accumulation in decision making: unifying the "take the best" and the "rational" models.

    Lee, Michael D; Cummins, Tarrant D R

    2004-04-01

    An evidence accumulation model of forced-choice decision making is proposed to unify the fast and frugal take the best (TTB) model and the alternative rational (RAT) model with which it is usually contrasted. The basic idea is to treat the TTB model as a sequential-sampling process that terminates as soon as any evidence in favor of a decision is found and the rational approach as a sequential-sampling process that terminates only when all available information has been assessed. The unified TTB and RAT models were tested in an experiment in which participants learned to make correct judgments for a set of real-world stimuli on the basis of feedback, and were then asked to make additional judgments without feedback for cases in which the TTB and the rational models made different predictions. The results show that, in both experiments, there was strong intraparticipant consistency in the use of either the TTB or the rational model but large interparticipant differences in which model was used. The unified model is shown to be able to capture the differences in decision making across participants in an interpretable way and is preferred by the minimum description length model selection criterion.

  5. Mental models of decision-making in a healthcare executive

    Long, Katrina

    2017-01-01

    Purpose The importance of shared mental models in teamwork has been explored in a diverse array of artificial work groups. This study extends such research to explore the role of mental models in the strategic decision-making of a real-world senior management group. Design/Methodology Data were collected from an intact group of senior healthcare executives (N=13) through semi-structured interviews, meeting observations and internal document analysis. Participants responded to intervi...

  6. A decision-making process model of young online shoppers.

    Lin, Chin-Feng; Wang, Hui-Fang

    2008-12-01

    Based on the concepts of brand equity, means-end chain, and Web site trust, this study proposes a novel model called the consumption decision-making process of adolescents (CDMPA) to understand adolescents' Internet consumption habits and behavioral intention toward particular sporting goods. The findings of the CDMPA model can help marketers understand adolescents' consumption preferences and habits for developing effective Internet marketing strategies.

  7. Data Driven Economic Model Predictive Control

    Masoud Kheradmandi

    2018-04-01

    Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

  8. The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer.

    Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M; Hartman, Mikael; Bhoo-Pathy, Nirmala

    2015-02-01

    Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.

  9. Modelling human emotions for tactical decision-making games

    Visschedijk, G.; Lazonder, Adrianus W.; van der Hulst, A.; Vink, N.; Leemkuil, Hendrik H.

    2013-01-01

    The training of tactical decision making increasingly occurs through serious computer games. A challenging aspect of designing such games is the modelling of human emotions. Two studies were performed to investigate the relation between fidelity and human emotion recognition in virtual human

  10. Modelling human emotions for tactical decision-making games

    Visschedijk, G.C.; Lazonder, A.W.; Hulst, A.H. van der; Vink, N.; Leemkuil, H.

    2013-01-01

    The training of tactical decision making increasingly occurs through serious computer games. A challenging aspect of designing such games is the modelling of human emotions. Two studieswere performed to investigate the relation between fidelity and human emotion recognition in virtual human

  11. Modelling Human Emotions for Tactical Decision-Making Games

    Visschedijk, Gillian C.; Lazonder, Ard W.; van der Hulst, Anja; Vink, Nathalie; Leemkuil, Henny

    2013-01-01

    The training of tactical decision making increasingly occurs through serious computer games. A challenging aspect of designing such games is the modelling of human emotions. Two studies were performed to investigate the relation between fidelity and human emotion recognition in virtual human characters. Study 1 compared five versions of a virtual…

  12. A Behavioral Decision Making Modeling Approach Towards Hedging Services

    Pennings, J.M.E.; Candel, M.J.J.M.; Egelkraut, T.M.

    2003-01-01

    This paper takes a behavioral approach toward the market for hedging services. A behavioral decision-making model is developed that provides insight into how and why owner-managers decide the way they do regarding hedging services. Insight into those choice processes reveals information needed by

  13. The limitations of applying rational decision-making models to ...

    The aim of this paper is to show the limitations of rational decision-making models as applied to child spacing and more specifically to the use of modern methods of contraception. In the light of factors known to influence low uptake of child spacing services in other African countries, suggestions are made to explain the ...

  14. Leadership of risk decision making in a complex, technology organization: The deliberative decision making model

    Flaming, Susan C.

    2007-12-01

    The continuing saga of satellite technology development is as much a story of successful risk management as of innovative engineering. How do program leaders on complex, technology projects manage high stakes risks that threaten business success and satellite performance? This grounded theory study of risk decision making portrays decision leadership practices at one communication satellite company. Integrated product team (IPT) leaders of multi-million dollar programs were interviewed and observed to develop an extensive description of the leadership skills required to navigate organizational influences and drive challenging risk decisions to closure. Based on the study's findings the researcher proposes a new decision making model, Deliberative Decision Making, to describe the program leaders' cognitive and organizational leadership practices. This Deliberative Model extends the insights of prominent decision making models including the rational (or classical) and the naturalistic and qualifies claims made by bounded rationality theory. The Deliberative Model describes how leaders proactively engage resources to play a variety of decision leadership roles. The Model incorporates six distinct types of leadership decision activities, undertaken in varying sequence based on the challenges posed by specific risks. Novel features of the Deliberative Decision Model include: an inventory of leadership methods for managing task challenges, potential stakeholder bias and debates; four types of leadership meta-decisions that guide decision processes, and aligned organizational culture. Both supporting and constraining organizational influences were observed as leaders managed major risks, requiring active leadership on the most difficult decisions. Although the company's engineering culture emphasized the importance of data-based decisions, the uncertainties intrinsic to satellite risks required expert engineering judgment to be exercised throughout. An investigation into

  15. An integrative formal model of motivation and decision making: The MGPM*.

    Ballard, Timothy; Yeo, Gillian; Loft, Shayne; Vancouver, Jeffrey B; Neal, Andrew

    2016-09-01

    We develop and test an integrative formal model of motivation and decision making. The model, referred to as the extended multiple-goal pursuit model (MGPM*), is an integration of the multiple-goal pursuit model (Vancouver, Weinhardt, & Schmidt, 2010) and decision field theory (Busemeyer & Townsend, 1993). Simulations of the model generated predictions regarding the effects of goal type (approach vs. avoidance), risk, and time sensitivity on prioritization. We tested these predictions in an experiment in which participants pursued different combinations of approach and avoidance goals under different levels of risk. The empirical results were consistent with the predictions of the MGPM*. Specifically, participants pursuing 1 approach and 1 avoidance goal shifted priority from the approach to the avoidance goal over time. Among participants pursuing 2 approach goals, those with low time sensitivity prioritized the goal with the larger discrepancy, whereas those with high time sensitivity prioritized the goal with the smaller discrepancy. Participants pursuing 2 avoidance goals generally prioritized the goal with the smaller discrepancy. Finally, all of these effects became weaker as the level of risk increased. We used quantitative model comparison to show that the MGPM* explained the data better than the original multiple-goal pursuit model, and that the major extensions from the original model were justified. The MGPM* represents a step forward in the development of a general theory of decision making during multiple-goal pursuit. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  16. Make

    Frauenfelder, Mark

    2012-01-01

    The first magazine devoted entirely to do-it-yourself technology projects presents its 29th quarterly edition for people who like to tweak, disassemble, recreate, and invent cool new uses for technology. MAKE Volume 29 takes bio-hacking to a new level. Get introduced to DIY tracking devices before they hit the consumer electronics marketplace. Learn how to build an EKG machine to study your heartbeat, and put together a DIY bio lab to study athletic motion using consumer grade hardware.

  17. Did Ptolemy make novel predictions? Launching Ptolemaic astronomy into the scientific realism debate.

    Carman, Christián; Díez, José

    2015-08-01

    The goal of this paper, both historical and philosophical, is to launch a new case into the scientific realism debate: geocentric astronomy. Scientific realism about unobservables claims that the non-observational content of our successful/justified empirical theories is true, or approximately true. The argument that is currently considered the best in favor of scientific realism is the No Miracles Argument: the predictive success of a theory that makes (novel) observational predictions while making use of non-observational content would be inexplicable unless such non-observational content approximately corresponds to the world "out there". Laudan's pessimistic meta-induction challenged this argument, and realists reacted by moving to a "selective" version of realism: the approximately true part of the theory is not its full non-observational content but only the part of it that is responsible for the novel, successful observational predictions. Selective scientific realism has been tested against some of the theories in Laudan's list, but the first member of this list, geocentric astronomy, has been traditionally ignored. Our goal here is to defend that Ptolemy's Geocentrism deserves attention and poses a prima facie strong case against selective realism, since it made several successful, novel predictions based on theoretical hypotheses that do not seem to be retained, not even approximately, by posterior theories. Here, though, we confine our work just to the detailed reconstruction of what we take to be the main novel, successful Ptolemaic predictions, leaving the full analysis and assessment of their significance for the realist thesis to future works. Copyright © 2015. Published by Elsevier Ltd.

  18. Plant control using embedded predictive models

    Godbole, S.S.; Gabler, W.E.; Eschbach, S.L.

    1990-01-01

    B and W recently undertook the design of an advanced light water reactor control system. A concept new to nuclear steam system (NSS) control was developed. The concept, which is called the Predictor-Corrector, uses mathematical models of portions of the controlled NSS to calculate, at various levels within the system, demand and control element position signals necessary to satisfy electrical demand. The models give the control system the ability to reduce overcooling and undercooling of the reactor coolant system during transients and upsets. Two types of mathematical models were developed for use in designing and testing the control system. One model was a conventional, comprehensive NSS model that responds to control system outputs and calculates the resultant changes in plant variables that are then used as inputs to the control system. Two other models, embedded in the control system, were less conventional, inverse models. These models accept as inputs plant variables, equipment states, and demand signals and predict plant operating conditions and control element states that will satisfy the demands. This paper reports preliminary results of closed-loop Reactor Coolant (RC) pump trip and normal load reduction testing of the advanced concept. Results of additional transient testing, and of open and closed loop stability analyses will be reported as they are available

  19. Scaling predictive modeling in drug development with cloud computing.

    Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

    2015-01-26

    Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

  20. Real estate value prediction using multivariate regression models

    Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

    2017-11-01

    The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

  1. Error analysis of short term wind power prediction models

    De Giorgi, Maria Grazia; Ficarella, Antonio; Tarantino, Marco

    2011-01-01

    The integration of wind farms in power networks has become an important problem. This is because the electricity produced cannot be preserved because of the high cost of storage and electricity production must follow market demand. Short-long-range wind forecasting over different lengths/periods of time is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based upon the valid assumption that all the causative factors are implicitly accounted for in the sequence of occurrence of the process itself. Hence time series modelling is equivalent to physical modelling. Auto Regressive Moving Average (ARMA) models, which perform a linear mapping between inputs and outputs, and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform a non-linear mapping, provide a robust approach to wind power prediction. In this work, these models are developed in order to forecast power production of a wind farm with three wind turbines, using real load data and comparing different time prediction periods. This comparative analysis takes in the first time, various forecasting methods, time horizons and a deep performance analysis focused upon the normalised mean error and the statistical distribution hereof in order to evaluate error distribution within a narrower curve and therefore forecasting methods whereby it is more improbable to make errors in prediction. (author)

  2. Atterberg Limits Prediction Comparing SVM with ANFIS Model

    Mohammad Murtaza Sherzoy

    2017-03-01

    Full Text Available Support Vector Machine (SVM and Adaptive Neuro-Fuzzy inference Systems (ANFIS both analytical methods are used to predict the values of Atterberg limits, such as the liquid limit, plastic limit and plasticity index. The main objective of this study is to make a comparison between both forecasts (SVM & ANFIS methods. All data of 54 soil samples are used and taken from the area of Peninsular Malaysian and tested for different parameters containing liquid limit, plastic limit, plasticity index and grain size distribution and were. The input parameter used in for this case are the fraction of grain size distribution which are the percentage of silt, clay and sand. The actual and predicted values of Atterberg limit which obtained from the SVM and ANFIS models are compared by using the correlation coefficient R2 and root mean squared error (RMSE value.  The outcome of the study show that the ANFIS model shows higher accuracy than SVM model for the liquid limit (R2 = 0.987, plastic limit (R2 = 0.949 and plastic index (R2 = 0966. RMSE value that obtained for both methods have shown that the ANFIS model has represent the best performance than SVM model to predict the Atterberg Limits as a whole.

  3. Error analysis of short term wind power prediction models

    De Giorgi, Maria Grazia; Ficarella, Antonio; Tarantino, Marco [Dipartimento di Ingegneria dell' Innovazione, Universita del Salento, Via per Monteroni, 73100 Lecce (Italy)

    2011-04-15

    The integration of wind farms in power networks has become an important problem. This is because the electricity produced cannot be preserved because of the high cost of storage and electricity production must follow market demand. Short-long-range wind forecasting over different lengths/periods of time is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based upon the valid assumption that all the causative factors are implicitly accounted for in the sequence of occurrence of the process itself. Hence time series modelling is equivalent to physical modelling. Auto Regressive Moving Average (ARMA) models, which perform a linear mapping between inputs and outputs, and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform a non-linear mapping, provide a robust approach to wind power prediction. In this work, these models are developed in order to forecast power production of a wind farm with three wind turbines, using real load data and comparing different time prediction periods. This comparative analysis takes in the first time, various forecasting methods, time horizons and a deep performance analysis focused upon the normalised mean error and the statistical distribution hereof in order to evaluate error distribution within a narrower curve and therefore forecasting methods whereby it is more improbable to make errors in prediction. (author)

  4. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

    Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

    2017-05-22

    of 40. The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.

  5. Modeling and Prediction of Krueger Device Noise

    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.

  6. Prediction of Chemical Function: Model Development and ...

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  7. Evaluating Predictive Models of Software Quality

    Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

    2014-06-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  8. Predicting FLDs Using a Multiscale Modeling Scheme

    Wu, Z.; Loy, C.; Wang, E.; Hegadekatte, V.

    2017-09-01

    The measurement of a single forming limit diagram (FLD) requires significant resources and is time consuming. We have developed a multiscale modeling scheme to predict FLDs using a combination of limited laboratory testing, crystal plasticity (VPSC) modeling, and dual sequential-stage finite element (ABAQUS/Explicit) modeling with the Marciniak-Kuczynski (M-K) criterion to determine the limit strain. We have established a means to work around existing limitations in ABAQUS/Explicit by using an anisotropic yield locus (e.g., BBC2008) in combination with the M-K criterion. We further apply a VPSC model to reduce the number of laboratory tests required to characterize the anisotropic yield locus. In the present work, we show that the predicted FLD is in excellent agreement with the measured FLD for AA5182 in the O temper. Instead of 13 different tests as for a traditional FLD determination within Novelis, our technique uses just four measurements: tensile properties in three orientations; plane strain tension; biaxial bulge; and the sheet crystallographic texture. The turnaround time is consequently far less than for the traditional laboratory measurement of the FLD.

  9. PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION

    Narciso Ysac Avila Serrano

    2009-06-01

    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  10. Evaluating predictive models of software quality

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  11. Gamma-Ray Pulsars Models and Predictions

    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...

  12. Artificial Neural Network Model for Predicting Compressive

    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.

  13. Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

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

    2015-01-01

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

  14. Making predictions of mangrove deforestation: a comparison of two methods in Kenya.

    Rideout, Alasdair J R; Joshi, Neha P; Viergever, Karin M; Huxham, Mark; Briers, Robert A

    2013-11-01

    Deforestation of mangroves is of global concern given their importance for carbon storage, biogeochemical cycling and the provision of other ecosystem services, but the links between rates of loss and potential drivers or risk factors are rarely evaluated. Here, we identified key drivers of mangrove loss in Kenya and compared two different approaches to predicting risk. Risk factors tested included various possible predictors of anthropogenic deforestation, related to population, suitability for land use change and accessibility. Two approaches were taken to modelling risk; a quantitative statistical approach and a qualitative categorical ranking approach. A quantitative model linking rates of loss to risk factors was constructed based on generalized least squares regression and using mangrove loss data from 1992 to 2000. Population density, soil type and proximity to roads were the most important predictors. In order to validate this model it was used to generate a map of losses of Kenyan mangroves predicted to have occurred between 2000 and 2010. The qualitative categorical model was constructed using data from the same selection of variables, with the coincidence of different risk factors in particular mangrove areas used in an additive manner to create a relative risk index which was then mapped. Quantitative predictions of loss were significantly correlated with the actual loss of mangroves between 2000 and 2010 and the categorical risk index values were also highly correlated with the quantitative predictions. Hence, in this case the relatively simple categorical modelling approach was of similar predictive value to the more complex quantitative model of mangrove deforestation. The advantages and disadvantages of each approach are discussed, and the implications for mangroves are outlined. © 2013 Blackwell Publishing Ltd.

  15. Climate change decision-making: Model & parameter uncertainties explored

    Dowlatabadi, H.; Kandlikar, M.; Linville, C.

    1995-12-31

    A critical aspect of climate change decision-making is uncertainties in current understanding of the socioeconomic, climatic and biogeochemical processes involved. Decision-making processes are much better informed if these uncertainties are characterized and their implications understood. Quantitative analysis of these uncertainties serve to inform decision makers about the likely outcome of policy initiatives, and help set priorities for research so that outcome ambiguities faced by the decision-makers are reduced. A family of integrated assessment models of climate change have been developed at Carnegie Mellon. These models are distinguished from other integrated assessment efforts in that they were designed from the outset to characterize and propagate parameter, model, value, and decision-rule uncertainties. The most recent of these models is ICAM 2.1. This model includes representation of the processes of demographics, economic activity, emissions, atmospheric chemistry, climate and sea level change and impacts from these changes and policies for emissions mitigation, and adaptation to change. The model has over 800 objects of which about one half are used to represent uncertainty. In this paper we show, that when considering parameter uncertainties, the relative contribution of climatic uncertainties are most important, followed by uncertainties in damage calculations, economic uncertainties and direct aerosol forcing uncertainties. When considering model structure uncertainties we find that the choice of policy is often dominated by model structure choice, rather than parameter uncertainties.

  16. Consumer's Online Shopping Influence Factors and Decision-Making Model

    Yan, Xiangbin; Dai, Shiliang

    Previous research on online consumer behavior has mostly been confined to the perceived risk which is used to explain those barriers for purchasing online. However, perceived benefit is another important factor which influences consumers’ decision when shopping online. As a result, an integrated consumer online shopping decision-making model is developed which contains three elements—Consumer, Product, and Web Site. This model proposed relative factors which influence the consumers’ intention during the online shopping progress, and divided them into two different dimensions—mentally level and material level. We tested those factors with surveys, from both online volunteers and offline paper surveys with more than 200 samples. With the help of SEM, the experimental results show that the proposed model and method can be used to analyze consumer’s online shopping decision-making process effectively.

  17. The information value of early career productivity in mathematics: a ROC analysis of prediction errors in bibliometricly informed decision making.

    Lindahl, Jonas; Danell, Rickard

    The aim of this study was to provide a framework to evaluate bibliometric indicators as decision support tools from a decision making perspective and to examine the information value of early career publication rate as a predictor of future productivity. We used ROC analysis to evaluate a bibliometric indicator as a tool for binary decision making. The dataset consisted of 451 early career researchers in the mathematical sub-field of number theory. We investigated the effect of three different definitions of top performance groups-top 10, top 25, and top 50 %; the consequences of using different thresholds in the prediction models; and the added prediction value of information on early career research collaboration and publications in prestige journals. We conclude that early career performance productivity has an information value in all tested decision scenarios, but future performance is more predictable if the definition of a high performance group is more exclusive. Estimated optimal decision thresholds using the Youden index indicated that the top 10 % decision scenario should use 7 articles, the top 25 % scenario should use 7 articles, and the top 50 % should use 5 articles to minimize prediction errors. A comparative analysis between the decision thresholds provided by the Youden index which take consequences into consideration and a method commonly used in evaluative bibliometrics which do not take consequences into consideration when determining decision thresholds, indicated that differences are trivial for the top 25 and the 50 % groups. However, a statistically significant difference between the methods was found for the top 10 % group. Information on early career collaboration and publication strategies did not add any prediction value to the bibliometric indicator publication rate in any of the models. The key contributions of this research is the focus on consequences in terms of prediction errors and the notion of transforming uncertainty

  18. Ordering decision-making methods on spare parts for a new aircraft fleet based on a two-sample prediction

    Yongquan, Sun; Xi, Chen; He, Ren; Yingchao, Jin; Quanwu, Liu

    2016-01-01

    Ordering decision-making on spare parts is crucial in maximizing aircraft utilization and minimizing total operating cost. Extensive researches on spare parts inventory management and optimal allocation could be found based on the amount of historical operation data or condition-monitoring data. However, it is challengeable to make an ordering decision on spare parts under the case of establishment of a fleet by introducing new aircraft with little historical data. In this paper, spare parts supporting policy and ordering decision-making policy for new aircraft fleet are analyzed firstly. Then two-sample predictions for a Weibull distribution and a Weibull process are incorporated into forecast of the first failure time and failure number during certain time period using Bayesian and classical method respectively, according to which the ordering time and ordering quantity for spare parts are identified. Finally, a case study is presented to illustrate the methods of identifying the ordering time and ordering number of engine-driven pumps through forecasting the failure time and failure number, followed by a discussion on the impact of various fleet sizes on prediction results. This method has the potential to decide the ordering time and quantity of spare parts when a new aircraft fleet is established. - Highlights: • A modeling framework of ordering spare parts for a new fleet is proposed. • Models for ordering time and number are established based on two-sample prediction. • The computation of future failure time is simplified using Newtonian binomial law. • Comparison of the first failure time PDFs is used to identify process parameters. • Identification methods for spare parts are validated by Engine Driven Pump case study.

  19. Dual processing model of medical decision-making

    2012-01-01

    Background Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. Methods We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. Results We show that physician’s beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. Conclusions We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical

  20. Dual processing model of medical decision-making.

    Djulbegovic, Benjamin; Hozo, Iztok; Beckstead, Jason; Tsalatsanis, Athanasios; Pauker, Stephen G

    2012-09-03

    Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. We show that physician's beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker's threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the

  1. Dual processing model of medical decision-making

    Djulbegovic Benjamin

    2012-09-01

    Full Text Available Abstract Background Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I and/or an analytical, deliberative (system II processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. Methods We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. Results We show that physician’s beliefs about whether to treat at higher (lower probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. Conclusions We have developed the first dual processing model of medical decision-making that has potential to

  2. An Anisotropic Hardening Model for Springback Prediction

    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.

  3. An Anisotropic Hardening Model for Springback Prediction

    Zeng, Danielle; Xia, Z. Cedric

    2005-01-01

    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

  4. Computational neurorehabilitation: modeling plasticity and learning to predict recovery.

    Reinkensmeyer, David J; Burdet, Etienne; Casadio, Maura; Krakauer, John W; Kwakkel, Gert; Lang, Catherine E; Swinnen, Stephan P; Ward, Nick S; Schweighofer, Nicolas

    2016-04-30

    Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

  5. Stochastic Watershed Models for Risk Based Decision Making

    Vogel, R. M.

    2017-12-01

    Over half a century ago, the Harvard Water Program introduced the field of operational or synthetic hydrology providing stochastic streamflow models (SSMs), which could generate ensembles of synthetic streamflow traces useful for hydrologic risk management. The application of SSMs, based on streamflow observations alone, revolutionized water resources planning activities, yet has fallen out of favor due, in part, to their inability to account for the now nearly ubiquitous anthropogenic influences on streamflow. This commentary advances the modern equivalent of SSMs, termed `stochastic watershed models' (SWMs) useful as input to nearly all modern risk based water resource decision making approaches. SWMs are deterministic watershed models implemented using stochastic meteorological series, model parameters and model errors, to generate ensembles of streamflow traces that represent the variability in possible future streamflows. SWMs combine deterministic watershed models, which are ideally suited to accounting for anthropogenic influences, with recent developments in uncertainty analysis and principles of stochastic simulation

  6. Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding.

    Zhang, Xianguo; Huang, Tiejun; Tian, Yonghong; Gao, Wen

    2014-02-01

    The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.

  7. Web tools for predictive toxicology model building.

    Jeliazkova, Nina

    2012-07-01

    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  8. Risk Decision Making Model for Reservoir Floodwater resources Utilization

    Huang, X.

    2017-12-01

    Floodwater resources utilization(FRU) can alleviate the shortage of water resources, but there are risks. In order to safely and efficiently utilize the floodwater resources, it is necessary to study the risk of reservoir FRU. In this paper, the risk rate of exceeding the design flood water level and the risk rate of exceeding safety discharge are estimated. Based on the principle of the minimum risk and the maximum benefit of FRU, a multi-objective risk decision making model for FRU is constructed. Probability theory and mathematical statistics method is selected to calculate the risk rate; C-D production function method and emergy analysis method is selected to calculate the risk benefit; the risk loss is related to flood inundation area and unit area loss; the multi-objective decision making problem of the model is solved by the constraint method. Taking the Shilianghe reservoir in Jiangsu Province as an example, the optimal equilibrium solution of FRU of the Shilianghe reservoir is found by using the risk decision making model, and the validity and applicability of the model are verified.

  9. Predictions of models for environmental radiological assessment

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando

    2011-01-01

    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 137 Cs and 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)

  10. Domestic appliances energy optimization with model predictive control

    Rodrigues, E.M.G.; Godina, R.; Pouresmaeil, E.; Ferreira, J.R.; Catalão, J.P.S.

    2017-01-01

    Highlights: • An alternative power management control for home appliances that require thermal regulation is presented. • A Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat. • Problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. • A modulation scheme of a two-level Model Predictive Control signal as an interface block is presented. • The implementation costs in home appliances with thermal regulation requirements are reduced. - Abstract: A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new

  11. A Predictive Maintenance Model for Railway Tracks

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

    2015-01-01

    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......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...

  12. Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment.

    McMeekin, Peter; Flynn, Darren; Ford, Gary A; Rodgers, Helen; Gray, Jo; Thomson, Richard G

    2015-11-11

    Individualised prediction of outcomes can support clinical and shared decision making. This paper describes the building of such a model to predict outcomes with and without intravenous thrombolysis treatment following ischaemic stroke. A decision analytic model (DAM) was constructed to establish the likely balance of benefits and risks of treating acute ischaemic stroke with thrombolysis. Probability of independence, (modified Rankin score mRS ≤ 2), dependence (mRS 3 to 5) and death at three months post-stroke was based on a calibrated version of the Stroke-Thrombolytic Predictive Instrument using data from routinely treated stroke patients in the Safe Implementation of Treatments in Stroke (SITS-UK) registry. Predictions in untreated patients were validated using data from the Virtual International Stroke Trials Archive (VISTA). The probability of symptomatic intracerebral haemorrhage in treated patients was incorporated using a scoring model from Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) data. The model predicts probabilities of haemorrhage, death, independence and dependence at 3-months, with and without thrombolysis, as a function of 13 patient characteristics. Calibration (and inclusion of additional predictors) of the Stroke-Thrombolytic Predictive Instrument (S-TPI) addressed issues of under and over prediction. Validation with VISTA data confirmed that assumptions about treatment effect were just. The C-statistics for independence and death in treated patients in the DAM were 0.793 and 0.771 respectively, and 0.776 for independence in untreated patients from VISTA. We have produced a DAM that provides an estimation of the likely benefits and risks of thrombolysis for individual patients, which has subsequently been embedded in a computerised decision aid to support better decision-making and informed consent.

  13. Effective modelling for predictive analytics in data science ...

    Effective modelling for predictive analytics in data science. ... the nearabsence of empirical or factual predictive analytics in the mainstream research going on ... Keywords: Predictive Analytics, Big Data, Business Intelligence, Project Planning.

  14. Environmental indicators and international models for making decision

    Polanco, Camilo

    2006-01-01

    The last international features proposed by the Organization for Economic Cooperation Development (OECD) and United Nations (UN) are analyzed in the use of the environmental indicators, in typology, selection criteria, and models, for organizing the information for management, environmental performance, and decision making. The advantages and disadvantages of each model are analyzed, as well as their environmental index characteristics. The analyzed models are Pressure - State - Response (PSR) and its conceptual developments: Driving Force - State Response (DSR), Driving Force - Pressure - State - Impact - Response (DPSIR), Model- Flow-Quality (MFQ), Pressure - State - Impact - Effect - Response (PSIER), and, finally, Pressure-State - Impact - Effect - Response - Management (PSIERM). The use of one or another model will depend on the quality of the available information, as well as on the proposed objectives

  15. Unicriterion Model: A Qualitative Decision Making Method That Promotes Ethics

    Fernando Guilherme Silvano Lobo Pimentel

    2011-06-01

    Full Text Available Management decision making methods frequently adopt quantitativemodels of several criteria that bypass the question of whysome criteria are considered more important than others, whichmakes more difficult the task of delivering a transparent viewof preference structure priorities that might promote ethics andlearning and serve as a basis for future decisions. To tackle thisparticular shortcoming of usual methods, an alternative qualitativemethodology of aggregating preferences based on the rankingof criteria is proposed. Such an approach delivers a simpleand transparent model for the solution of each preference conflictfaced during the management decision making process. Themethod proceeds by breaking the decision problem into ‘two criteria– two alternatives’ scenarios, and translating the problem ofchoice between alternatives to a problem of choice between criteriawhenever appropriate. The unicriterion model method is illustratedby its application in a car purchase and a house purchasedecision problem.

  16. Social influence and perceptual decision making: a diffusion model analysis.

    Germar, Markus; Schlemmer, Alexander; Krug, Kristine; Voss, Andreas; Mojzisch, Andreas

    2014-02-01

    Classic studies on social influence used simple perceptual decision-making tasks to examine how the opinions of others change individuals' judgments. Since then, one of the most fundamental questions in social psychology has been whether social influence can alter basic perceptual processes. To address this issue, we used a diffusion model analysis. Diffusion models provide a stochastic approach for separating the cognitive processes underlying speeded binary decisions. Following this approach, our study is the first to disentangle whether social influence on decision making is due to altering the uptake of available sensory information or due to shifting the decision criteria. In two experiments, we found consistent evidence for the idea that social influence alters the uptake of available sensory evidence. By contrast, participants did not adjust their decision criteria.

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

    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.

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

    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

  19. Combining GPS measurements and IRI model predictions

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.

    2002-01-01

    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  20. The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.

    Roche, Daniel B; Buenavista, Maria T; Tetchner, Stuart J; McGuffin, Liam J

    2011-07-01

    The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence. Our guiding principles behind the server development were as follows: (i) to provide a simple unified resource that makes our prediction software accessible to all and (ii) to produce integrated output for predictions that can be easily interpreted. The output for predictions is presented as a simple table that summarizes all results graphically via plots and annotated 3D models. The raw machine readable data files for each set of predictions are also provided for developers, which comply with the Critical Assessment of Methods for Protein Structure Prediction (CASP) data standards. The server comprises an integrated suite of five novel methods: nFOLD4, for tertiary structure prediction; ModFOLD 3.0, for model quality assessment; DISOclust 2.0, for disorder prediction; DomFOLD 2.0 for domain prediction; and FunFOLD 1.0, for ligand binding site prediction. Predictions from the IntFOLD server were found to be competitive in several categories in the recent CASP9 experiment. The IntFOLD server is available at the following web site: http://www.reading.ac.uk/bioinf/IntFOLD/.

  1. Validating predictions from climate envelope models.

    James I Watling

    Full Text Available 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

  2. Validating predictions from climate envelope models

    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.

  3. Towards a Decision Making Model for City Break Travel

    Dunne, Gerard; Flanagan, Sheila; Buckley, Joan

    2011-01-01

    Purpose The purpose of this paper is to examine the city break travel decision and in particular to develop a decision making model that reflects the characteristics of this type of trip taking. Method The research follows a sequential mixed methods approach consisting of two phases. Phase One involves a quantitative survey of 1,000 visitors to Dublin, from which city break and non city break visitor cohorts are separated and compared. Phase Two entails a qualitative analysis (involvin...

  4. A MODEL OF STUDENTS’ UNIVERSITY DECISION-MAKING BEHAVIOR

    Ionela MANIU; George C. MANIU

    2014-01-01

    Over the last decade the higher education institutional framework suffered a major transformation:the increasing influence of market competition on academic life - “marketization”.Consequently, HEI attention is increasingly focused on attracting high quality (human) resources and students. Such context demands a deeper understanding of students’ decision making process for HEI. Literature on higher education management provides a large number of models, which attempt to provide a...

  5. Mathematical models for indoor radon prediction

    Malanca, A.; Pessina, V.; Dallara, G.

    1995-01-01

    It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

  6. Towards predictive models for transitionally rough surfaces

    Abderrahaman-Elena, Nabil; Garcia-Mayoral, Ricardo

    2017-11-01

    We analyze and model the previously presented decomposition for flow variables in DNS of turbulence over transitionally rough surfaces. The flow is decomposed into two contributions: one produced by the overlying turbulence, which has no footprint of the surface texture, and one induced by the roughness, which is essentially the time-averaged flow around the surface obstacles, but modulated in amplitude by the first component. The roughness-induced component closely resembles the laminar steady flow around the roughness elements at the same non-dimensional roughness size. For small - yet transitionally rough - textures, the roughness-free component is essentially the same as over a smooth wall. Based on these findings, we propose predictive models for the onset of the transitionally rough regime. Project supported by the Engineering and Physical Sciences Research Council (EPSRC).

  7. Decision-Making Theories and Models: A Discussion of Rational and Psychological Decision-Making Theories and Models: The Search for a Cultural-Ethical Decision-Making Model

    Oliveira, Arnaldo

    2007-01-01

    This paper examines rational and psychological decision-making models. Descriptive and normative methodologies such as attribution theory, schema theory, prospect theory, ambiguity model, game theory, and expected utility theory are discussed. The definition of culture is reviewed, and the relationship between culture and decision making is also highlighted as many organizations use a cultural-ethical decision-making model.

  8. Losing a dime with a satisfied mind: positive affect predicts less search in sequential decision making.

    von Helversen, Bettina; Mata, Rui

    2012-12-01

    We investigated the contribution of cognitive ability and affect to age differences in sequential decision making by asking younger and older adults to shop for items in a computerized sequential decision-making task. Older adults performed poorly compared to younger adults partly due to searching too few options. An analysis of the decision process with a formal model suggested that older adults set lower thresholds for accepting an option than younger participants. Further analyses suggested that positive affect, but not fluid abilities, was related to search in the sequential decision task. A second study that manipulated affect in younger adults supported the causal role of affect: Increased positive affect lowered the initial threshold for accepting an attractive option. In sum, our results suggest that positive affect is a key factor determining search in sequential decision making. Consequently, increased positive affect in older age may contribute to poorer sequential decisions by leading to insufficient search. 2013 APA, all rights reserved

  9. Resource-estimation models and predicted discovery

    Hill, G.W.

    1982-01-01

    Resources have been estimated by predictive extrapolation from past discovery experience, by analogy with better explored regions, or by inference from evidence of depletion of targets for exploration. Changes in technology and new insights into geological mechanisms have occurred sufficiently often in the long run to form part of the pattern of mature discovery experience. The criterion, that a meaningful resource estimate needs an objective measure of its precision or degree of uncertainty, excludes 'estimates' based solely on expert opinion. This is illustrated by development of error measures for several persuasive models of discovery and production of oil and gas in USA, both annually and in terms of increasing exploration effort. Appropriate generalizations of the models resolve many points of controversy. This is illustrated using two USA data sets describing discovery of oil and of U 3 O 8 ; the latter set highlights an inadequacy of available official data. Review of the oil-discovery data set provides a warrant for adjusting the time-series prediction to a higher resource figure for USA petroleum. (author)

  10. Testing Process Predictions of Models of Risky Choice: A Quantitative Model Comparison Approach

    Thorsten ePachur

    2013-09-01

    Full Text Available This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or nonlinear functions thereof and the separate evaluation of risky options (expectation models. Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models. We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter, Gigerenzer, & Hertwig, 2006, and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up and direction of search (i.e., gamble-wise vs. reason-wise. In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly; acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988 called similarity. In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies.

  11. Testing process predictions of models of risky choice: a quantitative model comparison approach

    Pachur, Thorsten; Hertwig, Ralph; Gigerenzer, Gerd; Brandstätter, Eduard

    2013-01-01

    This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies. PMID:24151472

  12. Comparison of pause predictions of two sequence-dependent transcription models

    Bai, Lu; Wang, Michelle D

    2010-01-01

    Two recent theoretical models, Bai et al (2004, 2007) and Tadigotla et al (2006), formulated thermodynamic explanations of sequence-dependent transcription pausing by RNA polymerase (RNAP). The two models differ in some basic assumptions and therefore make different yet overlapping predictions for pause locations, and different predictions on pause kinetics and mechanisms. Here we present a comprehensive comparison of the two models. We show that while they have comparable predictive power of pause locations at low NTP concentrations, the Bai et al model is more accurate than Tadigotla et al at higher NTP concentrations. The pausing kinetics predicted by Bai et al is also consistent with time-course transcription reactions, while Tadigotla et al is unsuited for this type of kinetic prediction. More importantly, the two models in general predict different pausing mechanisms even for the same pausing sites, and the Bai et al model provides an explanation more consistent with recent single molecule observations

  13. Prediction of pipeline corrosion rate based on grey Markov models

    Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin

    2009-01-01

    Based on the model that combined by grey model and Markov model, the prediction of corrosion rate of nuclear power pipeline was studied. Works were done to improve the grey model, and the optimization unbiased grey model was obtained. This new model was used to predict the tendency of corrosion rate, and the Markov model was used to predict the residual errors. In order to improve the prediction precision, rolling operation method was used in these prediction processes. The results indicate that the improvement to the grey model is effective and the prediction precision of the new model combined by the optimization unbiased grey model and Markov model is better, and the use of rolling operation method may improve the prediction precision further. (authors)

  14. Our calibrated model has poor predictive value: An example from the petroleum industry

    Carter, J.N.; Ballester, P.J.; Tavassoli, Z.; King, P.R.

    2006-01-01

    It is often assumed that once a model has been calibrated to measurements then it will have some level of predictive capability, although this may be limited. If the model does not have predictive capability then the assumption is that the model needs to be improved in some way. Using an example from the petroleum industry, we show that cases can exit where calibrated models have limited predictive capability. This occurs even when there is no modelling error present. It is also shown that the introduction of a small modelling error can make it impossible to obtain any models with useful predictive capability. We have been unable to find ways of identifying which calibrated models will have some predictive capacity and those which will not

  15. Our calibrated model has poor predictive value: An example from the petroleum industry

    Carter, J.N. [Department of Earth Science and Engineering, Imperial College, London (United Kingdom)]. E-mail: j.n.carter@ic.ac.uk; Ballester, P.J. [Department of Earth Science and Engineering, Imperial College, London (United Kingdom); Tavassoli, Z. [Department of Earth Science and Engineering, Imperial College, London (United Kingdom); King, P.R. [Department of Earth Science and Engineering, Imperial College, London (United Kingdom)

    2006-10-15

    It is often assumed that once a model has been calibrated to measurements then it will have some level of predictive capability, although this may be limited. If the model does not have predictive capability then the assumption is that the model needs to be improved in some way. Using an example from the petroleum industry, we show that cases can exit where calibrated models have limited predictive capability. This occurs even when there is no modelling error present. It is also shown that the introduction of a small modelling error can make it impossible to obtain any models with useful predictive capability. We have been unable to find ways of identifying which calibrated models will have some predictive capacity and those which will not.

  16. A judgment and decision-making model for plant behavior.

    Karban, Richard; Orrock, John L

    2018-06-12

    Recently plant biologists have documented that plants, like animals, engage in many activities that can be considered as behaviors, although plant biologists currently lack a conceptual framework to understand these processes. Borrowing the well-established framework developed by psychologists, we propose that plant behaviors can be constructively modeled by identifying four distinct components: 1) a cue or stimulus that provides information, 2) a judgment whereby the plant perceives and processes this informative cue, 3) a decision whereby the plant chooses among several options based on their relative costs and benefits, and 4) action. Judgment for plants can be determined empirically by monitoring signaling associated with electrical, calcium, or hormonal fluxes. Decision-making can be evaluated empirically by monitoring gene expression or differential allocation of resources. We provide examples of the utility of this judgment and decision-making framework by considering cases in which plants either successfully or unsuccessfully induced resistance against attacking herbivores. Separating judgment from decision-making suggests new analytical paradigms (i.e., Bayesian methods for judgment and economic utility models for decision-making). Following this framework, we propose an experimental approach to plant behavior that explicitly manipulates the stimuli provided to plants, uses plants that vary in sensory abilities, and examines how environmental context affects plant responses. The concepts and approaches that follow from the judgment and decision-making framework can shape how we study and understand plant-herbivore interactions, biological invasions, plant responses to climate change, and the susceptibility of plants to evolutionary traps. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  17. Simple model for multiple-choice collective decision making.

    Lee, Ching Hua; Lucas, Andrew

    2014-11-01

    We describe a simple model of heterogeneous, interacting agents making decisions between n≥2 discrete choices. For a special class of interactions, our model is the mean field description of random field Potts-like models and is effectively solved by finding the extrema of the average energy E per agent. In these cases, by studying the propagation of decision changes via avalanches, we argue that macroscopic dynamics is well captured by a gradient flow along E. We focus on the permutation symmetric case, where all n choices are (on average) the same, and spontaneous symmetry breaking (SSB) arises purely from cooperative social interactions. As examples, we show that bimodal heterogeneity naturally provides a mechanism for the spontaneous formation of hierarchies between decisions and that SSB is a preferred instability to discontinuous phase transitions between two symmetric points. Beyond the mean field limit, exponentially many stable equilibria emerge when we place this model on a graph of finite mean degree. We conclude with speculation on decision making with persistent collective oscillations. Throughout the paper, we emphasize analogies between methods of solution to our model and common intuition from diverse areas of physics, including statistical physics and electromagnetism.

  18. Dinucleotide controlled null models for comparative RNA gene prediction

    Gesell Tanja

    2008-05-01

    Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require

  19. Dinucleotide controlled null models for comparative RNA gene prediction.

    Gesell, Tanja; Washietl, Stefan

    2008-05-27

    Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz

  20. Development of a Predictive Model for Induction Success of Labour

    Cristina Pruenza

    2018-03-01

    Full Text Available Induction of the labour process is an extraordinarily common procedure used in some pregnancies. Obstetricians face the need to end a pregnancy, for medical reasons usually (maternal or fetal requirements or less frequently, social (elective inductions for convenience. The success of induction procedure is conditioned by a multitude of maternal and fetal variables that appear before or during pregnancy or birth process, with a low predictive value. The failure of the induction process involves performing a caesarean section. This project arises from the clinical need to resolve a situation of uncertainty that occurs frequently in our clinical practice. Since the weight of clinical variables is not adequately weighted, we consider very interesting to know a priori the possibility of success of induction to dismiss those inductions with high probability of failure, avoiding unnecessary procedures or postponing end if possible. We developed a predictive model of induced labour success as a support tool in clinical decision making. Improve the predictability of a successful induction is one of the current challenges of Obstetrics because of its negative impact. The identification of those patients with high chances of failure, will allow us to offer them better care improving their health outcomes (adverse perinatal outcomes for mother and newborn, costs (medication, hospitalization, qualified staff and patient perceived quality. Therefore a Clinical Decision Support System was developed to give support to the Obstetricians. In this article, we had proposed a robust method to explore and model a source of clinical information with the purpose of obtaining all possible knowledge. Generally, in classification models are difficult to know the contribution that each attribute provides to the model. We had worked in this direction to offer transparency to models that may be considered as black boxes. The positive results obtained from both the

  1. An Operational Model for the Prediction of Jet Blast

    2012-01-09

    This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...

  2. Risk assessment and remedial policy evaluation using predictive modeling

    Linkov, L.; Schell, W.R.

    1996-01-01

    As a result of nuclear industry operation and accidents, large areas of natural ecosystems have been contaminated by radionuclides and toxic metals. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions, not only economic costs but also human and environmental risk assessments are desired. This paper introduces a general framework for risk assessment and remedial policy evaluation using predictive modeling. Ecological risk assessment requires evaluation of the radionuclide distribution in ecosystems. The FORESTPATH model is used for predicting the radionuclide fate in forest compartments after deposition as well as for evaluating the efficiency of remedial policies. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer annually. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus and a better methodology is required. In conclusion, FORESTPATH modeling framework could have wide applications in environmental remediation of radionuclides and toxic metals as well as in dose reconstruction and, risk-assessment

  3. Planning a Stigmatized Nonvisible Illness Disclosure: Applying the Disclosure Decision-Making Model

    Choi, Soe Yoon; Venetis, Maria K.; Greene, Kathryn; Magsamen-Conrad, Kate; Checton, Maria G.; Banerjee, Smita C.

    2016-01-01

    This study applied the disclosure decision-making model (DD-MM) to explore how individuals plan to disclose nonvisible illness (Study 1), compared to planning to disclose personal information (Study 2). Study 1 showed that perceived stigma from the illness negatively predicted disclosure efficacy; closeness predicted anticipated response (i.e., provision of support) although it did not influence disclosure efficacy; disclosure efficacy led to reduced planning, with planning leading to scheduling. Study 2 demonstrated that when information was considered to be intimate, it negatively influenced disclosure efficacy. Unlike the model with stigma (Study 1), closeness positively predicted both anticipated response and disclosure efficacy. The rest of the hypothesized relationships showed a similar pattern to Study 1: disclosure efficacy reduced planning, which then positively influenced scheduling. Implications of understanding stages of planning for stigmatized information are discussed. PMID:27662447

  4. Dynamics of Entropy in Quantum-like Model of Decision Making

    Basieva, Irina; Khrennikov, Andrei; Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu

    2011-03-01

    We present a quantum-like model of decision making in games of the Prisoner's Dilemma type. By this model the brain processes information by using representation of mental states in complex Hilbert space. Driven by the master equation the mental state of a player, say Alice, approaches an equilibrium point in the space of density matrices. By using this equilibrium point Alice determines her mixed (i.e., probabilistic) strategy with respect to Bob. Thus our model is a model of thinking through decoherence of initially pure mental state. Decoherence is induced by interaction with memory and external environment. In this paper we study (numerically) dynamics of quantum entropy of Alice's state in the process of decision making. Our analysis demonstrates that this dynamics depends nontrivially on the initial state of Alice's mind on her own actions and her prediction state (for possible actions of Bob.)

  5. a mathematical model for predicting output in an oilfield in the niger

    eobe

    resultant model was found to have greater utility in predicting oil field output as it produced less residual. The ... decision making by the oilfield manager is facilitated by reliable ... Scaling laws from percolation theory was used to predict oilfield ...

  6. Model Predictive Vibration Control Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures

    Takács, Gergely

    2012-01-01

    Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: ·         the implementation of ...

  7. Data driven propulsion system weight prediction model

    Gerth, Richard J.

    1994-10-01

    The objective of the research was to develop a method to predict the weight of paper engines, i.e., engines that are in the early stages of development. The impetus for the project was the Single Stage To Orbit (SSTO) project, where engineers need to evaluate alternative engine designs. Since the SSTO is a performance driven project the performance models for alternative designs were well understood. The next tradeoff is weight. Since it is known that engine weight varies with thrust levels, a model is required that would allow discrimination between engines that produce the same thrust. Above all, the model had to be rooted in data with assumptions that could be justified based on the data. The general approach was to collect data on as many existing engines as possible and build a statistical model of the engines weight as a function of various component performance parameters. This was considered a reasonable level to begin the project because the data would be readily available, and it would be at the level of most paper engines, prior to detailed component design.

  8. Model Predictive Control based on Finite Impulse Response Models

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

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

    Xiaojun Guo

    2014-01-01

    Full Text Available 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.

  10. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) : The TRIPOD statement

    Collins, G. S.; Reitsma, J. B.; Altman, D. G.; Moons, K. G. M.

    2015-01-01

    Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming

  11. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) : the TRIPOD Statement

    Collins, Gary S.; Reitsma, Johannes B.; Altman, Douglas G.; Moons, Karel G. M.

    2015-01-01

    Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present ( diagnostic models) or that a specific event will occur in the future ( prognostic models), to inform their decision making. However, the overwhelming

  12. Multi-level decision making models, methods and applications

    Zhang, Guangquan; Gao, Ya

    2015-01-01

    This monograph presents new developments in multi-level decision-making theory, technique and method in both modeling and solution issues. It especially presents how a decision support system can support managers in reaching a solution to a multi-level decision problem in practice. This monograph combines decision theories, methods, algorithms and applications effectively. It discusses in detail the models and solution algorithms of each issue of bi-level and tri-level decision-making, such as multi-leaders, multi-followers, multi-objectives, rule-set-based, and fuzzy parameters. Potential readers include organizational managers and practicing professionals, who can use the methods and software provided to solve their real decision problems; PhD students and researchers in the areas of bi-level and multi-level decision-making and decision support systems; students at an advanced undergraduate, master’s level in information systems, business administration, or the application of computer science.  

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

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

    2014-01-01

    Risky decision-making seems to be markedly disrupted in patients with chronic pain, probably due to the high cost that impose pain and negative mood on executive control functions. Patients’ behavioral performance on decision-making tasks such as the Iowa Gambling Task (IGT) is characterized by selecting cards more frequently from disadvantageous than from advantageous decks, and by switching often between competing responses in comparison with healthy controls (HCs). In the present study, we developed a simple heuristic model to simulate individuals’ choice behavior by varying the level of decision randomness and the importance given to gains and losses. The findings revealed that the model was able to differentiate the behavioral performance of patients with chronic pain and HCs at the group, as well as at the individual level. The best fit of the model in patients with chronic pain was yielded when decisions were not based on previous choices and when gains were considered more relevant than losses. By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains. In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis. PMID:25136301

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

    Leonardo Emanuel Hess

    2014-08-01

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

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

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

    2014-01-01

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

  16. Methodology for Designing Models Predicting Success of Infertility Treatment

    Alireza Zarinara; Mohammad Mahdi Akhondi; Hojjat Zeraati; Koorsh Kamali; Kazem Mohammad

    2016-01-01

    Abstract Background: The prediction models for infertility treatment success have presented since 25 years ago. There are scientific principles for designing and applying the prediction models that is also used to predict the success rate of infertility treatment. The purpose of this study is to provide basic principles for designing the model to predic infertility treatment success. Materials and Methods: In this paper, the principles for developing predictive models are explained and...

  17. An Integrative Model of Dynamic Strategy-Making

    Andersen, Torben Juul; Hallin, Carina Antonia; Li, Xin

    . We adopt the frame of complementary Yin-Yang elements and Zhong Yong balance to explain the time bound interaction between these opposing yet complementary strategy-making mechanisms where tradeoffs and synergies are balanced across hierarchical levels. The model outlines how the interaction between......The organizational capacity to cope with unexpected changes remains a fundamental challenge in strategy as global competition and technological innovation increase environmental uncertainty. Whereas conventional strategy-making often is conceived as a sequential linear process, we see it as a non......-linear interaction between top-down and bottom-up mechanisms dealing with multiple actions taken throughout the organization over time. It is driven by intension but with a flexible balance between centralized (planned) and decentralized (spontaneous) activities where strategy formulation and implementation interact...

  18. Effects of dynamic agricultural decision making in an ecohydrological model

    Reichenau, T. G.; Krimly, T.; Schneider, K.

    2012-04-01

    Due to various interdependencies between the cycles of water, carbon, nitrogen, and energy the impacts of climate change on ecohydrological systems can only be investigated in an integrative way. Furthermore, the human intervention in the environmental processes makes the system even more complex. On the one hand human impact affects natural systems. On the other hand the changing natural systems have a feedback on human decision making. One of the most important examples for this kind of interaction can be found in the agricultural sector. Management dates (planting, fertilization, harvesting) are chosen based on meteorological conditions and yield expectations. A faster development of crops under a warmer climate causes shorter cropping seasons. The choice of crops depends on their profitability, which is mainly determined by market prizes, the agro-political framework, and the (climate dependent) crop yield. This study investigates these relations for the district Günzburg located in the Upper Danube catchment in southern Germany. The modeling system DANUBIA was used to perform dynamically coupled simulations of plant growth, surface and soil hydrological processes, soil nitrogen transformations, and agricultural decision making. The agro-economic model simulates decisions on management dates (based on meteorological conditions and the crops' development state), on fertilization intensities (based on yield expectations), and on choice of crops (based on profitability). The environmental models included in DANUBIA are to a great extent process based to enable its use in a climate change scenario context. Scenario model runs until 2058 were performed using an IPCC A1B forcing. In consecutive runs, dynamic crop management, dynamic crop selection, and a changing agro-political framework were activated. Effects of these model features on hydrological and ecological variables were analyzed separately by comparing the results to a model run with constant crop

  19. Finite Unification: Theory, Models and Predictions

    Heinemeyer, S; Zoupanos, G

    2011-01-01

    All-loop Finite Unified Theories (FUTs) are very interesting N=1 supersymmetric Grand Unified Theories (GUTs) realising an old field theory dream, and moreover have a remarkable predictive power due to the required reduction of couplings. The reduction of the dimensionless couplings in N=1 GUTs is achieved by searching for renormalization group invariant (RGI) relations among them holding beyond the unification scale. Finiteness results from the fact that there exist RGI relations among dimensional couplings that guarantee the vanishing of all beta-functions in certain N=1 GUTs even to all orders. Furthermore developments in the soft supersymmetry breaking sector of N=1 GUTs and FUTs lead to exact RGI relations, i.e. reduction of couplings, in this dimensionful sector of the theory, too. Based on the above theoretical framework phenomenologically consistent FUTs have been constructed. Here we review FUT models based on the SU(5) and SU(3)^3 gauge groups and their predictions. Of particular interest is the Hig...

  20. Revised predictive equations for salt intrusion modelling in estuaries

    Gisen, J.I.A.; Savenije, H.H.G.; Nijzink, R.C.

    2015-01-01

    For one-dimensional salt intrusion models to be predictive, we need predictive equations to link model parameters to observable hydraulic and geometric variables. The one-dimensional model of Savenije (1993b) made use of predictive equations for the Van der Burgh coefficient $K$ and the dispersion

  1. Neutrino nucleosynthesis in supernovae: Shell model predictions

    Haxton, W.C.

    1989-01-01

    Almost all of the 3 · 10 53 ergs liberated in a core collapse supernova is radiated as neutrinos by the cooling neutron star. I will argue that these neutrinos interact with nuclei in the ejected shells of the supernovae to produce new elements. It appears that this nucleosynthesis mechanism is responsible for the galactic abundances of 7 Li, 11 B, 19 F, 138 La, and 180 Ta, and contributes significantly to the abundances of about 15 other light nuclei. I discuss shell model predictions for the charged and neutral current allowed and first-forbidden responses of the parent nuclei, as well as the spallation processes that produce the new elements. 18 refs., 1 fig., 1 tab

  2. Hierarchical Model Predictive Control for Resource Distribution

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

    2010-01-01

    units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising......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...... on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology...

  3. Model predictive control of a wind turbine modelled in Simpack

    Jassmann, U; Matzke, D; Reiter, M; Abel, D; Berroth, J; Schelenz, R; Jacobs, G

    2014-01-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine

  4. Model predictive control of a wind turbine modelled in Simpack

    Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.

    2014-06-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to

  5. The Urgent Need for Improved Climate Models and Predictions

    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.

  6. Poisson Mixture Regression Models for Heart Disease Prediction.

    Mufudza, Chipo; Erol, Hamza

    2016-01-01

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

  7. Use of nonlinear dose-effect models to predict consequences

    Seiler, F.A.; Alvarez, J.L.

    1996-01-01

    The linear dose-effect relationship was introduced as a model for the induction of cancer from exposure to nuclear radiation. Subsequently, it has been used by analogy to assess the risk of chemical carcinogens also. Recently, however, the model for radiation carcinogenesis has come increasingly under attack because its calculations contradict the epidemiological data, such as cancer in atomic bomb survivors. Even so, its proponents vigorously defend it, often using arguments that are not so much scientific as a mix of scientific, societal, and often political arguments. At least in part, the resilience of the linear model is due to two convenient properties that are exclusive to linearity: First, the risk of an event is determined solely by the event dose; second, the total risk of a population group depends only on the total population dose. In reality, the linear model has been conclusively falsified; i.e., it has been shown to make wrong predictions, and once this fact is generally realized, the scientific method calls for a new paradigm model. As all alternative models are by necessity nonlinear, all the convenient properties of the linear model are invalid, and calculational procedures have to be used that are appropriate for nonlinear models

  8. Decision Making under Uncertainty: A Neural Model based on Partially Observable Markov Decision Processes

    Rajesh P N Rao

    2010-11-01

    Full Text Available A fundamental problem faced by animals is learning to select actions based on noisy sensory information and incomplete knowledge of the world. It has been suggested that the brain engages in Bayesian inference during perception but how such probabilistic representations are used to select actions has remained unclear. Here we propose a neural model of action selection and decision making based on the theory of partially observable Markov decision processes (POMDPs. Actions are selected based not on a single optimal estimate of state but on the posterior distribution over states (the belief state. We show how such a model provides a unified framework for explaining experimental results in decision making that involve both information gathering and overt actions. The model utilizes temporal difference (TD learning for maximizing expected reward. The resulting neural architecture posits an active role for the neocortex in belief computation while ascribing a role to the basal ganglia in belief representation, value computation, and action selection. When applied to the random dots motion discrimination task, model neurons representing belief exhibit responses similar to those of LIP neurons in primate neocortex. The appropriate threshold for switching from information gathering to overt actions emerges naturally during reward maximization. Additionally, the time course of reward prediction error in the model shares similarities with dopaminergic responses in the basal ganglia during the random dots task. For tasks with a deadline, the model learns a decision making strategy that changes with elapsed time, predicting a collapsing decision threshold consistent with some experimental studies. The model provides a new framework for understanding neural decision making and suggests an important role for interactions between the neocortex and the basal ganglia in learning the mapping between probabilistic sensory representations and actions that maximize

  9. Effects of subordinate feedback to the supervisor and participation in decision-making in the prediction of organizational support.

    1992-03-01

    The present study tested the hypothesis that participation in decision-making (PDM) and perceived effectiveness of subordinate feedback to the supervisor would contribute unique variance in the prediction of perceptions of organizational support. In ...

  10. Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.

    Fechner, Hanna B; Pachur, Thorsten; Schooler, Lael J; Mehlhorn, Katja; Battal, Ceren; Volz, Kirsten G; Borst, Jelmer P

    2016-12-01

    How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Context-dependent decision-making: a simple Bayesian model.

    Lloyd, Kevin; Leslie, David S

    2013-05-06

    Many phenomena in animal learning can be explained by a context-learning process whereby an animal learns about different patterns of relationship between environmental variables. Differentiating between such environmental regimes or 'contexts' allows an animal to rapidly adapt its behaviour when context changes occur. The current work views animals as making sequential inferences about current context identity in a world assumed to be relatively stable but also capable of rapid switches to previously observed or entirely new contexts. We describe a novel decision-making model in which contexts are assumed to follow a Chinese restaurant process with inertia and full Bayesian inference is approximated by a sequential-sampling scheme in which only a single hypothesis about current context is maintained. Actions are selected via Thompson sampling, allowing uncertainty in parameters to drive exploration in a straightforward manner. The model is tested on simple two-alternative choice problems with switching reinforcement schedules and the results compared with rat behavioural data from a number of T-maze studies. The model successfully replicates a number of important behavioural effects: spontaneous recovery, the effect of partial reinforcement on extinction and reversal, the overtraining reversal effect, and serial reversal-learning effects.

  12. Predictable chaos: a review of the effects of emotions on attention, memory and decision making.

    LeBlanc, Vicki R; McConnell, Meghan M; Monteiro, Sandra D

    2015-03-01

    Healthcare practice and education are highly emotional endeavors. While this is recognized by educators and researchers seeking to develop interventions aimed at improving wellness in health professionals and at providing them with skills to deal with emotional interpersonal situations, the field of health professions education has largely ignored the role that emotions play on cognitive processes. The purpose of this review is to provide an introduction to the broader field of emotions, with the goal of better understanding the integral relationship between emotions and cognitive processes. Individuals, at any given time, are in an emotional state. This emotional state influences how they perceive the world around them, what they recall from it, as well as the decisions they make. Rather than treating emotions as undesirable forces that wreak havoc on the rational being, the field of health professions education could be enriched by a greater understanding of how these emotions can shape cognitive processes in increasingly predictable ways.

  13. Emotion regulation and risk taking: predicting risky choice in deliberative decision making.

    Panno, Angelo; Lauriola, Marco; Figner, Bernd

    2013-01-01

    Only very recently has research demonstrated that experimentally induced emotion regulation strategies (cognitive reappraisal and expressive suppression) affect risky choice (e.g., Heilman et al., 2010). However, it is unknown whether this effect also operates via habitual use of emotion regulation strategies in risky choice involving deliberative decision making. We investigated the role of habitual use of emotion regulation strategies in risky choice using the "cold" deliberative version of the Columbia Card Task (CCT; Figner et al., 2009). Fifty-three participants completed the Emotion Regulation Questionnaire (ERQ; Gross & John, 2003) and--one month later--the CCT and the PANAS. Greater habitual cognitive reappraisal use was related to increased risk taking, accompanied by decreased sensitivity to changes in probability and loss amount. Greater habitual expressive suppression use was related to decreased risk taking. The results show that habitual use of reappraisal and suppression strategies predict risk taking when decisions involve predominantly cognitive-deliberative processes.

  14. System dynamics models as decision-making tools in agritourism

    Jere Jakulin Tadeja

    2016-12-01

    Full Text Available Agritourism as a type of niche tourism is a complex and softly defined phaenomenon. The demands for fast and integrated decision regarding agritourism and its interconnections with environment, economy (investments, traffic and social factors (tourists is urgent. Many different methodologies and methods master softly structured questions and dilemmas with global and local properties. Here we present methods of systems thinking and system dynamics, which were first brought into force in the educational and training area in the form of different computer simulations and later as tools for decision-making and organisational re-engineering. We develop system dynamics models in order to present accuracy of methodology. These models are essentially simple and can serve only as describers of the activity of basic mutual influences among variables. We will pay the attention to the methodology for parameter model values determination and the so-called mental model. This one is the basis of causal connections among model variables. At the end, we restore a connection between qualitative and quantitative models in frame of system dynamics.

  15. Predictive integrated modelling for ITER scenarios

    Artaud, J.F.; Imbeaux, F.; Aniel, T.; Basiuk, V.; Eriksson, L.G.; Giruzzi, G.; Hoang, G.T.; Huysmans, G.; Joffrin, E.; Peysson, Y.; Schneider, M.; Thomas, P.

    2005-01-01

    The uncertainty on the prediction of ITER scenarios is evaluated. 2 transport models which have been extensively validated against the multi-machine database are used for the computation of the transport coefficients. The first model is GLF23, the second called Kiauto is a model in which the profile of dilution coefficient is a gyro Bohm-like analytical function, renormalized in order to get profiles consistent with a given global energy confinement scaling. The package of codes CRONOS is used, it gives access to the dynamics of the discharge and allows the study of interplay between heat transport, current diffusion and sources. The main motivation of this work is to study the influence of parameters such plasma current, heat, density, impurities and toroidal moment transport. We can draw the following conclusions: 1) the target Q = 10 can be obtained in ITER hybrid scenario at I p = 13 MA, using either the DS03 two terms scaling or the GLF23 model based on the same pedestal; 2) I p = 11.3 MA, Q = 10 can be reached only assuming a very peaked pressure profile and a low pedestal; 3) at fixed Greenwald fraction, Q increases with density peaking; 4) achieving a stationary q-profile with q > 1 requires a large non-inductive current fraction (80%) that could be provided by 20 to 40 MW of LHCD; and 5) owing to the high temperature the q-profile penetration is delayed and q = 1 is reached about 600 s in ITER hybrid scenario at I p = 13 MA, in the absence of active q-profile control. (A.C.)

  16. The application of the heuristic-systematic processing model to treatment decision making about prostate cancer.

    Steginga, Suzanne K; Occhipinti, Stefano

    2004-01-01

    The study investigated the utility of the Heuristic-Systematic Processing Model as a framework for the investigation of patient decision making. A total of 111 men recently diagnosed with localized prostate cancer were assessed using Verbal Protocol Analysis and self-report measures. Study variables included men's use of nonsystematic and systematic information processing, desire for involvement in decision making, and the individual differences of health locus of control, tolerance of ambiguity, and decision-related uncertainty. Most men (68%) preferred that decision making be shared equally between them and their doctor. Men's use of the expert opinion heuristic was related to men's verbal reports of decisional uncertainty and having a positive orientation to their doctor and medical care; a desire for greater involvement in decision making was predicted by a high internal locus of health control. Trends were observed for systematic information processing to increase when the heuristic strategy used was negatively affect laden and when men were uncertain about the probabilities for cure and side effects. There was a trend for decreased systematic processing when the expert opinion heuristic was used. Findings were consistent with the Heuristic-Systematic Processing Model and suggest that this model has utility for future research in applied decision making about health.

  17. Modeling, control and optimization of water systems systems engineering methods for control and decision making tasks

    2016-01-01

    This book provides essential background knowledge on the development of model-based real-world solutions in the field of control and decision making for water systems. It presents system engineering methods for modelling surface water and groundwater resources as well as water transportation systems (rivers, channels and pipelines). The models in turn provide information on both the water quantity (flow rates, water levels) of surface water and groundwater and on water quality. In addition, methods for modelling and predicting water demand are described. Sample applications of the models are presented, such as a water allocation decision support system for semi-arid regions, a multiple-criteria control model for run-of-river hydropower plants, and a supply network simulation for public services.

  18. Predicting Biological Information Flow in a Model Oxygen Minimum Zone

    Louca, S.; Hawley, A. K.; Katsev, S.; Beltran, M. T.; Bhatia, M. P.; Michiels, C.; Capelle, D.; Lavik, G.; Doebeli, M.; Crowe, S.; Hallam, S. J.

    2016-02-01

    Microbial activity drives marine biochemical fluxes and nutrient cycling at global scales. Geochemical measurements as well as molecular techniques such as metagenomics, metatranscriptomics and metaproteomics provide great insight into microbial activity. However, an integration of molecular and geochemical data into mechanistic biogeochemical models is still lacking. Recent work suggests that microbial metabolic pathways are, at the ecosystem level, strongly shaped by stoichiometric and energetic constraints. Hence, models rooted in fluxes of matter and energy may yield a holistic understanding of biogeochemistry. Furthermore, such pathway-centric models would allow a direct consolidation with meta'omic data. Here we present a pathway-centric biogeochemical model for the seasonal oxygen minimum zone in Saanich Inlet, a fjord off the coast of Vancouver Island. The model considers key dissimilatory nitrogen and sulfur fluxes, as well as the population dynamics of the genes that mediate them. By assuming a direct translation of biocatalyzed energy fluxes to biosynthesis rates, we make predictions about the distribution and activity of the corresponding genes. A comparison of the model to molecular measurements indicates that the model explains observed DNA, RNA, protein and cell depth profiles. This suggests that microbial activity in marine ecosystems such as oxygen minimum zones is well described by DNA abundance, which, in conjunction with geochemical constraints, determines pathway expression and process rates. Our work further demonstrates how meta'omic data can be mechanistically linked to environmental redox conditions and biogeochemical processes.

  19. On the use and potential use of seasonal to decadal climate predictions for decision-making in Europe

    Soares, Marta Bruno; Dessai, Suraje

    2014-05-01

    The need for climate information to help inform decision-making in sectors susceptible to climate events and impacts is widely recognised. In Europe, developments in the science and models underpinning the study of climate variability and change have led to an increased interest in seasonal to decadal climate predictions (S2DCP). While seasonal climate forecasts are now routinely produced operationally by a number of centres around the world, decadal climate predictions are still in its infancy restricted to the realm of research. Contrary to other regions of the world, where the use of these types of forecasts, particularly at seasonal timescales, has been pursued in recent years due to higher levels of predictability, little is known about the uptake and climate information needs of end-users regarding S2DCP in Europe. To fill this gap we conducted in-depth interviews with experts and decision-makers across a range of European sectors, a workshop with European climate services providers, and a systematic literature review on the use of S2DCP in Europe. This study is part of the EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale (EUPORIAS) project which aims to develop semi-operational prototypes of impact prediction systems in Europe on seasonal to decadal timescales. We found that the emerging landscape of users and potential users of S2DCP in Europe is complex and heterogeneous. Differences in S2DCP information needs across and within organisations and sectors are largely underpinned by factors such as the institutional and regulatory context of the organisations, the plethora of activities and decision-making processes involved, the level of expertise and capacity of the users, and the availability of resources within the organisations. In addition, although the use of S2DCP across Europe is still fairly limited, particular sectors such as agriculture, health, energy, water, (re)insurance, and transport are taking the lead on

  20. A Hybrid Multiple Criteria Decision Making Model for Supplier Selection

    Chung-Min Wu

    2013-01-01

    Full Text Available The sustainable supplier selection would be the vital part in the management of a sustainable supply chain. In this study, a hybrid multiple criteria decision making (MCDM model is applied to select optimal supplier. The fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP is then used to obtain their weights. To avoid calculation and additional pairwise comparisons of ANP, a technique for order preference by similarity to ideal solution (TOPSIS is used to rank the alternatives. The use of a combination of the fuzzy Delphi method, ANP, and TOPSIS, proposing an MCDM model for supplier selection, and applying these to a real case are the unique features of this study.

  1. Cognitive balanced model: a conceptual scheme of diagnostic decision making.

    Lucchiari, Claudio; Pravettoni, Gabriella

    2012-02-01

    Diagnostic reasoning is a critical aspect of clinical performance, having a high impact on quality and safety of care. Although diagnosis is fundamental in medicine, we still have a poor understanding of the factors that determine its course. According to traditional understanding, all information used in diagnostic reasoning is objective and logically driven. However, these conditions are not always met. Although we would be less likely to make an inaccurate diagnosis when following rational decision making, as described by normative models, the real diagnostic process works in a different way. Recent work has described the major cognitive biases in medicine as well as a number of strategies for reducing them, collectively called debiasing techniques. However, advances have encountered obstacles in achieving implementation into clinical practice. While traditional understanding of clinical reasoning has failed to consider contextual factors, most debiasing techniques seem to fail in raising sound and safer medical praxis. Technological solutions, being data driven, are fundamental in increasing care safety, but they need to consider human factors. Thus, balanced models, cognitive driven and technology based, are needed in day-to-day applications to actually improve the diagnostic process. The purpose of this article, then, is to provide insight into cognitive influences that have resulted in wrong, delayed or missed diagnosis. Using a cognitive approach, we describe the basis of medical error, with particular emphasis on diagnostic error. We then propose a conceptual scheme of the diagnostic process by the use of fuzzy cognitive maps. © 2011 Blackwell Publishing Ltd.

  2. Using multi-species occupancy models in structured decision making on managed lands

    Sauer, John R.; Blank, Peter J.; Zipkin, Elise F.; Fallon, Jane E.; Fallon, Frederick W.

    2013-01-01

    Land managers must balance the needs of a variety of species when manipulating habitats. Structured decision making provides a systematic means of defining choices and choosing among alternative management options; implementation of a structured decision requires quantitative approaches to predicting consequences of management on the relevant species. Multi-species occupancy models provide a convenient framework for making structured decisions when the management objective is focused on a collection of species. These models use replicate survey data that are often collected on managed lands. Occupancy can be modeled for each species as a function of habitat and other environmental features, and Bayesian methods allow for estimation and prediction of collective responses of groups of species to alternative scenarios of habitat management. We provide an example of this approach using data from breeding bird surveys conducted in 2008 at the Patuxent Research Refuge in Laurel, Maryland, evaluating the effects of eliminating meadow and wetland habitats on scrub-successional and woodland-breeding bird species using summed total occupancy of species as an objective function. Removal of meadows and wetlands decreased value of an objective function based on scrub-successional species by 23.3% (95% CI: 20.3–26.5), but caused only a 2% (0.5, 3.5) increase in value of an objective function based on woodland species, documenting differential effects of elimination of meadows and wetlands on these groups of breeding birds. This approach provides a useful quantitative tool for managers interested in structured decision making.

  3. A Predictive Model for Yeast Cell Polarization in Pheromone Gradients.

    Muller, Nicolas; Piel, Matthieu; Calvez, Vincent; Voituriez, Raphaël; Gonçalves-Sá, Joana; Guo, Chin-Lin; Jiang, Xingyu; Murray, Andrew; Meunier, Nicolas

    2016-04-01

    Budding yeast cells exist in two mating types, a and α, which use peptide pheromones to communicate with each other during mating. Mating depends on the ability of cells to polarize up pheromone gradients, but cells also respond to spatially uniform fields of pheromone by polarizing along a single axis. We used quantitative measurements of the response of a cells to α-factor to produce a predictive model of yeast polarization towards a pheromone gradient. We found that cells make a sharp transition between budding cycles and mating induced polarization and that they detect pheromone gradients accurately only over a narrow range of pheromone concentrations corresponding to this transition. We fit all the parameters of the mathematical model by using quantitative data on spontaneous polarization in uniform pheromone concentration. Once these parameters have been computed, and without any further fit, our model quantitatively predicts the yeast cell response to pheromone gradient providing an important step toward understanding how cells communicate with each other.

  4. Relevance of behavioral and social models to the study of consumer energy decision making and behavior

    Burns, B.A.

    1980-11-01

    This report reviews social and behavioral science models and techniques for their possible use in understanding and predicting consumer energy decision making and behaviors. A number of models and techniques have been developed that address different aspects of the decision process, use different theoretical bases and approaches, and have been aimed at different audiences. Three major areas of discussion were selected: (1) models of adaptation to social change, (2) decision making and choice, and (3) diffusion of innovation. Within these three areas, the contributions of psychologists, sociologists, economists, marketing researchers, and others were reviewed. Five primary components of the models were identified and compared. The components are: (1) situational characteristics, (2) product characteristics, (3) individual characteristics, (4) social influences, and (5) the interaction or decision rules. The explicit use of behavioral and social science models in energy decision-making and behavior studies has been limited. Examples are given of a small number of energy studies which applied and tested existing models in studying the adoption of energy conservation behaviors and technologies, and solar technology.

  5. Machine learning models in breast cancer survival prediction.

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

    2016-01-01

    accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.

  6. Foundation Settlement Prediction Based on a Novel NGM Model

    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.

  7. Hidden Hearing Loss and Computational Models of the Auditory Pathway: Predicting Speech Intelligibility Decline

    2016-11-28

    Title: Hidden Hearing Loss and Computational Models of the Auditory Pathway: Predicting Speech Intelligibility Decline Christopher J. Smalt...representation of speech intelligibility in noise. The auditory-periphery model of Zilany et al. (JASA 2009,2014) is used to make predictions of...auditory nerve (AN) responses to speech stimuli under a variety of difficult listening conditions. The resulting cochlear neurogram, a spectrogram

  8. Nonconvex model predictive control for commercial refrigeration

    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.

  9. Predictive Modelling of Heavy Metals in Urban Lakes

    Lindström, Martin

    2000-01-01

    Heavy metals are well-known environmental pollutants. In this thesis predictive models for heavy metals in urban lakes are discussed and new models presented. The base of predictive modelling is empirical data from field investigations of many ecosystems covering a wide range of ecosystem characteristics. Predictive models focus on the variabilities among lakes and processes controlling the major metal fluxes. Sediment and water data for this study were collected from ten small lakes in the ...

  10. Personality Makes a Difference: Attachment Orientation Moderates Theory of Planned Behavior Prediction of Cardiac Medication Adherence.

    Peleg, Shira; Vilchinsky, Noa; Fisher, William A; Khaskia, Abed; Mosseri, Morris

    2017-12-01

    To achieve a comprehensive understanding of patients' adherence to medication following acute coronary syndrome (ACS), we assessed the possible moderating role played by attachment orientation on the effects of attitudes, subjective norms, and perceived behavioral control (PBC), as derived from the Theory of Planned Behavior (TPB; Ajzen, 1991), on intention and reported adherence. A prospective longitudinal design was employed. During hospitalization, ACS male patients (N = 106) completed a set of self-report questionnaires including sociodemographic variables, attachment orientation, and measures of TPB constructs. Six months post-discharge, 90 participants completed a questionnaire measuring adherence to medication. Attachment orientations moderated some of the predictions of the TPB model. PBC predicted intention and reported adherence, but these associations were found to be significant only among individuals with lower, as opposed to higher, attachment anxiety. The association between attitudes and intention was stronger among individuals with higher, as opposed to lower, attachment anxiety. Only among individuals with higher attachment avoidance, subjective norms were negatively associated with intention to take medication. Cognitive variables appear to explain both adherence intention and behavior, but differently, depending on individuals' attachment orientations. Integrating personality and cognitive models may prove effective in understanding patients' health behaviors. © 2016 Wiley Periodicals, Inc.

  11. Climate change and plant distribution: local models predict high-elevation persistence

    Randin, Christophe F.; Engler, Robin; Normand, Signe

    2009-01-01

    Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range, and spatial resolution of data used in making these models, different rates...

  12. Obligations, internalization, and excuse making: integrating the triangle model and self-determination theory.

    Sheldon, Kennon M; Schachtman, Todd R

    2007-04-01

    Schlenker's triangle model (Schlenker, Britt, Pennington, Murphy, & Doherty, 1994, Schlenker, Pontari, & Christopher, 2001) identifies three excuses people use to avoid taking responsibility after failure: that one had no control in the situation, that the obligation was unclear, and that it was not really one's obligation. Three retrospective studies tested the presumed negative association between excuse making and responsibility taking. The studies also examined the effects of self-determination theory's concept of motivational internalization (Deci & Ryan, 2000) upon these variables. A complex but replicable pattern emerged, such that responsibility taking and motivational internalization correlated with adaptive outcomes such as future commitment and positive expectancy and excuse making did not. Of particular interest, perceiving that the person levying the obligation internalized motivation predicted responsibility taking, in all three studies. Implications for the triangle model, as well as for theories of maturity and personality development, are considered.

  13. Strong interactions between learned helplessness and risky decision-making in a rat gambling model.

    Nobrega, José N; Hedayatmofidi, Parisa S; Lobo, Daniela S

    2016-11-18

    Risky decision-making is characteristic of depression and of addictive disorders, including pathological gambling. However it is not clear whether a propensity to risky choices predisposes to depressive symptoms or whether the converse is the case. Here we tested the hypothesis that rats showing risky decision-making in a rat gambling task (rGT) would be more prone to depressive-like behaviour in the learned helplessness (LH) model. Results showed that baseline rGT choice behaviour did not predict escape deficits in the LH protocol. In contrast, exposure to the LH protocol resulted in a significant increase in risky rGT choices on retest. Unexpectedly, control rats subjected only to escapable stress in the LH protocol showed a subsequent decrease in riskier rGT choices. Further analyses indicated that the LH protocol affected primarily rats with high baseline levels of risky choices and that among these it had opposite effects in rats exposed to LH-inducing stress compared to rats exposed only to the escape trials. Together these findings suggest that while baseline risky decision making may not predict LH behaviour it interacts strongly with LH conditions in modulating subsequent decision-making behaviour. The suggested possibility that stress controllability may be a key factor should be further investigated.

  14. Decision-Making Involvement and Prediction of Adherence in Youth With Type 1 Diabetes: A Cohort Sequential Study.

    Miller, Victoria A; Jawad, Abbas F

    2018-05-17

    To assess developmental trajectories of decision-making involvement (DMI), defined as the ways in which parents and children engage each other in decision-making about illness management, in youth with type 1 diabetes (T1D) and examine the effects of DMI on levels of and changes in adherence with age. Participants included 117 youth with T1D, enrolled at ages 8-16 years and assessed five times over 2 years. The cohort sequential design allowed for the approximation of the longitudinal curve from age 8 to 19 from overlapping cohort segments. Children and parents completed the Decision-Making Involvement Scale, which yields subscales for different aspects of DMI, and a self-report adherence questionnaire. Mixed-effects growth curve modeling was used for analysis, with longitudinal measures nested within participant and participants nested within cohort. Most aspects of DMI (Parent Express, Parent Seek, Child Express, and Joint) increased with child age; scores on some child report subscales (Parent Express, Child Seek, and Joint) decreased after age 12-14 years. After accounting for age, Child Seek, Child Express, and Joint were associated with overall higher levels of adherence in both child (estimates = 0.08-0.13, p < .001) and parent (estimates = 0.07- 0.13, p < .01) report models, but they did not predict changes in adherence with age. These data suggest that helping children to be more proactive in T1D discussions, by encouraging them to express their opinions, share information, and solicit guidance from parents, is a potential target for interventions to enhance effective self-management.

  15. Qualitative modeling of the decision-making process using electrooculography.

    Zargari Marandi, Ramtin; Sabzpoushan, S H

    2015-12-01

    A novel method based on electrooculography (EOG) has been introduced in this work to study the decision-making process. An experiment was designed and implemented wherein subjects were asked to choose between two items from the same category that were presented within a limited time. The EOG and voice signals of the subjects were recorded during the experiment. A calibration task was performed to map the EOG signals to their corresponding gaze positions on the screen by using an artificial neural network. To analyze the data, 16 parameters were extracted from the response time and EOG signals of the subjects. Evaluation and comparison of the parameters, together with subjects' choices, revealed functional information. On the basis of this information, subjects switched their eye gazes between items about three times on average. We also found, according to statistical hypothesis testing-that is, a t test, t(10) = 71.62, SE = 1.25, p < .0001-that the correspondence rate of a subjects' gaze at the moment of selection with the selected item was significant. Ultimately, on the basis of these results, we propose a qualitative choice model for the decision-making task.

  16. Seasonal predictability of Kiremt rainfall in coupled general circulation models

    Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen

    2017-11-01

    The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

  17. Dual Processes in Decision Making and Developmental Neuroscience: A Fuzzy-Trace Model.

    Reyna, Valerie F; Brainerd, Charles J

    2011-09-01

    From Piaget to the present, traditional and dual-process theories have predicted improvement in reasoning from childhood to adulthood, and improvement has been observed. However, developmental reversals-that reasoning biases emerge with development -have also been observed in a growing list of paradigms. We explain how fuzzy-trace theory predicts both improvement and developmental reversals in reasoning and decision making. Drawing on research on logical and quantitative reasoning, as well as on risky decision making in the laboratory and in life, we illustrate how the same small set of theoretical principles apply to typical neurodevelopment, encompassing childhood, adolescence, and adulthood, and to neurological conditions such as autism and Alzheimer's disease. For example, framing effects-that risk preferences shift when the same decisions are phrases in terms of gains versus losses-emerge in early adolescence as gist-based intuition develops. In autistic individuals, who rely less on gist-based intuition and more on verbatim-based analysis, framing biases are attenuated (i.e., they outperform typically developing control subjects). In adults, simple manipulations based on fuzzy-trace theory can make framing effects appear and disappear depending on whether gist-based intuition or verbatim-based analysis is induced. These theoretical principles are summarized and integrated in a new mathematical model that specifies how dual modes of reasoning combine to produce predictable variability in performance. In particular, we show how the most popular and extensively studied model of decision making-prospect theory-can be derived from fuzzy-trace theory by combining analytical (verbatim-based) and intuitive (gist-based) processes.

  18. MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL

    Andrey Borisovich Nikolaev

    2017-09-01

    Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

  19. Predictive model for functional consequences of oral cavity tumour resections

    van Alphen, M.J.A.; Hageman, T.A.G.; Hageman, Tijmen Antoon Geert; Smeele, L.E.; Balm, Alfonsus Jacobus Maria; Balm, A.J.M.; van der Heijden, Ferdinand; Lemke, H.U.

    2013-01-01

    The prediction of functional consequences after treatment of large oral cavity tumours is mainly based on the size and location of the tumour. However, patient specific factors play an important role in the functional outcome, making the current predictions unreliable and subjective. An objective

  20. Assessing testamentary and decision-making capacity: Approaches and models.

    Purser, Kelly; Rosenfeld, Tuly

    2015-09-01

    The need for better and more accurate assessments of testamentary and decision-making capacity grows as Australian society ages and incidences of mentally disabling conditions increase. Capacity is a legal determination, but one on which medical opinion is increasingly being sought. The difficulties inherent within capacity assessments are exacerbated by the ad hoc approaches adopted by legal and medical professionals based on individual knowledge and skill, as well as the numerous assessment paradigms that exist. This can negatively affect the quality of assessments, and results in confusion as to the best way to assess capacity. This article begins by assessing the nature of capacity. The most common general assessment models used in Australia are then discussed, as are the practical challenges associated with capacity assessment. The article concludes by suggesting a way forward to satisfactorily assess legal capacity given the significant ramifications of getting it wrong.

  1. MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

    Son, S. W.; Lim, Y.; Kim, D.

    2017-12-01

    The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.

  2. Decision support system in Predicting the Best teacher with Multi Atribute Decesion Making Weighted Product (MADMWP Method

    Solikhun Solikhun

    2017-06-01

    Full Text Available Predicting of the best teacher in Indonesia aims to spur the development of the growth and improve the quality of the education. In this paper, the predicting  of the best teacher is implemented based on predefined criteria. To help the predicting process, a decision support system is needed. This paper employs Multi Atribute Decesion Making Weighted Product (MADMWP method. The result of this method is tested some teachers in  junior high school islamic boarding Al-Barokah school, Simalungun, North Sumatera, Indonesia. This system can be used to help in solving problems of the best teacher prediction.

  3. Improving Earth/Prediction Models to Improve Network Processing

    Wagner, G. S.

    2017-12-01

    The United States Atomic Energy Detection System (USAEDS) primaryseismic network consists of a relatively small number of arrays andthree-component stations. The relatively small number of stationsin the USAEDS primary network make it both necessary and feasibleto optimize both station and network processing.Station processing improvements include detector tuning effortsthat use Receiver Operator Characteristic (ROC) curves to helpjudiciously set acceptable Type 1 (false) vs. Type 2 (miss) errorrates. Other station processing improvements include the use ofempirical/historical observations and continuous background noisemeasurements to compute time-varying, maximum likelihood probabilityof detection thresholds.The USAEDS network processing software makes extensive use of theazimuth and slowness information provided by frequency-wavenumberanalysis at array sites, and polarization analysis at three-componentsites. Most of the improvements in USAEDS network processing aredue to improvements in the models used to predict azimuth, slowness,and probability of detection. Kriged travel-time, azimuth andslowness corrections-and associated uncertainties-are computedusing a ground truth database. Improvements in station processingand the use of improved models for azimuth, slowness, and probabilityof detection have led to significant improvements in USADES networkprocessing.

  4. An employer brand predictive model for talent attraction and retention

    Annelize Botha

    2011-11-01

    Full Text Available Orientation: In an ever shrinking global talent pool organisations use employer brand to attract and retain talent, however, in the absence of theoretical pointers, many organisations are losing out on a powerful business tool by not developing or maintaining their employer brand correctly. Research purpose: This study explores the current state of knowledge about employer brand and identifies the various employer brand building blocks which are conceptually integrated in a predictive model. Motivation for the study: The need for scientific progress though the accurate representation of a set of employer brand phenomena and propositions, which can be empirically tested, motivated this study. Research design, approach and method: This study was nonempirical in approach and searched for linkages between theoretical concepts by making use of relevant contextual data. Theoretical propositions which explain the identified linkages were developed for purpose of further empirical research. Main findings: Key findings suggested that employer brand is influenced by target group needs, a differentiated Employer Value Proposition (EVP, the people strategy, brand consistency, communication of the employer brand and measurement of Human Resources (HR employer branding efforts. Practical/managerial implications: The predictive model provides corporate leaders and their human resource functionaries a theoretical pointer relative to employer brand which could guide more effective talent attraction and retention decisions. Contribution/value add: This study adds to the small base of research available on employer brand and contributes to both scientific progress as well as an improved practical understanding of factors which influence employer brand.

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

    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.

  6. Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

    Morstyn, Thomas; Hredzak, Branislav; Aguilera, Ricardo P.

    2018-01-01

    , and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical......This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear $d$ – $q$ reference frame voltage...... feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real...

  7. Statistical Models for Predicting Threat Detection From Human Behavior

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

    2018-01-01

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

  8. Statistical Models for Predicting Threat Detection From Human Behavior

    Timothy Kelley

    2018-04-01

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

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

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

    2018-01-01

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

  10. Butterfly, Recurrence, and Predictability in Lorenz Models

    Shen, B. W.

    2017-12-01

    Over the span of 50 years, the original three-dimensional Lorenz model (3DLM; Lorenz,1963) and its high-dimensional versions (e.g., Shen 2014a and references therein) have been used for improving our understanding of the predictability of weather and climate with a focus on chaotic responses. Although the Lorenz studies focus on nonlinear processes and chaotic dynamics, people often apply a "linear" conceptual model to understand the nonlinear processes in the 3DLM. In this talk, we present examples to illustrate the common misunderstandings regarding butterfly effect and discuss the importance of solutions' recurrence and boundedness in the 3DLM and high-dimensional LMs. The first example is discussed with the following folklore that has been widely used as an analogy of the butterfly effect: "For want of a nail, the shoe was lost.For want of a shoe, the horse was lost.For want of a horse, the rider was lost.For want of a rider, the battle was lost.For want of a battle, the kingdom was lost.And all for the want of a horseshoe nail."However, in 2008, Prof. Lorenz stated that he did not feel that this verse described true chaos but that it better illustrated the simpler phenomenon of instability; and that the verse implicitly suggests that subsequent small events will not reverse the outcome (Lorenz, 2008). Lorenz's comments suggest that the verse neither describes negative (nonlinear) feedback nor indicates recurrence, the latter of which is required for the appearance of a butterfly pattern. The second example is to illustrate that the divergence of two nearby trajectories should be bounded and recurrent, as shown in Figure 1. Furthermore, we will discuss how high-dimensional LMs were derived to illustrate (1) negative nonlinear feedback that stabilizes the system within the five- and seven-dimensional LMs (5D and 7D LMs; Shen 2014a; 2015a; 2016); (2) positive nonlinear feedback that destabilizes the system within the 6D and 8D LMs (Shen 2015b; 2017); and (3

  11. Auditing predictive models : a case study in crop growth

    Metselaar, K.

    1999-01-01

    Methods were developed to assess and quantify the predictive quality of simulation models, with the intent to contribute to evaluation of model studies by non-scientists. In a case study, two models of different complexity, LINTUL and SUCROS87, were used to predict yield of forage maize

  12. Models for predicting compressive strength and water absorption of ...

    This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...

  13. Behavioural modelling of irrigation decision making under water scarcity

    Foster, T.; Brozovic, N.; Butler, A. P.

    2013-12-01

    Providing effective policy solutions to aquifer depletion caused by abstraction for irrigation is a key challenge for socio-hydrology. However, most crop production functions used in hydrological models do not capture the intraseasonal nature of irrigation planning, or the importance of well yield in land and water use decisions. Here we develop a method for determining stochastic intraseasonal water use that is based on observed farmer behaviour but is also theoretically consistent with dynamically optimal decision making. We use the model to (i) analyse the joint land and water use decision by farmers; (ii) to assess changes in behaviour and production risk in response to water scarcity; and (iii) to understand the limits of applicability of current methods in policy design. We develop a biophysical model of water-limited crop yield building on the AquaCrop model. The model is calibrated and applied to case studies of irrigated corn production in Nebraska and Texas. We run the model iteratively, using long-term climate records, to define two formulations of the crop-water production function: (i) the aggregate relationship between total seasonal irrigation and yield (typical of current approaches); and (ii) the stochastic response of yield and total seasonal irrigation to the choice of an intraseasonal soil moisture target and irrigated area. Irrigated area (the extensive margin decision) and per-area irrigation intensity (the intensive margin decision) are then calculated for different seasonal water restrictions (corresponding to regulatory policies) and well yield constraints on intraseasonal abstraction rates (corresponding to aquifer system limits). Profit- and utility-maximising decisions are determined assuming risk neutrality and varying degrees of risk aversion, respectively. Our results demonstrate that the formulation of the production function has a significant impact on the response to water scarcity. For low well yields, which are the major concern

  14. Reward rate optimization in two-alternative decision making: empirical tests of theoretical predictions.

    Simen, Patrick; Contreras, David; Buck, Cara; Hu, Peter; Holmes, Philip; Cohen, Jonathan D

    2009-12-01

    The drift-diffusion model (DDM) implements an optimal decision procedure for stationary, 2-alternative forced-choice tasks. The height of a decision threshold applied to accumulating information on each trial determines a speed-accuracy tradeoff (SAT) for the DDM, thereby accounting for a ubiquitous feature of human performance in speeded response tasks. However, little is known about how participants settle on particular tradeoffs. One possibility is that they select SATs that maximize a subjective rate of reward earned for performance. For the DDM, there exist unique, reward-rate-maximizing values for its threshold and starting point parameters in free-response tasks that reward correct responses (R. Bogacz, E. Brown, J. Moehlis, P. Holmes, & J. D. Cohen, 2006). These optimal values vary as a function of response-stimulus interval, prior stimulus probability, and relative reward magnitude for correct responses. We tested the resulting quantitative predictions regarding response time, accuracy, and response bias under these task manipulations and found that grouped data conformed well to the predictions of an optimally parameterized DDM.

  15. Model for nuclear proliferation resistance analysis using decision making tools

    Ko, Won Il; Kim, Ho Dong; Yang, Myung Seung

    2003-06-01

    The nuclear proliferation risks of nuclear fuel cycles is being considered as one of the most important factors in assessing advanced and innovative nuclear systems in GEN IV and INPRO program. They have been trying to find out an appropriate and reasonable method to evaluate quantitatively several nuclear energy system alternatives. Any reasonable methodology for integrated analysis of the proliferation resistance, however, has not yet been come out at this time. In this study, several decision making methods, which have been used in the situation of multiple objectives, are described in order to see if those can be appropriately used for proliferation resistance evaluation. Especially, the AHP model for quantitatively evaluating proliferation resistance is dealt with in more detail. The theoretical principle of the method and some examples for the proliferation resistance problem are described. For more efficient applications, a simple computer program for the AHP model is developed, and the usage of the program is introduced here in detail. We hope that the program developed in this study could be useful for quantitative analysis of the proliferation resistance involving multiple conflict criteria

  16. Model for nuclear proliferation resistance analysis using decision making tools

    Ko, Won Il; Kim, Ho Dong; Yang, Myung Seung

    2003-06-01

    The nuclear proliferation risks of nuclear fuel cycles is being considered as one of the most important factors in assessing advanced and innovative nuclear systems in GEN IV and INPRO program. They have been trying to find out an appropriate and reasonable method to evaluate quantitatively several nuclear energy system alternatives. Any reasonable methodology for integrated analysis of the proliferation resistance, however, has not yet been come out at this time. In this study, several decision making methods, which have been used in the situation of multiple objectives, are described in order to see if those can be appropriately used for proliferation resistance evaluation. Especially, the AHP model for quantitatively evaluating proliferation resistance is dealt with in more detail. The theoretical principle of the method and some examples for the proliferation resistance problem are described. For more efficient applications, a simple computer program for the AHP model is developed, and the usage of the program is introduced here in detail. We hope that the program developed in this study could be useful for quantitative analysis of the proliferation resistance involving multiple conflict criteria.

  17. Statistical and Machine Learning Models to Predict Programming Performance

    Bergin, Susan

    2006-01-01

    This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

  18. Making coarse grained polymer simulations quantitatively predictive for statics and dynamics

    Kremer, Kurt

    2010-03-01

    By combining input from short simulation runs of rather small systems with all atomistic details together with properly adapted coarse grained models we are able quantitatively predict static and especially dynamical properties of both pure polymer melts of long fully entangled but also of systems with low molecular weight additives. Comparisons to rather different experiments such as diffusion constant measurements or NMR relaxation experiments show a remarkable quantitative agreement without any adjustable parameter. Reintroduction of chemical details into the coarse grained trajectories allows the study of long time trajectories in all atomistic detail providing the opportunity for rather different means of data analysis. References: V. Harmandaris, K. Kremer, Macromolecules, in press (2009) V. Harmandaris et al, Macromolecules, 40, 7026 (2007) B. Hess, S. Leon, N. van der Vegt, K. Kremer, Soft Matter 2, 409 (2006) D. Fritz et al, Soft Matter 5, 4556 (2009)

  19. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    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...

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

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

    2018-05-01

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

  1. Stargardt disease: towards developing a model to predict phenotype.

    Heathfield, Laura; Lacerda, Miguel; Nossek, Christel; Roberts, Lisa; Ramesar, Rajkumar S

    2013-10-01

    Stargardt disease is an ABCA4-associated retinopathy, which generally follows an autosomal recessive inheritance pattern and is a frequent cause of macular degeneration in childhood. ABCA4 displays significant allelic heterogeneity whereby different mutations can cause retinal diseases with varying severity and age of onset. A genotype-phenotype model has been proposed linking ABCA4 mutations, purported ABCA4 functional protein activity and severity of disease, as measured by degree of visual loss and the age of onset. It has, however, been difficult to verify this model statistically in observational studies, as the number of individuals sharing any particular mutation combination is typically low. Seven founder mutations have been identified in a large number of Caucasian Afrikaner patients in South Africa, making it possible to test the genotype-phenotype model. A generalised linear model was developed to predict and assess the relative pathogenic contribution of the seven mutations to the age of onset of Stargardt disease. It is shown that the pathogenicity of an individual mutation can differ significantly depending on the genetic context in which it occurs. The results reported here may be used to identify suitable candidates for inclusion in clinical trials, as well as guide the genetic counselling of affected individuals and families.

  2. A new ensemble model for short term wind power prediction

    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...

  3. Testing the predictive power of nuclear mass models

    Mendoza-Temis, J.; Morales, I.; Barea, J.; Frank, A.; Hirsch, J.G.; Vieyra, J.C. Lopez; Van Isacker, P.; Velazquez, V.

    2008-01-01

    A number of tests are introduced which probe the ability of nuclear mass models to extrapolate. Three models are analyzed in detail: the liquid drop model, the liquid drop model plus empirical shell corrections and the Duflo-Zuker mass formula. If predicted nuclei are close to the fitted ones, average errors in predicted and fitted masses are similar. However, the challenge of predicting nuclear masses in a region stabilized by shell effects (e.g., the lead region) is far more difficult. The Duflo-Zuker mass formula emerges as a powerful predictive tool

  4. From Predictive Models to Instructional Policies

    Rollinson, Joseph; Brunskill, Emma

    2015-01-01

    At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…

  5. Regulator Loss Functions and Hierarchical Modeling for Safety Decision Making.

    Hatfield, Laura A; Baugh, Christine M; Azzone, Vanessa; Normand, Sharon-Lise T

    2017-07-01

    Regulators must act to protect the public when evidence indicates safety problems with medical devices. This requires complex tradeoffs among risks and benefits, which conventional safety surveillance methods do not incorporate. To combine explicit regulator loss functions with statistical evidence on medical device safety signals to improve decision making. In the Hospital Cost and Utilization Project National Inpatient Sample, we select pediatric inpatient admissions and identify adverse medical device events (AMDEs). We fit hierarchical Bayesian models to the annual hospital-level AMDE rates, accounting for patient and hospital characteristics. These models produce expected AMDE rates (a safety target), against which we compare the observed rates in a test year to compute a safety signal. We specify a set of loss functions that quantify the costs and benefits of each action as a function of the safety signal. We integrate the loss functions over the posterior distribution of the safety signal to obtain the posterior (Bayes) risk; the preferred action has the smallest Bayes risk. Using simulation and an analysis of AMDE data, we compare our minimum-risk decisions to a conventional Z score approach for classifying safety signals. The 2 rules produced different actions for nearly half of hospitals (45%). In the simulation, decisions that minimize Bayes risk outperform Z score-based decisions, even when the loss functions or hierarchical models are misspecified. Our method is sensitive to the choice of loss functions; eliciting quantitative inputs to the loss functions from regulators is challenging. A decision-theoretic approach to acting on safety signals is potentially promising but requires careful specification of loss functions in consultation with subject matter experts.

  6. Toward a Theoretical Model of Decision-Making and Resistance to Change among Higher Education Online Course Designers

    Dodd, Bucky J.

    2013-01-01

    Online course design is an emerging practice in higher education, yet few theoretical models currently exist to explain or predict how the diffusion of innovations occurs in this space. This study used a descriptive, quantitative survey research design to examine theoretical relationships between decision-making style and resistance to change…

  7. Modelling the behaviour of long-lived radionuclides in the Irish Sea - comparison of model predictions with field observations

    Kershaw, P.J.; Pentreath, R.J.; Gurbutt, P.A.; Woodhead, D.S.; Durance, J.A.; Camplin, W.C.

    1988-01-01

    A multi-compartmental box model of the Irish Sea has been developed to predict the distribution and radiological consequences of radionuclides discharged from the Sellafield reprocessing plant. The box structure was based on observations of radionuclide distributions in the sea bed and the water circulation was generated from extensive time-series data on 137 Cs concentrations in seawater. Measurements of naturally-occurring nuclides provided both data on the extent and rate of these processes and a means to validate the model assumptions. The model structure is briefly outlined, comparisons are made between model predictions and field observation, and some of the difficulties in making such comparisons are discussed. (author)

  8. The Complexity of Developmental Predictions from Dual Process Models

    Stanovich, Keith E.; West, Richard F.; Toplak, Maggie E.

    2011-01-01

    Drawing developmental predictions from dual-process theories is more complex than is commonly realized. Overly simplified predictions drawn from such models may lead to premature rejection of the dual process approach as one of many tools for understanding cognitive development. Misleading predictions can be avoided by paying attention to several…

  9. Sweat loss prediction using a multi-model approach.

    Xu, Xiaojiang; Santee, William R

    2011-07-01

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

  10. Comparisons of Faulting-Based Pavement Performance Prediction Models

    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.

  11. A Method for Driving Route Predictions Based on Hidden Markov Model

    Ning Ye

    2015-01-01

    Full Text Available We present a driving route prediction method that is based on Hidden Markov Model (HMM. This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

  12. Individual differences in bodily freezing predict emotional biases in decision making

    Ly, V.; Huys, Q.; Stins, J.F.; Roelofs, K.; Cools, R.

    2014-01-01

    Instrumental decision making has long been argued to be vulnerable to emotional responses. Literature on multiple decision making systems suggests that this emotional biasing might reflect effects of a system that regulates innately specified, evolutionarily preprogrammed responses. To test this

  13. East London Modified-Broset as Decision-Making Tool to Predict Seclusion in Psychiatric Intensive Care Units

    Loi, Felice; Marlowe, Karl

    2017-01-01

    Seclusion is a last resort intervention for management of aggressive behavior in psychiatric settings. There is no current objective and practical decision-making instrument for seclusion use on psychiatric wards. Our aim was to test the predictive and discriminatory characteristics of the East London Modified-Broset (ELMB), to delineate its decision-making profile for seclusion of adult psychiatric patients, and second to benchmark it against the psychometric properties of the Broset Violenc...

  14. Predicting individual differences in decision-making process from signature movement styles: an illustrative study of leaders

    Connors, Brenda L.; Rende, Richard; Colton, Timothy J.

    2013-01-01

    There has been a surge of interest in examining the utility of methods for capturing individual differences in decision-making style. We illustrate the potential offered by Movement Pattern Analysis (MPA), an observational methodology that has been used in business and by the US Department of Defense to record body movements that provide predictive insight into individual differences in decision-making motivations and actions. Twelve military officers participated in an intensive 2-h intervie...

  15. Models of Eucalypt phenology predict bat population flux.

    Giles, John R; Plowright, Raina K; Eby, Peggy; Peel, Alison J; McCallum, Hamish

    2016-10-01

    Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt-focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86-93% accuracy. Negative binomial models explained 43-53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt-based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar-based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology

  16. Dual Processes in Decision Making and Developmental Neuroscience: A Fuzzy-Trace Model

    Reyna, Valerie F.; Brainerd, Charles J.

    2011-01-01

    From Piaget to the present, traditional and dual-process theories have predicted improvement in reasoning from childhood to adulthood, and improvement has been observed. However, developmental reversals—that reasoning biases emerge with development —have also been observed in a growing list of paradigms. We explain how fuzzy-trace theory predicts both improvement and developmental reversals in reasoning and decision making. Drawing on research on logical and quantitative reasoning, as well as on risky decision making in the laboratory and in life, we illustrate how the same small set of theoretical principles apply to typical neurodevelopment, encompassing childhood, adolescence, and adulthood, and to neurological conditions such as autism and Alzheimer's disease. For example, framing effects—that risk preferences shift when the same decisions are phrases in terms of gains versus losses—emerge in early adolescence as gist-based intuition develops. In autistic individuals, who rely less on gist-based intuition and more on verbatim-based analysis, framing biases are attenuated (i.e., they outperform typically developing control subjects). In adults, simple manipulations based on fuzzy-trace theory can make framing effects appear and disappear depending on whether gist-based intuition or verbatim-based analysis is induced. These theoretical principles are summarized and integrated in a new mathematical model that specifies how dual modes of reasoning combine to produce predictable variability in performance. In particular, we show how the most popular and extensively studied model of decision making—prospect theory—can be derived from fuzzy-trace theory by combining analytical (verbatim-based) and intuitive (gist-based) processes. PMID:22096268

  17. Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task

    Cristóbal Moënne-Loccoz

    2017-09-01

    Full Text Available Our daily interaction with the world is plagued of situations in which we develop expertise through self-motivated repetition of the same task. In many of these interactions, and especially when dealing with computer and machine interfaces, we must deal with sequences of decisions and actions. For instance, when drawing cash from an ATM machine, choices are presented in a step-by-step fashion and a specific sequence of choices must be performed in order to produce the expected outcome. But, as we become experts in the use of such interfaces, is it possible to identify specific search and learning strategies? And if so, can we use this information to predict future actions? In addition to better understanding the cognitive processes underlying sequential decision making, this could allow building adaptive interfaces that can facilitate interaction at different moments of the learning curve. Here we tackle the question of modeling sequential decision-making behavior in a simple human-computer interface that instantiates a 4-level binary decision tree (BDT task. We record behavioral data from voluntary participants while they attempt to solve the task. Using a Hidden Markov Model-based approach that capitalizes on the hierarchical structure of behavior, we then model their performance during the interaction. Our results show that partitioning the problem space into a small set of hierarchically related stereotyped strategies can potentially capture a host of individual decision making policies. This allows us to follow how participants learn and develop expertise in the use of the interface. Moreover, using a Mixture of Experts based on these stereotyped strategies, the model is able to predict the behavior of participants that master the task.

  18. Making eco logic and models work : An integrative approach to lake ecosystem modelling

    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

  19. An expandable software model for collaborative decision making during the whole building life cycle

    Papamichael, K.; Pal, V.; Bourassa, N.; Loffeld, J.; Capeluto, G.

    2000-01-01

    Decisions throughout the life cycle of a building, from design through construction and commissioning to operation and demolition, require the involvement of multiple interested parties (e.g., architects, engineers, owners, occupants and facility managers). The performance of alternative designs and courses of action must be assessed with respect to multiple performance criteria, such as comfort, aesthetics, energy, cost and environmental impact. Several stand-alone computer tools are currently available that address specific performance issues during various stages of a building's life cycle. Some of these tools support collaboration by providing means for synchronous and asynchronous communications, performance simulations, and monitoring of a variety of performance parameters involved in decisions about a building during building operation. However, these tools are not linked in any way, so significant work is required to maintain and distribute information to all parties. In this paper we describe a software model that provides the data management and process control required for collaborative decision making throughout a building's life cycle. The requirements for the model are delineated addressing data and process needs for decision making at different stages of a building's life cycle. The software model meets these requirements and allows addition of any number of processes and support databases over time. What makes the model infinitely expandable is that it is a very generic conceptualization (or abstraction) of processes as relations among data. The software model supports multiple concurrent users, and facilitates discussion and debate leading to decision making. The software allows users to define rules and functions for automating tasks and alerting all participants to issues that need attention. It supports management of simulated as well as real data and continuously generates information useful for improving performance prediction and

  20. Modeling of Complex Life Cycle Prediction Based on Cell Division

    Fucheng Zhang

    2017-01-01

    Full Text Available Effective fault diagnosis and reasonable life expectancy are of great significance and practical engineering value for the safety, reliability, and maintenance cost of equipment and working environment. At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. Most of them need to be based on a large amount of data to achieve the problem. For this issue, we propose learning from the mechanism of cell division in the organism. We have established a moderate complexity of life prediction model across studying the complex multifactor correlation life model. In this paper, we model the life prediction of cell division. Experiments show that our model can effectively simulate the state of cell division. Through the model of reference, we will use it for the equipment of the complex life prediction.

  1. Predictive modeling and reducing cyclic variability in autoignition engines

    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.

  2. Optimization control of LNG regasification plant using Model Predictive Control

    Wahid, A.; Adicandra, F. F.

    2018-03-01

    Optimization of liquified natural gas (LNG) regasification plant is important to minimize costs, especially operational costs. Therefore, it is important to choose optimum LNG regasification plant design and maintaining the optimum operating conditions through the implementation of model predictive control (MPC). Optimal tuning parameter for MPC such as P (prediction horizon), M (control of the horizon) and T (sampling time) are achieved by using fine-tuning method. The optimal criterion for design is the minimum amount of energy used and for control is integral of square error (ISE). As a result, the optimum design is scheme 2 which is developed by Devold with an energy savings of 40%. To maintain the optimum conditions, required MPC with P, M and T as follows: tank storage pressure: 90, 2, 1; product pressure: 95, 2, 1; temperature vaporizer: 65, 2, 2; and temperature heater: 35, 6, 5, with ISE value at set point tracking respectively 0.99, 1792.78, 34.89 and 7.54, or improvement of control performance respectively 4.6%, 63.5%, 3.1% and 58.2% compared to PI controller performance. The energy savings that MPC controllers can make when there is a disturbance in temperature rise 1°C of sea water is 0.02 MW.

  3. Dynamic Simulation of Human Gait Model With Predictive Capability.

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

    2018-03-01

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

  4. The Predictive Accuracy of PREDICT : A Personalized Decision-Making Tool for Southeast Asian Women With Breast Cancer

    Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M.; Hartman, Mikael; Bhoo Pathy, N

    Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480

  5. Mathematical modelling approach to collective decision-making

    Zabzina, Natalia

    2017-01-01

    In everyday situations individuals make decisions. For example, a tourist usually chooses a crowded or recommended restaurant to have dinner. Perhaps it is an individual decision, but the observed pattern of decision-making is a collective phenomenon. Collective behaviour emerges from the local interactions that give rise to a complex pattern at the group level. In our example, the recommendations or simple copying the choices of others make a crowded restaurant even more crowded. The rules o...

  6. Predictive Modeling of Mechanical Properties of Welded Joints Based on Dynamic Fuzzy RBF Neural Network

    ZHANG Yongzhi

    2016-10-01

    Full Text Available A dynamic fuzzy RBF neural network model was built to predict the mechanical properties of welded joints, and the purpose of the model was to overcome the shortcomings of static neural networks including structural identification, dynamic sample training and learning algorithm. The structure and parameters of the model are no longer head of default, dynamic adaptive adjustment in the training, suitable for dynamic sample data for learning, learning algorithm introduces hierarchical learning and fuzzy rule pruning strategy, to accelerate the training speed of model and make the model more compact. Simulation of the model was carried out by using three kinds of thickness and different process TC4 titanium alloy TIG welding test data. The results show that the model has higher prediction accuracy, which is suitable for predicting the mechanical properties of welded joints, and has opened up a new way for the on-line control of the welding process.

  7. A Note on the Use of Mixture Models for Individual Prediction.

    Cole, Veronica T; Bauer, Daniel J

    Mixture models capture heterogeneity in data by decomposing the population into latent subgroups, each of which is governed by its own subgroup-specific set of parameters. Despite the flexibility and widespread use of these models, most applications have focused solely on making inferences for whole or sub-populations, rather than individual cases. The current article presents a general framework for computing marginal and conditional predicted values for individuals using mixture model results. These predicted values can be used to characterize covariate effects, examine the fit of the model for specific individuals, or forecast future observations from previous ones. Two empirical examples are provided to demonstrate the usefulness of individual predicted values in applications of mixture models. The first example examines the relative timing of initiation of substance use using a multiple event process survival mixture model whereas the second example evaluates changes in depressive symptoms over adolescence using a growth mixture model.

  8. Comparative Evaluation of Some Crop Yield Prediction Models ...

    A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...

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

    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.

  10. Modelling microbial interactions and food structure in predictive microbiology

    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

  11. Ocean wave prediction using numerical and neural network models

    Mandal, S.; Prabaharan, N.

    This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

  12. Ensemble models on palaeoclimate to predict India's groundwater challenge

    Partha Sarathi Datta

    2013-09-01

    Full Text Available In many parts of the world, freshwater crisis is largely due to increasing water consumption and pollution by rapidly growing population and aspirations for economic development, but, ascribed usually to the climate. However, limited understanding and knowledge gaps in the factors controlling climate and uncertainties in the climate models are unable to assess the probable impacts on water availability in tropical regions. In this context, review of ensemble models on δ18O and δD in rainfall and groundwater, 3H- and 14C- ages of groundwater and 14C- age of lakes sediments helped to reconstruct palaeoclimate and long-term recharge in the North-west India; and predict future groundwater challenge. The annual mean temperature trend indicates both warming/cooling in different parts of India in the past and during 1901–2010. Neither the GCMs (Global Climate Models nor the observational record indicates any significant change/increase in temperature and rainfall over the last century, and climate change during the last 1200 yrs BP. In much of the North-West region, deep groundwater renewal occurred from past humid climate, and shallow groundwater renewal from limited modern recharge over the past decades. To make water management to be more responsive to climate change, the gaps in the science of climate change need to be bridged.

  13. Model structural uncertainty quantification and hydrologic parameter and prediction error analysis using airborne electromagnetic data

    Minsley, B. J.; Christensen, Nikolaj Kruse; Christensen, Steen

    Model structure, or the spatial arrangement of subsurface lithological units, is fundamental to the hydrological behavior of Earth systems. Knowledge of geological model structure is critically important in order to make informed hydrological predictions and management decisions. Model structure...... is never perfectly known, however, and incorrect assumptions can be a significant source of error when making model predictions. We describe a systematic approach for quantifying model structural uncertainty that is based on the integration of sparse borehole observations and large-scale airborne...... electromagnetic (AEM) data. Our estimates of model structural uncertainty follow a Bayesian framework that accounts for both the uncertainties in geophysical parameter estimates given AEM data, and the uncertainties in the relationship between lithology and geophysical parameters. Using geostatistical sequential...

  14. A mathematical model for predicting earthquake occurrence ...

    We consider the continental crust under damage. We use the observed results of microseism in many seismic stations of the world which was established to study the time series of the activities of the continental crust with a view to predicting possible time of occurrence of earthquake. We consider microseism time series ...

  15. Model for predicting the injury severity score.

    Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi

    2015-07-01

    To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P  Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.

  16. Predicting Career Advancement with Structural Equation Modelling

    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…

  17. Interactions of age and cognitive functions in predicting decision making under risky conditions over the life span.

    Brand, Matthias; Schiebener, Johannes

    2013-01-01

    Little is known about how normal healthy aging affects decision-making competence. In this study 538 participants (age 18-80 years) performed the Game of Dice Task (GDT). Subsamples also performed the Iowa Gambling Task as well as tasks measuring logical thinking and executive functions. In a moderated regression analysis, the significant interaction between age and executive components indicates that older participants with good executive functioning perform well on the GDT, while older participants with reduced executive functions make more risky choices. The same pattern emerges for the interaction of age and logical thinking. Results demonstrate that age and cognitive functions act in concert in predicting the decision-making performance.

  18. Statistical model based gender prediction for targeted NGS clinical panels

    Palani Kannan Kandavel

    2017-12-01

    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  19. A predictive pilot model for STOL aircraft landing

    Kleinman, D. L.; Killingsworth, W. R.

    1974-01-01

    An optimal control approach has been used to model pilot performance during STOL flare and landing. The model is used to predict pilot landing performance for three STOL configurations, each having a different level of automatic control augmentation. Model predictions are compared with flight simulator data. It is concluded that the model can be effective design tool for studying analytically the effects of display modifications, different stability augmentation systems, and proposed changes in the landing area geometry.

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

    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.

  1. Pharmaceutical expenditure forecast model to support health policy decision making.

    Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project 'European Union (EU) Pharmaceutical expenditure forecast' - http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). A model was built to assess policy scenarios' impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate, and distribution. Reducing, even slightly, the prices of

  2. Pharmaceutical expenditure forecast model to support health policy decision making

    Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    Background and objective With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project ‘European Union (EU) Pharmaceutical expenditure forecast’ – http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Methods A model was built to assess policy scenarios’ impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Results Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Conclusions Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate

  3. Model-based uncertainty in species range prediction

    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...

  4. Wind turbine control and model predictive control for uncertain systems

    Thomsen, Sven Creutz

    as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...

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

    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...

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

    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.

  7. Models for predicting fuel consumption in sagebrush-dominated ecosystems

    Clinton S. Wright

    2013-01-01

    Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentate Nutt.) ecosystems....

  8. Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

    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

  9. A new, accurate predictive model for incident hypertension

    Völzke, Henry; Fung, Glenn; Ittermann, Till

    2013-01-01

    Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures.......Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures....

  10. Prediction models for successful external cephalic version: a systematic review

    Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M.; Molkenboer, Jan F. M.; van der Post, Joris A. M.; Mol, Ben W.; Kok, Marjolein

    2015-01-01

    To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015.

  11. Hidden Markov Model for quantitative prediction of snowfall

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  12. Mathematical model for dissolved oxygen prediction in Cirata ...

    This paper presents the implementation and performance of mathematical model to predict theconcentration of dissolved oxygen in Cirata Reservoir, West Java by using Artificial Neural Network (ANN). The simulation program was created using Visual Studio 2012 C# software with ANN model implemented in it. Prediction ...

  13. Econometric models for predicting confusion crop ratios

    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.

  14. Hybrid Decision Making: When Interpretable Models Collaborate With Black-Box Models

    Wang, Tong

    2018-01-01

    Interpretable machine learning models have received increasing interest in recent years, especially in domains where humans are involved in the decision-making process. However, the possible loss of the task performance for gaining interpretability is often inevitable. This performance downgrade puts practitioners in a dilemma of choosing between a top-performing black-box model with no explanations and an interpretable model with unsatisfying task performance. In this work, we propose a nove...

  15. Deriving the expected utility of a predictive model when the utilities are uncertain.

    Cooper, Gregory F; Visweswaran, Shyam

    2005-01-01

    Predictive models are often constructed from clinical databases with the goal of eventually helping make better clinical decisions. Evaluating models using decision theory is therefore natural. When constructing a model using statistical and machine learning methods, however, we are often uncertain about precisely how the model will be used. Thus, decision-independent measures of classification performance, such as the area under an ROC curve, are popular. As a complementary method of evaluation, we investigate techniques for deriving the expected utility of a model under uncertainty about the model's utilities. We demonstrate an example of the application of this approach to the evaluation of two models that diagnose coronary artery disease.

  16. PEEX Modelling Platform for Seamless Environmental Prediction

    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. Crisis Decision Making Through a Shared Integrative Negotiation Mental Model

    Van Santen, W.; Jonker, C.M.; Wijngaards, N.

    2009-01-01

    Decision making during crises takes place in (multi-agency) teams, in a bureaucratic political context. As a result, the common notion that during crises decision making should be done in line with a Command & Control structure is invalid. This paper shows that the best way for crisis decision

  18. Impact of modellers' decisions on hydrological a priori predictions

    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

  19. Economic sustainability in franchising: a model to predict franchisor success or failure

    Calderón Monge, Esther; Pastor Sanz, Ivan .; Huerta Zavala, Pilar Angélica

    2017-01-01

    As a business model, franchising makes a major contribution to gross domestic product (GDP). A model that predicts franchisor success or failure is therefore necessary to ensure economic sustainability. In this study, such a model was developed by applying Lasso regression to a sample of franchises operating between 2002 and 2013. For franchises with the highest likelihood of survival, the franchise fees and the ratio of company-owned to franchised outlets were suited to the age ...

  20. Adding propensity scores to pure prediction models fails to improve predictive performance

    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.

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

    Dowding, Kevin J.; Rutherford, Brian Milne

    2003-07-01

    analyses and hypothesis tests as a part of the validation step to provide feedback to analysts and modelers. Decisions on how to proceed in making model-based predictions are made based on these analyses together with the application requirements. Updating modifying and understanding the boundaries associated with the model are also assisted through this feedback. (4) We include a ''model supplement term'' when model problems are indicated. This term provides a (bias) correction to the model so that it will better match the experimental results and more accurately account for uncertainty. Presumably, as the models continue to develop and are used for future applications, the causes for these apparent biases will be identified and the need for this supplementary modeling will diminish. (5) We use a response-modeling approach for our predictions that allows for general types of prediction and for assessment of prediction uncertainty. This approach is demonstrated through a case study supporting the assessment of a weapons response when subjected to a hydrocarbon fuel fire. The foam decomposition model provides an important element of the response of a weapon system in this abnormal thermal environment. Rigid foam is used to encapsulate critical components in the weapon system providing the needed mechanical support as well as thermal isolation. Because the foam begins to decompose at temperatures above 250 C, modeling the decomposition is critical to assessing a weapons response. In the validation analysis it is indicated that the model tends to ''exaggerate'' the effect of temperature changes when compared to the experimental results. The data, however, are too few and to restricted in terms of experimental design to make confident statements regarding modeling problems. For illustration, we assume these indications are correct and compensate for this apparent bias by constructing a model supplement term for use in the model

  2. NOx PREDICTION FOR FBC BOILERS USING EMPIRICAL MODELS

    Jiří Štefanica

    2014-02-01

    Full Text Available Reliable prediction of NOx emissions can provide useful information for boiler design and fuel selection. Recently used kinetic prediction models for FBC boilers are overly complex and require large computing capacity. Even so, there are many uncertainties in the case of FBC boilers. An empirical modeling approach for NOx prediction has been used exclusively for PCC boilers. No reference is available for modifying this method for FBC conditions. This paper presents possible advantages of empirical modeling based prediction of NOx emissions for FBC boilers, together with a discussion of its limitations. Empirical models are reviewed, and are applied to operation data from FBC boilers used for combusting Czech lignite coal or coal-biomass mixtures. Modifications to the model are proposed in accordance with theoretical knowledge and prediction accuracy.

  3. Making the most of what we have: application of extrapolation approaches in wildlife transfer models

    Beresford, Nicholas A.; Barnett, Catherine L.; Wells, Claire [NERC Centre for Ecology and Hydrology, Lancaster Environment Center, Library Av., Bailrigg, Lancaster, LA1 4AP (United Kingdom); School of Environment and Life Sciences, University of Salford, Manchester, M4 4WT (United Kingdom); Wood, Michael D. [School of Environment and Life Sciences, University of Salford, Manchester, M4 4WT (United Kingdom); Vives i Batlle, Jordi [Belgian Nuclear Research Centre, Boeretang 200, 2400 Mol (Belgium); Brown, Justin E.; Hosseini, Ali [Norwegian Radiation Protection Authority, P.O. Box 55, N-1332 Oesteraas (Norway); Yankovich, Tamara L. [International Atomic Energy Agency, Vienna International Centre, 1400, Vienna (Austria); Bradshaw, Clare [Department of Ecology, Environment and Plant Sciences, Stockholm University, SE-10691 (Sweden); Willey, Neil [Centre for Research in Biosciences, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY (United Kingdom)

    2014-07-01

    Radiological environmental protection models need to predict the transfer of many radionuclides to a large number of organisms. There has been considerable development of transfer (predominantly concentration ratio) databases over the last decade. However, in reality it is unlikely we will ever have empirical data for all the species-radionuclide combinations which may need to be included in assessments. To provide default values for a number of existing models/frameworks various extrapolation approaches have been suggested (e.g. using data for a similar organism or element). This paper presents recent developments in two such extrapolation approaches, namely phylogeny and allometry. An evaluation of how extrapolation approaches have performed and the potential application of Bayesian statistics to make best use of available data will also be given. Using a Residual Maximum Likelihood (REML) mixed-model regression we initially analysed a dataset comprising 597 entries for 53 freshwater fish species from 67 sites to investigate if phylogenetic variation in transfer could be identified. The REML analysis generated an estimated mean value for each species on a common scale after taking account of the effect of the inter-site variation. Using an independent dataset, we tested the hypothesis that the REML model outputs could be used to predict radionuclide activity concentrations in other species from the results of a species which had been sampled at a specific site. The outputs of the REML analysis accurately predicted {sup 137}Cs activity concentrations in different species of fish from 27 lakes. Although initially investigated as an extrapolation approach the output of this work is a potential alternative to the highly site dependent concentration ratio model. We are currently applying this approach to a wider range of organism types and different ecosystems. An initial analysis of these results will be presented. The application of allometric, or mass

  4. Complex versus simple models: ion-channel cardiac toxicity prediction.

    Mistry, Hitesh B

    2018-01-01

    There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model B net was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the B net model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

  5. Complex versus simple models: ion-channel cardiac toxicity prediction

    Hitesh B. Mistry

    2018-02-01

    Full Text Available There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model Bnet was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the Bnet model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

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

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

    2012-01-01

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

  7. Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

    Kovalenko, Andriy

    2014-01-01

    Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology

  8. Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

    Kovalenko, Andriy

    2014-08-01

    Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology

  9. [Application of ARIMA model on prediction of malaria incidence].

    Jing, Xia; Hua-Xun, Zhang; Wen, Lin; Su-Jian, Pei; Ling-Cong, Sun; Xiao-Rong, Dong; Mu-Min, Cao; Dong-Ni, Wu; Shunxiang, Cai

    2016-01-29

    To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA). SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation. The model of ARIMA (1, 1, 1) (1, 1, 0) 12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable. The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.

  10. Planetary Suit Hip Bearing Model for Predicting Design vs. Performance

    Cowley, Matthew S.; Margerum, Sarah; Harvil, Lauren; Rajulu, Sudhakar

    2011-01-01

    Designing a planetary suit is very complex and often requires difficult trade-offs between performance, cost, mass, and system complexity. In order to verifying that new suit designs meet requirements, full prototypes must eventually be built and tested with human subjects. Using computer models early in the design phase of new hardware development can be advantageous, allowing virtual prototyping to take place. Having easily modifiable models of the suit hard sections may reduce the time it takes to make changes to the hardware designs and then to understand their impact on suit and human performance. A virtual design environment gives designers the ability to think outside the box and exhaust design possibilities before building and testing physical prototypes with human subjects. Reductions in prototyping and testing may eventually reduce development costs. This study is an attempt to develop computer models of the hard components of the suit with known physical characteristics, supplemented with human subject performance data. Objectives: The primary objective was to develop an articulating solid model of the Mark III hip bearings to be used for evaluating suit design performance of the hip joint. Methods: Solid models of a planetary prototype (Mark III) suit s hip bearings and brief section were reverse-engineered from the prototype. The performance of the models was then compared by evaluating the mobility performance differences between the nominal hardware configuration and hardware modifications. This was accomplished by gathering data from specific suited tasks. Subjects performed maximum flexion and abduction tasks while in a nominal suit bearing configuration and in three off-nominal configurations. Performance data for the hip were recorded using state-of-the-art motion capture technology. Results: The results demonstrate that solid models of planetary suit hard segments for use as a performance design tool is feasible. From a general trend perspective

  11. Sensorimotor learning biases choice behavior: a learning neural field model for decision making.

    Christian Klaes

    Full Text Available According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action

  12. Dynamics of Metabolism and Decision Making During Alcohol Consumption: Modeling and Analysis.

    Giraldo, Luis Felipe; Passino, Kevin M; Clapp, John D; Ruderman, Danielle

    2017-11-01

    Heavy alcohol consumption is considered an important public health issue in the United States as over 88 000 people die every year from alcohol-related causes. Research is being conducted to understand the etiology of alcohol consumption and to develop strategies to decrease high-risk consumption and its consequences, but there are still important gaps in determining the main factors that influence the consumption behaviors throughout the drinking event. There is a need for methodologies that allow us not only to identify such factors but also to have a comprehensive understanding of how they are connected and how they affect the dynamical evolution of a drinking event. In this paper, we use previous empirical findings from laboratory and field studies to build a mathematical model of the blood alcohol concentration dynamics in individuals that are in drinking events. We characterize these dynamics as the result of the interaction between a decision-making system and the metabolic process for alcohol. We provide a model of the metabolic process for arbitrary alcohol intake patterns and a characterization of the mechanisms that drive the decision-making process of a drinker during the drinking event. We use computational simulations and Lyapunov stability theory to analyze the effects of the parameters of the model on the blood alcohol concentration dynamics that are characterized. Also, we propose a methodology to inform the model using data collected in situ and to make estimations that provide additional information to the analysis. We show how this model allows us to analyze and predict previously observed behaviors, to design new approaches for the collection of data that improves the construction of the model, and help with the design of interventions.

  13. Poisson Mixture Regression Models for Heart Disease Prediction

    Erol, Hamza

    2016-01-01

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

  14. Predictive error dependencies when using pilot points and singular value decomposition in groundwater model calibration

    Christensen, Steen; Doherty, John

    2008-01-01

    super parameters), and that the structural errors caused by using pilot points and super parameters to parameterize the highly heterogeneous log-transmissivity field can be significant. For the test case much effort is put into studying how the calibrated model's ability to make accurate predictions...

  15. Predicting birth weight with conditionally linear transformation models.

    Möst, Lisa; Schmid, Matthias; Faschingbauer, Florian; Hothorn, Torsten

    2016-12-01

    Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs. © The Author(s) 2014.

  16. Aquatic pathways model to predict the fate of phenolic compounds

    Aaberg, R.L.; Peloquin, R.A.; Strenge, D.L.; Mellinger, P.J.

    1983-04-01

    Organic materials released from energy-related activities could affect human health and the environment. To better assess possible impacts, we developed a model to predict the fate of spills or discharges of pollutants into flowing or static bodies of fresh water. A computer code, Aquatic Pathways Model (APM), was written to implement the model. The computer programs use compartmental analysis to simulate aquatic ecosystems. The APM estimates the concentrations of chemicals in fish tissue, water and sediment, and is therefore useful for assessing exposure to humans through aquatic pathways. The APM will consider any aquatic pathway for which the user has transport data. Additionally, APM will estimate transport rates from physical and chemical properties of chemicals between several key compartments. The major pathways considered are biodegradation, fish and sediment uptake, photolysis, and evaporation. The model has been implemented with parameters for distribution of phenols, an important class of compounds found in the water-soluble fractions of coal liquids. Current modeling efforts show that, in comparison with many pesticides and polyaromatic hydrocarbons (PAH), the lighter phenolics (the cresols) are not persistent in the environment. The properties of heavier molecular weight phenolics (indanols, naphthols) are not well enough understood at this time to make similar judgements. For the twelve phenolics studied, biodegradation appears to be the major pathway for elimination from aquatic environments. A pond system simulation (using APM) of a spill of solvent refined coal (SRC-II) materials indicates that phenol, cresols, and other single cyclic phenolics are degraded to 16 to 25 percent of their original concentrations within 30 hours. Adsorption of these compounds into sediments and accumulation by fish was minor.

  17. Prediction of hourly solar radiation with multi-model framework

    Wu, Ji; Chan, Chee Keong

    2013-01-01

    Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

  18. Developing model-making and model-breaking skills using direct measurement video-based activities

    Vonk, Matthew; Bohacek, Peter; Militello, Cheryl; Iverson, Ellen

    2017-12-01

    This study focuses on student development of two important laboratory skills in the context of introductory college-level physics. The first skill, which we call model making, is the ability to analyze a phenomenon in a way that produces a quantitative multimodal model. The second skill, which we call model breaking, is the ability to critically evaluate if the behavior of a system is consistent with a given model. This study involved 116 introductory physics students in four different sections, each taught by a different instructor. All of the students within a given class section participated in the same instruction (including labs) with the exception of five activities performed throughout the semester. For those five activities, each class section was split into two groups; one group was scaffolded to focus on model-making skills and the other was scaffolded to focus on model-breaking skills. Both conditions involved direct measurement videos. In some cases, students could vary important experimental parameters within the video like mass, frequency, and tension. Data collected at the end of the semester indicate that students in the model-making treatment group significantly outperformed the other group on the model-making skill despite the fact that both groups shared a common physical lab experience. Likewise, the model-breaking treatment group significantly outperformed the other group on the model-breaking skill. This is important because it shows that direct measurement video-based instruction can help students acquire science-process skills, which are critical for scientists, and which are a key part of current science education approaches such as the Next Generation Science Standards and the Advanced Placement Physics 1 course.

  19. Prediction of psychological functioning one year after the predictive test for Huntington's disease and impact of the test result on reproductive decision making.

    Decruyenaere, M; Evers-Kiebooms, G; Boogaerts, A; Cassiman, J J; Cloostermans, T; Demyttenaere, K; Dom, R; Fryns, J P; Van den Berghe, H

    1996-01-01

    For people at risk for Huntington's disease, the anxiety and uncertainty about the future may be very burdensome and may be an obstacle to personal decision making about important life issues, for example, procreation. For some at risk persons, this situation is the reason for requesting predictive DNA testing. The aim of this paper is two-fold. First, we want to evaluate whether knowing one's carrier status reduces anxiety and uncertainty and whether it facilitates decision making about procreation. Second, we endeavour to identify pretest predictors of psychological adaptation one year after the predictive test (psychometric evaluation of general anxiety, depression level, and ego strength). The impact of the predictive test result was assessed in 53 subjects tested, using pre- and post-test psychometric measurement and self-report data of follow up interviews. Mean anxiety and depression levels were significantly decreased one year after a good test result; there was no significant change in the case of a bad test result. The mean personality profile, including ego strength, remained unchanged one year after the test. The study further shows that the test result had a definite impact on reproductive decision making. Stepwise multiple regression analyses were used to select the best predictors of the subject's post-test reactions. The results indicate that a careful evaluation of pretest ego strength, depression level, and coping strategies may be helpful in predicting post-test reactions, independently of the carrier status. Test result (carrier/ non-carrier), gender, and age did not significantly contribute to the prediction. About one third of the variance of post-test anxiety and depression level and more than half of the variance of ego strength was explained, implying that other psychological or social aspects should also be taken into account when predicting individual post-test reactions. PMID:8880572

  20. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

    2015-01-01

    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167