Predictive Modeling in Race Walking

Directory of Open Access Journals (Sweden)

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.

REMINDER Saved Leave Scheme (SLS) : Transfer of leave to saved leave accounts

CERN Multimedia

HR Division

2002-01-01

Under the provisions of the voluntary saved leave scheme (SLS), a maximum total of 10 days'*) annual and compensatory leave (excluding saved leave accumulated in accordance with the provisions of Administrative Circular No. 22B) can be transferred to the saved leave account at the end of the leave year (30 September). We remind you that, since last year, unused leave of all those taking part in the saved leave scheme at the closure of the leave-year accounts is transferred automatically to the saved leave account on that date. Therefore, staff members have no administrative steps to take. In addition, the transfer, which eliminates the risk of omitting to request leave transfers and rules out calculation errors in transfer requests, will be clearly shown in the list of leave transactions that can be consulted in EDH from October 2002 onwards. Furthermore, this automatic leave transfer optimizes staff members' chances of benefiting from a saved leave bonus provided that they are still participants in the schem...

Fisher classifier and its probability of error estimation

Science.gov (United States)

Chittineni, C. B.

1979-01-01

Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.

Science.gov (United States)

Faber, Felix A; Hutchison, Luke; Huang, Bing; Gilmer, Justin; Schoenholz, Samuel S; Dahl, George E; Vinyals, Oriol; Kearnes, Steven; Riley, Patrick F; von Lilienfeld, O Anatole

2017-11-14

evidence that ML model predictions deviate from DFT (B3LYP) less than DFT (B3LYP) deviates from experiment for all properties. Furthermore, out-of-sample prediction errors with respect to hybrid DFT reference are on par with, or close to, chemical accuracy. The results suggest that ML models could be more accurate than hybrid DFT if explicitly electron correlated quantum (or experimental) data were available.

Scaling prediction errors to reward variability benefits error-driven learning in humans.

Science.gov (United States)

Diederen, Kelly M J; Schultz, Wolfram

2015-09-01

Effective error-driven learning requires individuals to adapt learning to environmental reward variability. The adaptive mechanism may involve decays in learning rate across subsequent trials, as shown previously, and rescaling of reward prediction errors. The present study investigated the influence of prediction error scaling and, in particular, the consequences for learning performance. Participants explicitly predicted reward magnitudes that were drawn from different probability distributions with specific standard deviations. By fitting the data with reinforcement learning models, we found scaling of prediction errors, in addition to the learning rate decay shown previously. Importantly, the prediction error scaling was closely related to learning performance, defined as accuracy in predicting the mean of reward distributions, across individual participants. In addition, participants who scaled prediction errors relative to standard deviation also presented with more similar performance for different standard deviations, indicating that increases in standard deviation did not substantially decrease "adapters'" accuracy in predicting the means of reward distributions. However, exaggerated scaling beyond the standard deviation resulted in impaired performance. Thus efficient adaptation makes learning more robust to changing variability. Copyright © 2015 the American Physiological Society.

Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method

Science.gov (United States)

Brovelli, Maria Antonia; Crespi, Mattia; Fratarcangeli, Francesca; Giannone, Francesca; Realini, Eugenio

Interest in high-resolution satellite imagery (HRSI) is spreading in several application fields, at both scientific and commercial levels. Fundamental and critical goals for the geometric use of this kind of imagery are their orientation and orthorectification, processes able to georeference the imagery and correct the geometric deformations they undergo during acquisition. In order to exploit the actual potentialities of orthorectified imagery in Geomatics applications, the definition of a methodology to assess the spatial accuracy achievable from oriented imagery is a crucial topic. In this paper we want to propose a new method for accuracy assessment based on the Leave-One-Out Cross-Validation (LOOCV), a model validation method already applied in different fields such as machine learning, bioinformatics and generally in any other field requiring an evaluation of the performance of a learning algorithm (e.g. in geostatistics), but never applied to HRSI orientation accuracy assessment. The proposed method exhibits interesting features which are able to overcome the most remarkable drawbacks involved by the commonly used method (Hold-Out Validation — HOV), based on the partitioning of the known ground points in two sets: the first is used in the orientation-orthorectification model (GCPs — Ground Control Points) and the second is used to validate the model itself (CPs — Check Points). In fact the HOV is generally not reliable and it is not applicable when a low number of ground points is available. To test the proposed method we implemented a new routine that performs the LOOCV in the software SISAR, developed by the Geodesy and Geomatics Team at the Sapienza University of Rome to perform the rigorous orientation of HRSI; this routine was tested on some EROS-A and QuickBird images. Moreover, these images were also oriented using the world recognized commercial software OrthoEngine v. 10 (included in the Geomatica suite by PCI), manually performing the LOOCV

Comparison of Prediction-Error-Modelling Criteria

DEFF Research Database (Denmark)

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...

Interaction of Instrumental and Goal-Directed Learning Modulates Prediction Error Representations in the Ventral Striatum.

Science.gov (United States)

Guo, Rong; Böhmer, Wendelin; Hebart, Martin; Chien, Samson; Sommer, Tobias; Obermayer, Klaus; Gläscher, Jan

2016-12-14

Goal-directed and instrumental learning are both important controllers of human behavior. Learning about which stimulus event occurs in the environment and the reward associated with them allows humans to seek out the most valuable stimulus and move through the environment in a goal-directed manner. Stimulus-response associations are characteristic of instrumental learning, whereas response-outcome associations are the hallmark of goal-directed learning. Here we provide behavioral, computational, and neuroimaging results from a novel task in which stimulus-response and response-outcome associations are learned simultaneously but dominate behavior at different stages of the experiment. We found that prediction error representations in the ventral striatum depend on which type of learning dominates. Furthermore, the amygdala tracks the time-dependent weighting of stimulus-response versus response-outcome learning. Our findings suggest that the goal-directed and instrumental controllers dynamically engage the ventral striatum in representing prediction errors whenever one of them is dominating choice behavior. Converging evidence in human neuroimaging studies has shown that the reward prediction errors are correlated with activity in the ventral striatum. Our results demonstrate that this region is simultaneously correlated with a stimulus prediction error. Furthermore, the learning system that is currently dominating behavioral choice dynamically engages the ventral striatum for computing its prediction errors. This demonstrates that the prediction error representations are highly dynamic and influenced by various experimental context. This finding points to a general role of the ventral striatum in detecting expectancy violations and encoding error signals regardless of the specific nature of the reinforcer itself. Copyright © 2016 the authors 0270-6474/16/3612650-11$15.00/0.

Artificial neural network implementation of a near-ideal error prediction controller

Science.gov (United States)

Mcvey, Eugene S.; Taylor, Lynore Denise

1992-01-01

A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error

Seismic attenuation relationship with homogeneous and heterogeneous prediction-error variance models

Science.gov (United States)

Mu, He-Qing; Xu, Rong-Rong; Yuen, Ka-Veng

2014-03-01

Peak ground acceleration (PGA) estimation is an important task in earthquake engineering practice. One of the most well-known models is the Boore-Joyner-Fumal formula, which estimates the PGA using the moment magnitude, the site-to-fault distance and the site foundation properties. In the present study, the complexity for this formula and the homogeneity assumption for the prediction-error variance are investigated and an efficiency-robustness balanced formula is proposed. For this purpose, a reduced-order Monte Carlo simulation algorithm for Bayesian model class selection is presented to obtain the most suitable predictive formula and prediction-error model for the seismic attenuation relationship. In this approach, each model class (a predictive formula with a prediction-error model) is evaluated according to its plausibility given the data. The one with the highest plausibility is robust since it possesses the optimal balance between the data fitting capability and the sensitivity to noise. A database of strong ground motion records in the Tangshan region of China is obtained from the China Earthquake Data Center for the analysis. The optimal predictive formula is proposed based on this database. It is shown that the proposed formula with heterogeneous prediction-error variance is much simpler than the attenuation model suggested by Boore, Joyner and Fumal (1993).

["Second victim" - error, crises and how to get out of it].

Science.gov (United States)

von Laue, N; Schwappach, D; Hochreutener, M

2012-06-01

Medical errors do not only harm patients ("first victims"). Almost all health care professionals become a so-called "second victim" once in their career by being involved in a medical error. Studies show that error involvement can have a tremendous impact on health care workers leading to burnout, depression and professional crisis. Moreover persons involved in errors show a decline in job performance and jeopardize therefore patient safety. Blaming the person is one of the typical psychological reactions after an error happened as the attribution theory tells. The self-esteem gets stabilized if we can put blame on someone and pick out a scapegoat. But standing alone makes the emotional situation even worse. A vicious circle can evolve with tragic effect for the individual and negative implications for patient safety and the health care setting.

Does the sensorimotor system minimize prediction error or select the most likely prediction during object lifting?

Science.gov (United States)

McGregor, Heather R.; Pun, Henry C. H.; Buckingham, Gavin; Gribble, Paul L.

2016-01-01

The human sensorimotor system is routinely capable of making accurate predictions about an object's weight, which allows for energetically efficient lifts and prevents objects from being dropped. Often, however, poor predictions arise when the weight of an object can vary and sensory cues about object weight are sparse (e.g., picking up an opaque water bottle). The question arises, what strategies does the sensorimotor system use to make weight predictions when one is dealing with an object whose weight may vary? For example, does the sensorimotor system use a strategy that minimizes prediction error (minimal squared error) or one that selects the weight that is most likely to be correct (maximum a posteriori)? In this study we dissociated the predictions of these two strategies by having participants lift an object whose weight varied according to a skewed probability distribution. We found, using a small range of weight uncertainty, that four indexes of sensorimotor prediction (grip force rate, grip force, load force rate, and load force) were consistent with a feedforward strategy that minimizes the square of prediction errors. These findings match research in the visuomotor system, suggesting parallels in underlying processes. We interpret our findings within a Bayesian framework and discuss the potential benefits of using a minimal squared error strategy. NEW & NOTEWORTHY Using a novel experimental model of object lifting, we tested whether the sensorimotor system models the weight of objects by minimizing lifting errors or by selecting the statistically most likely weight. We found that the sensorimotor system minimizes the square of prediction errors for object lifting. This parallels the results of studies that investigated visually guided reaching, suggesting an overlap in the underlying mechanisms between tasks that involve different sensory systems. PMID:27760821

Competition between learned reward and error outcome predictions in anterior cingulate cortex.

Science.gov (United States)

Alexander, William H; Brown, Joshua W

2010-02-15

The anterior cingulate cortex (ACC) is implicated in performance monitoring and cognitive control. Non-human primate studies of ACC show prominent reward signals, but these are elusive in human studies, which instead show mainly conflict and error effects. Here we demonstrate distinct appetitive and aversive activity in human ACC. The error likelihood hypothesis suggests that ACC activity increases in proportion to the likelihood of an error, and ACC is also sensitive to the consequence magnitude of the predicted error. Previous work further showed that error likelihood effects reach a ceiling as the potential consequences of an error increase, possibly due to reductions in the average reward. We explored this issue by independently manipulating reward magnitude of task responses and error likelihood while controlling for potential error consequences in an Incentive Change Signal Task. The fMRI results ruled out a modulatory effect of expected reward on error likelihood effects in favor of a competition effect between expected reward and error likelihood. Dynamic causal modeling showed that error likelihood and expected reward signals are intrinsic to the ACC rather than received from elsewhere. These findings agree with interpretations of ACC activity as signaling both perceptions of risk and predicted reward. Copyright 2009 Elsevier Inc. All rights reserved.

Surface roughness considerations for atmospheric correction of ocean color sensors. I - The Rayleigh-scattering component. II - Error in the retrieved water-leaving radiance

Science.gov (United States)

Gordon, Howard R.; Wang, Menghua

1992-01-01

The first step in the Coastal Zone Color Scanner (CZCS) atmospheric-correction algorithm is the computation of the Rayleigh-scattering (RS) contribution, L sub r, to the radiance leaving the top of the atmosphere over the ocean. In the present algorithm, L sub r is computed by assuming that the ocean surface is flat. Calculations of the radiance leaving an RS atmosphere overlying a rough Fresnel-reflecting ocean are presented to evaluate the radiance error caused by the flat-ocean assumption. Simulations are carried out to evaluate the error incurred when the CZCS-type algorithm is applied to a realistic ocean in which the surface is roughened by the wind. In situations where there is no direct sun glitter, it is concluded that the error induced by ignoring the Rayleigh-aerosol interaction is usually larger than that caused by ignoring the surface roughness. This suggests that, in refining algorithms for future sensors, more effort should be focused on dealing with the Rayleigh-aerosol interaction than on the roughness of the sea surface.

Quality of Work Life, Nurses' Intention to Leave the Profession, and Nurses Leaving the Profession: A One-Year Prospective Survey.

Science.gov (United States)

Lee, Ya-Wen; Dai, Yu-Tzu; Chang, Mei Yeh; Chang, Yue-Cune; Yao, Kaiping Grace; Liu, Mei-Chun

2017-07-01

To examine the associations among quality of work life, nurses' intention to leave the profession, and nurses leaving the profession. A prospective study design was used. Participants were 1,283 hospital nurses with a purposive sampling in Taiwan. The self-reported questionnaire consisted of three questionnaires: the Chinese version of the Quality of Nursing Work Life scale, an intention-to-leave profession questionnaire, and a demographic questionnaire. Records of nurses leaving the profession were surveyed 1 year later. Data were analyzed by descriptive statistics and inferential statistics. As many as 720 nurses (56.1%) had tendencies to leave their profession. However, only 31 nurses (2.5%) left their profession 1 year later. Nurses' intention to leave the profession mediated the relationship between the milieu of respect and autonomy, quality of work life, and nurses leaving the profession. The milieu of respect and autonomy describing the quality of work life predicts the nurses' intention to leave the profession, and together these predict nurses leaving the profession. This study illustrates that nurse managers could provide effective interventions to ameliorate the milieu of respect and autonomy aspect of quality of work life to prevent nurses from leaving their profession. © 2017 Sigma Theta Tau International.

BANKRUPTCY PREDICTION MODEL WITH ZETAc OPTIMAL CUT-OFF SCORE TO CORRECT TYPE I ERRORS

Directory of Open Access Journals (Sweden)

Mohamad Iwan

2005-06-01

This research has successfully attained the following results: (1 type I error is in fact 59,83 times more costly compared to type II error, (2 22 ratios distinguish between bankrupt and non-bankrupt groups, (3 2 financial ratios proved to be effective in predicting bankruptcy, (4 prediction using ZETAc optimal cut-off score predicts more companies filing for bankruptcy within one year compared to prediction using Hair et al. optimum cutting score, (5 Although prediction using Hair et al. optimum cutting score is more accurate, prediction using ZETAc optimal cut-off score proved to be able to minimize cost incurred from classification errors.

REMINDER: Saved Leave Scheme (SLS)

CERN Multimedia

2003-01-01

Transfer of leave to saved leave accounts Under the provisions of the voluntary saved leave scheme (SLS), a maximum total of 10 days'* annual and compensatory leave (excluding saved leave accumulated in accordance with the provisions of Administrative Circular No 22B) can be transferred to the saved leave account at the end of the leave year (30 September). We remind you that unused leave of all those taking part in the saved leave scheme at the closure of the leave year accounts is transferred automatically to the saved leave account on that date. Therefore, staff members have no administrative steps to take. In addition, the transfer, which eliminates the risk of omitting to request leave transfers and rules out calculation errors in transfer requests, will be clearly shown in the list of leave transactions that can be consulted in EDH from October 2003 onwards. Furthermore, this automatic leave transfer optimizes staff members' chances of benefiting from a saved leave bonus provided that they ar...

Critical evidence for the prediction error theory in associative learning.

Science.gov (United States)

Terao, Kanta; Matsumoto, Yukihisa; Mizunami, Makoto

2015-03-10

In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. Complete evidence for the prediction error theory, however, has not been obtained in any learning systems: Prediction error theory stems from the finding of a blocking phenomenon, but blocking can also be accounted for by other theories, such as the attentional theory. We demonstrated blocking in classical conditioning in crickets and obtained evidence to reject the attentional theory. To obtain further evidence supporting the prediction error theory and rejecting alternative theories, we constructed a neural model to match the prediction error theory, by modifying our previous model of learning in crickets, and we tested a prediction from the model: the model predicts that pharmacological intervention of octopaminergic transmission during appetitive conditioning impairs learning but not formation of reward prediction itself, and it thus predicts no learning in subsequent training. We observed such an "auto-blocking", which could be accounted for by the prediction error theory but not by other competitive theories to account for blocking. This study unambiguously demonstrates validity of the prediction error theory in associative learning.

Prediction error demarcates the transition from retrieval, to reconsolidation, to new learning

NARCIS (Netherlands)

Sevenster, Dieuwke|info:eu-repo/dai/nl/375491104; Beckers, Tom; Kindt, Merel

2014-01-01

Although disrupting reconsolidation is promising in targeting emotional memories, the conditions under which memory becomes labile are still unclear. The current study showed that post-retrieval changes in expectancy as an index for prediction error may serve as a read-out for the underlying

Prediction error, ketamine and psychosis: An updated model.

Science.gov (United States)

Corlett, Philip R; Honey, Garry D; Fletcher, Paul C

2016-11-01

In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.

A second study of the prediction of cognitive errors using the 'CREAM' technique

International Nuclear Information System (INIS)

Collier, Steve; Andresen, Gisle

2000-03-01

Some human errors, such as errors of commission and knowledge-based errors, are not adequately modelled in probabilistic safety assessments. Even qualitative methods for handling these sorts of errors are comparatively underdeveloped. The 'Cognitive Reliability and Error Analysis Method' (CREAM) was recently developed for prediction of cognitive error modes. It has not yet been comprehensively established how reliable, valid and generally useful it could be to researchers and practitioners. A previous study of CREAM at Halden was promising, showing a relationship between errors predicted in advance and those that actually occurred in simulated fault scenarios. The present study continues this work. CREAM was used to make predictions of cognitive error modes throughout two rather difficult fault scenarios. Predictions were made of the most likely cognitive error mode, were one to occur at all, at several points throughout the expected scenarios, based upon the scenario design and description. Each scenario was then run 15 times with different operators. Error modes occurring during simulations were later scored using the task description for the scenario, videotapes of operator actions, eye-track recording, operators' verbal protocols and an expert's concurrent commentary. The scoring team had no previous substantive knowledge of the experiment or the techniques used, so as to provide a more stringent test of the data and knowledge needed for scoring. The scored error modes were then compared with the CREAM predictions to assess the degree of agreement. Some cognitive error modes were predicted successfully, but the results were generally not so encouraging as the previous study. Several problems were found with both the CREAM technique and the data needed to complete the analysis. It was felt that further development was needed before this kind of analysis can be reliable and valid, either in a research setting or as a practitioner's tool in a safety assessment

Learning from sensory and reward prediction errors during motor adaptation.

Science.gov (United States)

Izawa, Jun; Shadmehr, Reza

2011-03-01

Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.

Notes on human error analysis and prediction

International Nuclear Information System (INIS)

Rasmussen, J.

1978-11-01

The notes comprise an introductory discussion of the role of human error analysis and prediction in industrial risk analysis. Following this introduction, different classes of human errors and role in industrial systems are mentioned. Problems related to the prediction of human behaviour in reliability and safety analysis are formulated and ''criteria for analyzability'' which must be met by industrial systems so that a systematic analysis can be performed are suggested. The appendices contain illustrative case stories and a review of human error reports for the task of equipment calibration and testing as found in the US Licensee Event Reports. (author)

Error-related anterior cingulate cortex activity and the prediction of conscious error awareness

Directory of Open Access Journals (Sweden)

Catherine eOrr

2012-06-01

Full Text Available Research examining the neural mechanisms associated with error awareness has consistently identified dorsal anterior cingulate activity (ACC as necessary but not predictive of conscious error detection. Two recent studies (Steinhauser and Yeung, 2010; Wessel et al. 2011 have found a contrary pattern of greater dorsal ACC activity (in the form of the error-related negativity during detected errors, but suggested that the greater activity may instead reflect task influences (e.g., response conflict, error probability and or individual variability (e.g., statistical power. We re-analyzed fMRI BOLD data from 56 healthy participants who had previously been administered the Error Awareness Task, a motor Go/No-go response inhibition task in which subjects make errors of commission of which they are aware (Aware errors, or unaware (Unaware errors. Consistent with previous data, the activity in a number of cortical regions was predictive of error awareness, including bilateral inferior parietal and insula cortices, however in contrast to previous studies, including our own smaller sample studies using the same task, error-related dorsal ACC activity was significantly greater during aware errors when compared to unaware errors. While the significantly faster RT for aware errors (compared to unaware was consistent with the hypothesis of higher response conflict increasing ACC activity, we could find no relationship between dorsal ACC activity and the error RT difference. The data suggests that individual variability in error awareness is associated with error-related dorsal ACC activity, and therefore this region may be important to conscious error detection, but it remains unclear what task and individual factors influence error awareness.

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Science.gov (United States)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

Science.gov (United States)

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error

Science.gov (United States)

Pokhrel, Samir; Saha, Subodh Kumar; Dhakate, Ashish; Rahman, Hasibur; Chaudhari, Hemantkumar S.; Salunke, Kiran; Hazra, Anupam; Sujith, K.; Sikka, D. R.

2016-04-01

A detailed analysis of sensitivity to the initial condition for the simulation of the Indian summer monsoon using retrospective forecast by the latest version of the Climate Forecast System version-2 (CFSv2) is carried out. This study primarily focuses on the tropical region of Indian and Pacific Ocean basin, with special emphasis on the Indian land region. The simulated seasonal mean and the inter-annual standard deviations of rainfall, upper and lower level atmospheric circulations and Sea Surface Temperature (SST) tend to be more skillful as the lead forecast time decreases (5 month lead to 0 month lead time i.e. L5-L0). In general spatial correlation (bias) increases (decreases) as forecast lead time decreases. This is further substantiated by their averaged value over the selected study regions over the Indian and Pacific Ocean basins. The tendency of increase (decrease) of model bias with increasing (decreasing) forecast lead time also indicates the dynamical drift of the model. Large scale lower level circulation (850 hPa) shows enhancement of anomalous westerlies (easterlies) over the tropical region of the Indian Ocean (Western Pacific Ocean), which indicates the enhancement of model error with the decrease in lead time. At the upper level circulation (200 hPa) biases in both tropical easterly jet and subtropical westerlies jet tend to decrease as the lead time decreases. Despite enhancement of the prediction skill, mean SST bias seems to be insensitive to the initialization. All these biases are significant and together they make CFSv2 vulnerable to seasonal uncertainties in all the lead times. Overall the zeroth lead (L0) seems to have the best skill, however, in case of Indian summer monsoon rainfall (ISMR), the 3 month lead forecast time (L3) has the maximum ISMR prediction skill. This is valid using different independent datasets, wherein these maximum skill scores are 0.64, 0.42 and 0.57 with respect to the Global Precipitation Climatology Project

Predicting stay/leave behavior among volleyball referees

NARCIS (Netherlands)

Van Yperen, N.W.

1998-01-01

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

Human medial frontal cortex activity predicts learning from errors.

Science.gov (United States)

Hester, Robert; Barre, Natalie; Murphy, Kevin; Silk, Tim J; Mattingley, Jason B

2008-08-01

Learning from errors is a critical feature of human cognition. It underlies our ability to adapt to changing environmental demands and to tune behavior for optimal performance. The posterior medial frontal cortex (pMFC) has been implicated in the evaluation of errors to control behavior, although it has not previously been shown that activity in this region predicts learning from errors. Using functional magnetic resonance imaging, we examined activity in the pMFC during an associative learning task in which participants had to recall the spatial locations of 2-digit targets and were provided with immediate feedback regarding accuracy. Activity within the pMFC was significantly greater for errors that were subsequently corrected than for errors that were repeated. Moreover, pMFC activity during recall errors predicted future responses (correct vs. incorrect), despite a sizeable interval (on average 70 s) between an error and the next presentation of the same recall probe. Activity within the hippocampus also predicted future performance and correlated with error-feedback-related pMFC activity. A relationship between performance expectations and pMFC activity, in the absence of differing reinforcement value for errors, is consistent with the idea that error-related pMFC activity reflects the extent to which an outcome is "worse than expected."

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported.

Science.gov (United States)

Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D

2018-05-18

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.

Error analysis in predictive modelling demonstrated on mould data.

Science.gov (United States)

Baranyi, József; Csernus, Olívia; Beczner, Judit

2014-01-17

The purpose of this paper was to develop a predictive model for the effect of temperature and water activity on the growth rate of Aspergillus niger and to determine the sources of the error when the model is used for prediction. Parallel mould growth curves, derived from the same spore batch, were generated and fitted to determine their growth rate. The variances of replicate ln(growth-rate) estimates were used to quantify the experimental variability, inherent to the method of determining the growth rate. The environmental variability was quantified by the variance of the respective means of replicates. The idea is analogous to the "within group" and "between groups" variability concepts of ANOVA procedures. A (secondary) model, with temperature and water activity as explanatory variables, was fitted to the natural logarithm of the growth rates determined by the primary model. The model error and the experimental and environmental errors were ranked according to their contribution to the total error of prediction. Our method can readily be applied to analysing the error structure of predictive models of bacterial growth models, too. © 2013.

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

Science.gov (United States)

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

2016-01-01

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

Model-free and model-based reward prediction errors in EEG.

Science.gov (United States)

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Advanced error-prediction LDPC with temperature compensation for highly reliable SSDs

Science.gov (United States)

Tokutomi, Tsukasa; Tanakamaru, Shuhei; Iwasaki, Tomoko Ogura; Takeuchi, Ken

2015-09-01

To improve the reliability of NAND Flash memory based solid-state drives (SSDs), error-prediction LDPC (EP-LDPC) has been proposed for multi-level-cell (MLC) NAND Flash memory (Tanakamaru et al., 2012, 2013), which is effective for long retention times. However, EP-LDPC is not as effective for triple-level cell (TLC) NAND Flash memory, because TLC NAND Flash has higher error rates and is more sensitive to program-disturb error. Therefore, advanced error-prediction LDPC (AEP-LDPC) has been proposed for TLC NAND Flash memory (Tokutomi et al., 2014). AEP-LDPC can correct errors more accurately by precisely describing the error phenomena. In this paper, the effects of AEP-LDPC are investigated in a 2×nm TLC NAND Flash memory with temperature characterization. Compared with LDPC-with-BER-only, the SSD's data-retention time is increased by 3.4× and 9.5× at room-temperature (RT) and 85 °C, respectively. Similarly, the acceptable BER is increased by 1.8× and 2.3×, respectively. Moreover, AEP-LDPC can correct errors with pre-determined tables made at higher temperatures to shorten the measurement time before shipping. Furthermore, it is found that one table can cover behavior over a range of temperatures in AEP-LDPC. As a result, the total table size can be reduced to 777 kBytes, which makes this approach more practical.

Error sensitivity analysis in 10-30-day extended range forecasting by using a nonlinear cross-prediction error model

Science.gov (United States)

Xia, Zhiye; Xu, Lisheng; Chen, Hongbin; Wang, Yongqian; Liu, Jinbao; Feng, Wenlan

2017-06-01

Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-6-10-2), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-1-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.

Accounting for the measurement error of spectroscopically inferred soil carbon data for improved precision of spatial predictions.

Science.gov (United States)

Somarathna, P D S N; Minasny, Budiman; Malone, Brendan P; Stockmann, Uta; McBratney, Alex B

2018-08-01

Spatial modelling of environmental data commonly only considers spatial variability as the single source of uncertainty. In reality however, the measurement errors should also be accounted for. In recent years, infrared spectroscopy has been shown to offer low cost, yet invaluable information needed for digital soil mapping at meaningful spatial scales for land management. However, spectrally inferred soil carbon data are known to be less accurate compared to laboratory analysed measurements. This study establishes a methodology to filter out the measurement error variability by incorporating the measurement error variance in the spatial covariance structure of the model. The study was carried out in the Lower Hunter Valley, New South Wales, Australia where a combination of laboratory measured, and vis-NIR and MIR inferred topsoil and subsoil soil carbon data are available. We investigated the applicability of residual maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC) simulation methods to generate parameters of the Matérn covariance function directly from the data in the presence of measurement error. The results revealed that the measurement error can be effectively filtered-out through the proposed technique. When the measurement error was filtered from the data, the prediction variance almost halved, which ultimately yielded a greater certainty in spatial predictions of soil carbon. Further, the MCMC technique was successfully used to define the posterior distribution of measurement error. This is an important outcome, as the MCMC technique can be used to estimate the measurement error if it is not explicitly quantified. Although this study dealt with soil carbon data, this method is amenable for filtering the measurement error of any kind of continuous spatial environmental data. Copyright © 2018 Elsevier B.V. All rights reserved.

Model parameter-related optimal perturbations and their contributions to El Niño prediction errors

Science.gov (United States)

Tao, Ling-Jiang; Gao, Chuan; Zhang, Rong-Hua

2018-04-01

Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling ({α _τ } ), and the other involves the thermocline effect on the SST ({α _{Te}} ). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the {α _τ } component is mainly concentrated in the central equatorial Pacific, and the {α _{Te}} component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Niño- or La Niña-like error evolution, resulting in an El Niño-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and to the thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.

A causal link between prediction errors, dopamine neurons and learning.

Science.gov (United States)

Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H

2013-07-01

Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.

Prediction-error variance in Bayesian model updating: a comparative study

Science.gov (United States)

Asadollahi, Parisa; Li, Jian; Huang, Yong

2017-04-01

In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes' Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model

Error-related brain activity predicts cocaine use after treatment at 3-month follow-up.

Science.gov (United States)

Marhe, Reshmi; van de Wetering, Ben J M; Franken, Ingmar H A

2013-04-15

Relapse after treatment is one of the most important problems in drug dependency. Several studies suggest that lack of cognitive control is one of the causes of relapse. In this study, a relative new electrophysiologic index of cognitive control, the error-related negativity, is investigated to examine its suitability as a predictor of relapse. The error-related negativity was measured in 57 cocaine-dependent patients during their first week in detoxification treatment. Data from 49 participants were used to predict cocaine use at 3-month follow-up. Cocaine use at follow-up was measured by means of self-reported days of cocaine use in the last month verified by urine screening. A multiple hierarchical regression model was used to examine the predictive value of the error-related negativity while controlling for addiction severity and self-reported craving in the week before treatment. The error-related negativity was the only significant predictor in the model and added 7.4% of explained variance to the control variables, resulting in a total of 33.4% explained variance in the prediction of days of cocaine use at follow-up. A reduced error-related negativity measured during the first week of treatment was associated with more days of cocaine use at 3-month follow-up. Moreover, the error-related negativity was a stronger predictor of recent cocaine use than addiction severity and craving. These results suggest that underactive error-related brain activity might help to identify patients who are at risk of relapse as early as in the first week of detoxification treatment. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

[Fire behavior of Mongolian oak leaves fuel bed under no-wind and zero-slope conditions. II. Analysis of the factors affecting flame length and residence time and related prediction models].

Science.gov (United States)

Zhang, Ji-Li; Liu, Bo-Fei; Di, Xue-Ying; Chu, Teng-Fei; Jin, Sen

2012-11-01

Taking fuel moisture content, fuel loading, and fuel bed depth as controlling factors, the fuel beds of Mongolian oak leaves in Maoershan region of Northeast China in field were simulated, and a total of one hundred experimental burnings under no-wind and zero-slope conditions were conducted in laboratory, with the effects of the fuel moisture content, fuel loading, and fuel bed depth on the flame length and its residence time analyzed and the multivariate linear prediction models constructed. The results indicated that fuel moisture content had a significant negative liner correlation with flame length, but less correlation with flame residence time. Both the fuel loading and the fuel bed depth were significantly positively correlated with flame length and its residence time. The interactions of fuel bed depth with fuel moisture content and fuel loading had significant effects on the flame length, while the interactions of fuel moisture content with fuel loading and fuel bed depth affected the flame residence time significantly. The prediction model of flame length had better prediction effect, which could explain 83.3% of variance, with a mean absolute error of 7.8 cm and a mean relative error of 16.2%, while the prediction model of flame residence time was not good enough, which could only explain 54% of variance, with a mean absolute error of 9.2 s and a mean relative error of 18.6%.

Predictive error detection in pianists: A combined ERP and motion capture study

Directory of Open Access Journals (Sweden)

Clemens eMaidhof

2013-09-01

Full Text Available Performing a piece of music involves the interplay of several cognitive and motor processes and requires extensive training to achieve a high skill level. However, even professional musicians commit errors occasionally. Previous event-related potential (ERP studies have investigated the neurophysiological correlates of pitch errors during piano performance, and reported pre-error negativity already occurring approximately 70-100 ms before the error had been committed and audible. It was assumed that this pre-error negativity reflects predictive control processes that compare predicted consequences with actual consequences of one’s own actions. However, in previous investigations, correct and incorrect pitch events were confounded by their different tempi. In addition, no data about the underlying movements were available. In the present study, we exploratively recorded the ERPs and 3D movement data of pianists’ fingers simultaneously while they performed fingering exercises from memory. Results showed a pre-error negativity for incorrect keystrokes when both correct and incorrect keystrokes were performed with comparable tempi. Interestingly, even correct notes immediately preceding erroneous keystrokes elicited a very similar negativity. In addition, we explored the possibility of computing ERPs time-locked to a kinematic landmark in the finger motion trajectories defined by when a finger makes initial contact with the key surface, that is, at the onset of tactile feedback. Results suggest that incorrect notes elicited a small difference after the onset of tactile feedback, whereas correct notes preceding incorrect ones elicited negativity before the onset of tactile feedback. The results tentatively suggest that tactile feedback plays an important role in error-monitoring during piano performance, because the comparison between predicted and actual sensory (tactile feedback may provide the information necessary for the detection of an

Prospective detection of large prediction errors: a hypothesis testing approach

International Nuclear Information System (INIS)

Ruan, Dan

2010-01-01

Real-time motion management is important in radiotherapy. In addition to effective monitoring schemes, prediction is required to compensate for system latency, so that treatment can be synchronized with tumor motion. However, it is difficult to predict tumor motion at all times, and it is critical to determine when large prediction errors may occur. Such information can be used to pause the treatment beam or adjust monitoring/prediction schemes. In this study, we propose a hypothesis testing approach for detecting instants corresponding to potentially large prediction errors in real time. We treat the future tumor location as a random variable, and obtain its empirical probability distribution with the kernel density estimation-based method. Under the null hypothesis, the model probability is assumed to be a concentrated Gaussian centered at the prediction output. Under the alternative hypothesis, the model distribution is assumed to be non-informative uniform, which reflects the situation that the future position cannot be inferred reliably. We derive the likelihood ratio test (LRT) for this hypothesis testing problem and show that with the method of moments for estimating the null hypothesis Gaussian parameters, the LRT reduces to a simple test on the empirical variance of the predictive random variable. This conforms to the intuition to expect a (potentially) large prediction error when the estimate is associated with high uncertainty, and to expect an accurate prediction when the uncertainty level is low. We tested the proposed method on patient-derived respiratory traces. The 'ground-truth' prediction error was evaluated by comparing the prediction values with retrospective observations, and the large prediction regions were subsequently delineated by thresholding the prediction errors. The receiver operating characteristic curve was used to describe the performance of the proposed hypothesis testing method. Clinical implication was represented by miss

A prediction rule for shoulder pain related sick leave: a prospective cohort study

Directory of Open Access Journals (Sweden)

van der Heijden Geert JMG

2006-12-01

Full Text Available Abstract Background Shoulder pain is common in primary care, and has an unfavourable outcome in many patients. Information about predictors of shoulder pain related sick leave in workers is scarce and inconsistent. The objective was to develop a clinical prediction rule for calculating the risk of shoulder pain related sick leave for individual workers, during the 6 months following first consultation in general practice. Methods A prospective cohort study with 6 months follow-up was conducted among 350 workers with a new episode of shoulder pain. Potential predictors included the results of a physical examination, sociodemographic variables, disease characteristics (duration of symptoms, sick leave in the 2 months prior to consultation, pain intensity, disability, comorbidity, physical activity, physical work load, psychological factors, and the psychosocial work environment. The main outcome measure was sick leave during 6 months following first consultation in general practice. Results Response rate to the follow-up questionnaire at 6 months was 85%. During the 6 months after first consultation 30% (89/298 of the workers reported sick leave. 16% (47 reported 10 days sick leave or more. Sick leave during this period was predicted in a multivariable model by a longer duration of sick leave prior to consultation, more shoulder pain, a perceived cause of strain or overuse during regular activities, and co-existing psychological complaints. The discriminative ability of the prediction model was satisfactory with an area under the curve of 0.70 (95% CI 0.64–0.76. Conclusion Although 30% of all workers with shoulder pain reported sick leave during follow-up, the duration of sick leave was limited to a few days in most workers. We developed a prediction rule and a score chart that can be used by general practitioners and occupational health care providers to calculate the absolute risk of sick leave in individual workers with shoulder pain, which

A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

Science.gov (United States)

Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon

2016-03-01

Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD  =  1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be

Climbing fibers predict movement kinematics and performance errors.

Science.gov (United States)

Streng, Martha L; Popa, Laurentiu S; Ebner, Timothy J

2017-09-01

Requisite for understanding cerebellar function is a complete characterization of the signals provided by complex spike (CS) discharge of Purkinje cells, the output neurons of the cerebellar cortex. Numerous studies have provided insights into CS function, with the most predominant view being that they are evoked by error events. However, several reports suggest that CSs encode other aspects of movements and do not always respond to errors or unexpected perturbations. Here, we evaluated CS firing during a pseudo-random manual tracking task in the monkey ( Macaca mulatta ). This task provides extensive coverage of the work space and relative independence of movement parameters, delivering a robust data set to assess the signals that activate climbing fibers. Using reverse correlation, we determined feedforward and feedback CSs firing probability maps with position, velocity, and acceleration, as well as position error, a measure of tracking performance. The direction and magnitude of the CS modulation were quantified using linear regression analysis. The major findings are that CSs significantly encode all three kinematic parameters and position error, with acceleration modulation particularly common. The modulation is not related to "events," either for position error or kinematics. Instead, CSs are spatially tuned and provide a linear representation of each parameter evaluated. The CS modulation is largely predictive. Similar analyses show that the simple spike firing is modulated by the same parameters as the CSs. Therefore, CSs carry a broader array of signals than previously described and argue for climbing fiber input having a prominent role in online motor control. NEW & NOTEWORTHY This article demonstrates that complex spike (CS) discharge of cerebellar Purkinje cells encodes multiple parameters of movement, including motor errors and kinematics. The CS firing is not driven by error or kinematic events; instead it provides a linear representation of each

Evaluating the effect of measurement error when using one or two 24 h dietary recalls to assess eating out: a study in the context of the HECTOR project.

Science.gov (United States)

Orfanos, Philippos; Knüppel, Sven; Naska, Androniki; Haubrock, Jennifer; Trichopoulou, Antonia; Boeing, Heiner

2013-09-28

Eating out is often recorded through short-term measurements and the large within-person variability in intakes may not be adequately captured. The present study aimed to understand the effect of measurement error when using eating-out data from one or two 24 h dietary recalls (24hDR), in order to describe intakes and assess associations between eating out and personal characteristics. In a sample of 366 adults from Potsdam, Germany, two 24hDR and a FFQ were collected. Out-of-home intakes were estimated based on either one 24hDR or two 24hDR or the Multiple Source Method (MSM) combining the two 24hDR and the questionnaire. The distribution of out-of-home intakes of energy, macronutrients and selected foods was described. Multiple linear regression and partial correlation coefficients were estimated to assess associations between out-of-home energy intake and participants' characteristics. The mean daily out-of-home intakes estimated from the two 24hDR were similar to the usual intakes estimated through the MSM. The out-of-home energy intake, estimated through either one or two 24hDR, was positively associated with total energy intake, inversely with age and associations were stronger when using the two 24hDR. A marginally significant inverse association between out-of-home energy intake and physical activity at work was observed only on the basis of the two 24hDR. After applying the MSM, all significant associations remained and were more precise. Data on eating out collected through one or two 24hDR may not adequately describe intake distributions, but significant associations between eating out and participants' characteristics are highly unlikely to appear when in reality these do not exist.

SHERPA: A systematic human error reduction and prediction approach

International Nuclear Information System (INIS)

Embrey, D.E.

1986-01-01

This paper describes a Systematic Human Error Reduction and Prediction Approach (SHERPA) which is intended to provide guidelines for human error reduction and quantification in a wide range of human-machine systems. The approach utilizes as its basic current cognitive models of human performance. The first module in SHERPA performs task and human error analyses, which identify likely error modes, together with guidelines for the reduction of these errors by training, procedures and equipment redesign. The second module uses a SARAH approach to quantify the probability of occurrence of the errors identified earlier, and provides cost benefit analyses to assist in choosing the appropriate error reduction approaches in the third module

Brain negativity as an indicator of predictive error processing: the contribution of visual action effect monitoring.

Science.gov (United States)

Joch, Michael; Hegele, Mathias; Maurer, Heiko; Müller, Hermann; Maurer, Lisa Katharina

2017-07-01

The error (related) negativity (Ne/ERN) is an event-related potential in the electroencephalogram (EEG) correlating with error processing. Its conditions of appearance before terminal external error information suggest that the Ne/ERN is indicative of predictive processes in the evaluation of errors. The aim of the present study was to specifically examine the Ne/ERN in a complex motor task and to particularly rule out other explaining sources of the Ne/ERN aside from error prediction processes. To this end, we focused on the dependency of the Ne/ERN on visual monitoring about the action outcome after movement termination but before result feedback (action effect monitoring). Participants performed a semi-virtual throwing task by using a manipulandum to throw a virtual ball displayed on a computer screen to hit a target object. Visual feedback about the ball flying to the target was masked to prevent action effect monitoring. Participants received a static feedback about the action outcome (850 ms) after each trial. We found a significant negative deflection in the average EEG curves of the error trials peaking at ~250 ms after ball release, i.e., before error feedback. Furthermore, this Ne/ERN signal did not depend on visual ball-flight monitoring after release. We conclude that the Ne/ERN has the potential to indicate error prediction in motor tasks and that it exists even in the absence of action effect monitoring. NEW & NOTEWORTHY In this study, we are separating different kinds of possible contributors to an electroencephalogram (EEG) error correlate (Ne/ERN) in a throwing task. We tested the influence of action effect monitoring on the Ne/ERN amplitude in the EEG. We used a task that allows us to restrict movement correction and action effect monitoring and to control the onset of result feedback. We ascribe the Ne/ERN to predictive error processing where a conscious feeling of failure is not a prerequisite. Copyright © 2017 the American Physiological

The Pupillary Orienting Response Predicts Adaptive Behavioral Adjustment after Errors.

Directory of Open Access Journals (Sweden)

Peter R Murphy

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

Using lexical variables to predict picture-naming errors in jargon aphasia

Directory of Open Access Journals (Sweden)

Catherine Godbold

2015-04-01

Full Text Available Introduction Individuals with jargon aphasia produce fluent output which often comprises high proportions of non-word errors (e.g., maf for dog. Research has been devoted to identifying the underlying mechanisms behind such output. Some accounts posit a reduced flow of spreading activation between levels in the lexical network (e.g., Robson et al., 2003. If activation level differences across the lexical network are a cause of non-word outputs, we would predict improved performance when target items reflect an increased flow of activation between levels (e.g. more frequently-used words are often represented by higher resting levels of activation. This research investigates the effect of lexical properties of targets (e.g., frequency, imageability on accuracy, error type (real word vs. non-word and target-error overlap of non-word errors in a picture naming task by individuals with jargon aphasia. Method Participants were 17 individuals with Wernicke’s aphasia, who produced a high proportion of non-word errors (>20% of errors on the Philadelphia Naming Test (PNT; Roach et al., 1996. The data were retrieved from the Moss Aphasic Psycholinguistic Database Project (MAPPD, Mirman et al., 2010. We used a series of mixed models to test whether lexical variables predicted accuracy, error type (real word vs. non-word and target-error overlap for the PNT data. As lexical variables tend to be highly correlated, we performed a principal components analysis to reduce the variables into five components representing variables associated with phonology (length, phonotactic probability, neighbourhood density and neighbourhood frequency, semantics (imageability and concreteness, usage (frequency and age-of-acquisition, name agreement and visual complexity. Results and Discussion Table 1 shows the components that made a significant contribution to each model. Individuals with jargon aphasia produced more correct responses and fewer non-word errors relative to

The conditions that promote fear learning: prediction error and Pavlovian fear conditioning.

Science.gov (United States)

Li, Susan Shi Yuan; McNally, Gavan P

2014-02-01

A key insight of associative learning theory is that learning depends on the actions of prediction error: a discrepancy between the actual and expected outcomes of a conditioning trial. When positive, such error causes increments in associative strength and, when negative, such error causes decrements in associative strength. Prediction error can act directly on fear learning by determining the effectiveness of the aversive unconditioned stimulus or indirectly by determining the effectiveness, or associability, of the conditioned stimulus. Evidence from a variety of experimental preparations in human and non-human animals suggest that discrete neural circuits code for these actions of prediction error during fear learning. Here we review the circuits and brain regions contributing to the neural coding of prediction error during fear learning and highlight areas of research (safety learning, extinction, and reconsolidation) that may profit from this approach to understanding learning. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

WaLIDD score, a new tool to diagnose dysmenorrhea and predict medical leave in university students

Science.gov (United States)

Teherán, Aníbal A; Piñeros, Luis Gabriel; Pulido, Fabián; Mejía Guatibonza, María Camila

2018-01-01

Background Dysmenorrhea is a frequent and misdiagnosed symptom affecting the quality of life in young women. A working ability, location, intensity, days of pain, dysmenorrhea (WaLIDD) score was designed to diagnose dysmenorrhea and to predict medical leave. Methods This cross-sectional design included young medical students, who completed a self-administered questionnaire that contained the verbal rating score (VRS; pain and drug subscales) and WaLIDD scales. The correlation between scales was established through Spearman test. The area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and likelihood ratio (LR +/−) were evaluated to diagnose students availing medical leave due to dysmenorrhea; moreover, to predict medical leave in students with dysmenorrhea, a binary logistic regression was performed. Results In all, 585 students, with a mean age of 21 years and menarche at 12 years, participated. Most of them had regular cycles, 5 days of menstrual blood flow and 1–2 days of lower abdominal pain. The WaLIDD scale presented an adequate internal consistency and strong correlation with VRS subscales. With a cutoff of >6 for WaLIDD and 2 for VRS subscales (drug subscale and pain subscale) to identify students with dysmenorrhea, these scales presented an area under the curve (AUC) ROC of 0.82, 0.62, and 0.67, respectively. To identify students taking medical leave due to dysmenorrhea, WaLIDD (cutoff >9) and VRS subscales (cutoff >2) presented an AUC ROC of 0.97, 0.68, and 0.81; moreover, the WaLIDD scale showed a good LR +14.2 (95% CI, 13.5–14.9), LR −0.00 (95% CI, undefined), and predictive risk (OR 5.38; 95% CI, 1.78–16.2). Conclusion This research allowed a comparison between two multidimensional scales regarding their capabilities, one previously validated and a new one, to discriminate among the general population of medical students, among those with dysmenorrhea or those availing medical leave secondary to dysmenorrhea

CREME96 and Related Error Rate Prediction Methods

Science.gov (United States)

Adams, James H., Jr.

2012-01-01

Predicting the rate of occurrence of single event effects (SEEs) in space requires knowledge of the radiation environment and the response of electronic devices to that environment. Several analytical models have been developed over the past 36 years to predict SEE rates. The first error rate calculations were performed by Binder, Smith and Holman. Bradford and Pickel and Blandford, in their CRIER (Cosmic-Ray-Induced-Error-Rate) analysis code introduced the basic Rectangular ParallelePiped (RPP) method for error rate calculations. For the radiation environment at the part, both made use of the Cosmic Ray LET (Linear Energy Transfer) spectra calculated by Heinrich for various absorber Depths. A more detailed model for the space radiation environment within spacecraft was developed by Adams and co-workers. This model, together with a reformulation of the RPP method published by Pickel and Blandford, was used to create the CR ME (Cosmic Ray Effects on Micro-Electronics) code. About the same time Shapiro wrote the CRUP (Cosmic Ray Upset Program) based on the RPP method published by Bradford. It was the first code to specifically take into account charge collection from outside the depletion region due to deformation of the electric field caused by the incident cosmic ray. Other early rate prediction methods and codes include the Single Event Figure of Merit, NOVICE, the Space Radiation code and the effective flux method of Binder which is the basis of the SEFA (Scott Effective Flux Approximation) model. By the early 1990s it was becoming clear that CREME and the other early models needed Revision. This revision, CREME96, was completed and released as a WWW-based tool, one of the first of its kind. The revisions in CREME96 included improved environmental models and improved models for calculating single event effects. The need for a revision of CREME also stimulated the development of the CHIME (CRRES/SPACERAD Heavy Ion Model of the Environment) and MACREE (Modeling and

Predicting diagnostic error in Radiology via eye-tracking and image analytics: Application in mammography

Energy Technology Data Exchange (ETDEWEB)

Voisin, Sophie [ORNL; Pinto, Frank M [ORNL; Morin-Ducote, Garnetta [University of Tennessee, Knoxville (UTK); Hudson, Kathy [University of Tennessee, Knoxville (UTK); Tourassi, Georgia [ORNL

2013-01-01

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADs images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.

Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia.

Science.gov (United States)

Diuk, Carlos; Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew; Niv, Yael

2013-03-27

Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.

Science.gov (United States)

Limongi, Roberto; Silva, Angélica M

2016-11-01

The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production - where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

Signed reward prediction errors drive declarative learning

NARCIS (Netherlands)

De Loof, E.; Ergo, K.; Naert, L.; Janssens, C.; Talsma, D.; van Opstal, F.; Verguts, T.

2018-01-01

Reward prediction errors (RPEs) are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning). However, empirical evidence on whether RPEs drive declarative learning–a quintessentially human form of learning–remains surprisingly absent. We

An MEG signature corresponding to an axiomatic model of reward prediction error.

Science.gov (United States)

Talmi, Deborah; Fuentemilla, Lluis; Litvak, Vladimir; Duzel, Emrah; Dolan, Raymond J

2012-01-02

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data. Copyright © 2011 Elsevier Inc. All rights reserved.

Intermittently-visual Tracking Experiments Reveal the Roles of Error-correction and Predictive Mechanisms in the Human Visual-motor Control System

Science.gov (United States)

Hayashi, Yoshikatsu; Tamura, Yurie; Sase, Kazuya; Sugawara, Ken; Sawada, Yasuji

Prediction mechanism is necessary for human visual motion to compensate a delay of sensory-motor system. In a previous study, “proactive control” was discussed as one example of predictive function of human beings, in which motion of hands preceded the virtual moving target in visual tracking experiments. To study the roles of the positional-error correction mechanism and the prediction mechanism, we carried out an intermittently-visual tracking experiment where a circular orbit is segmented into the target-visible regions and the target-invisible regions. Main results found in this research were following. A rhythmic component appeared in the tracer velocity when the target velocity was relatively high. The period of the rhythm in the brain obtained from environmental stimuli is shortened more than 10%. The shortening of the period of rhythm in the brain accelerates the hand motion as soon as the visual information is cut-off, and causes the precedence of hand motion to the target motion. Although the precedence of the hand in the blind region is reset by the environmental information when the target enters the visible region, the hand motion precedes the target in average when the predictive mechanism dominates the error-corrective mechanism.

Suppressing my memories by listening to yours: The effect of socially triggered context-based prediction error on memory.

Science.gov (United States)

Vlasceanu, Madalina; Drach, Rae; Coman, Alin

2018-05-03

The mind is a prediction machine. In most situations, it has expectations as to what might happen. But when predictions are invalidated by experience (i.e., prediction errors), the memories that generate these predictions are suppressed. Here, we explore the effect of prediction error on listeners' memories following social interaction. We find that listening to a speaker recounting experiences similar to one's own triggers prediction errors on the part of the listener that lead to the suppression of her memories. This effect, we show, is sensitive to a perspective-taking manipulation, such that individuals who are instructed to take the perspective of the speaker experience memory suppression, whereas individuals who undergo a low-perspective-taking manipulation fail to show a mnemonic suppression effect. We discuss the relevance of these findings for our understanding of the bidirectional influences between cognition and social contexts, as well as for the real-world situations that involve memory-based predictions.

Curiosity and reward: Valence predicts choice and information prediction errors enhance learning.

Science.gov (United States)

Marvin, Caroline B; Shohamy, Daphna

2016-03-01

Curiosity drives many of our daily pursuits and interactions; yet, we know surprisingly little about how it works. Here, we harness an idea implied in many conceptualizations of curiosity: that information has value in and of itself. Reframing curiosity as the motivation to obtain reward-where the reward is information-allows one to leverage major advances in theoretical and computational mechanisms of reward-motivated learning. We provide new evidence supporting 2 predictions that emerge from this framework. First, we find an asymmetric effect of positive versus negative information, with positive information enhancing both curiosity and long-term memory for information. Second, we find that it is not the absolute value of information that drives learning but, rather, the gap between the reward expected and reward received, an "information prediction error." These results support the idea that information functions as a reward, much like money or food, guiding choices and driving learning in systematic ways. (c) 2016 APA, all rights reserved).

Development of a prototype system for prediction of the group error at the maintenance work

International Nuclear Information System (INIS)

Yoshino, Kenji; Hirotsu, Yuuko

2001-01-01

This paper described on development and performance evaluation of a prototype system for prediction of the group error at the maintenance work. The results so far are as follows. (1) When a user inputs the existence and the grade of the feature factor of the maintenance work as a prediction object, an organization and an organization factor and a group PSF put into the system. The maintenance group error to target can be predicted through the prediction model which consists of a class of seven stages. (2) This system by utilizing the information on a prediction result database, it can be use not only for prediction of a maintenance group but for various safe Activity, such as KYT(Kiken Yochi Training) and TBM(Tool Box Meeting). (3) This system predicts a cooperation error at highest rate, and subsequently. Predicts the detection error at a high rate. and to the decision-making. Error, the transfer error and the state cognitive error, and state error, it has the characteristics predicted at almost same rate. (4) if it has full knowledge even if the feature, such as the enforcement conditions of maintenance work, and organization, even if the user has neither the knowledge about a human factor, users experience, anyone of this system is slight about the extent, generating of a maintenance group error made difficult from the former logically and systematically, it can predict with business time for about 15 minutes. (author)

Working Memory Load Strengthens Reward Prediction Errors.

Science.gov (United States)

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

2017-04-19

Reinforcement learning (RL) in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors (RPEs) are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we investigated how working memory (WM) and incremental RL processes interact to guide human learning. WM load was manipulated by varying the number of stimuli to be learned across blocks. Behavioral results and computational modeling confirmed that learning was best explained as a mixture of two mechanisms: a fast, capacity-limited, and delay-sensitive WM process together with slower RL. Model-based analysis of fMRI data showed that striatum and lateral prefrontal cortex were sensitive to RPE, as shown previously, but, critically, these signals were reduced when the learning problem was within capacity of WM. The degree of this neural interaction related to individual differences in the use of WM to guide behavioral learning. These results indicate that the two systems do not process information independently, but rather interact during learning. SIGNIFICANCE STATEMENT Reinforcement learning (RL) theory has been remarkably productive at improving our understanding of instrumental learning as well as dopaminergic and striatal network function across many mammalian species. However, this neural network is only one contributor to human learning and other mechanisms such as prefrontal cortex working memory also play a key role. Our results also show that these other players interact with the dopaminergic RL system, interfering with its key computation of reward prediction errors. Copyright © 2017 the authors 0270-6474/17/374332-11$15.00/0.

Estimation of Separation Buffers for Wind-Prediction Error in an Airborne Separation Assistance System

Science.gov (United States)

Consiglio, Maria C.; Hoadley, Sherwood T.; Allen, B. Danette

2009-01-01

Wind prediction errors are known to affect the performance of automated air traffic management tools that rely on aircraft trajectory predictions. In particular, automated separation assurance tools, planned as part of the NextGen concept of operations, must be designed to account and compensate for the impact of wind prediction errors and other system uncertainties. In this paper we describe a high fidelity batch simulation study designed to estimate the separation distance required to compensate for the effects of wind-prediction errors throughout increasing traffic density on an airborne separation assistance system. These experimental runs are part of the Safety Performance of Airborne Separation experiment suite that examines the safety implications of prediction errors and system uncertainties on airborne separation assurance systems. In this experiment, wind-prediction errors were varied between zero and forty knots while traffic density was increased several times current traffic levels. In order to accurately measure the full unmitigated impact of wind-prediction errors, no uncertainty buffers were added to the separation minima. The goal of the study was to measure the impact of wind-prediction errors in order to estimate the additional separation buffers necessary to preserve separation and to provide a baseline for future analyses. Buffer estimations from this study will be used and verified in upcoming safety evaluation experiments under similar simulation conditions. Results suggest that the strategic airborne separation functions exercised in this experiment can sustain wind prediction errors up to 40kts at current day air traffic density with no additional separation distance buffer and at eight times the current day with no more than a 60% increase in separation distance buffer.

Prediction Error During Functional and Non-Functional Action Sequences

DEFF Research Database (Denmark)

Nielbo, Kristoffer Laigaard; Sørensen, Jesper

2013-01-01

recurrent networks were made and the results are presented in this article. The simulations show that non-functional action sequences do indeed increase prediction error, but that context representations, such as abstract goal information, can modulate the error signal considerably. It is also shown...... that the networks are sensitive to boundaries between sequences in both functional and non-functional actions....

Service Approaches to Young People with Complex Needs Leaving Out-of-Home Care

Science.gov (United States)

Malvaso, Catia; Delfabbro, Paul; Hackett, Louisa; Mills, Hayley

2016-01-01

Although leaving statutory out-of-home care can be a challenging time for many young people, it is recognised that young people who have multiple or complex needs find this transition particularly difficult. This study aims to gain a deeper understanding of the challenges faced by care leavers who have complex needs, as well as to identify some of…

Surprised at all the entropy: hippocampal, caudate and midbrain contributions to learning from prediction errors.

Directory of Open Access Journals (Sweden)

Anne-Marike Schiffer

Full Text Available Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts.

Surprised at all the entropy: hippocampal, caudate and midbrain contributions to learning from prediction errors.

Science.gov (United States)

Schiffer, Anne-Marike; Ahlheim, Christiane; Wurm, Moritz F; Schubotz, Ricarda I

2012-01-01

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts.

PREVAIL: Predicting Recovery through Estimation and Visualization of Active and Incident Lesions.

Science.gov (United States)

Dworkin, Jordan D; Sweeney, Elizabeth M; Schindler, Matthew K; Chahin, Salim; Reich, Daniel S; Shinohara, Russell T

2016-01-01

The goal of this study was to develop a model that integrates imaging and clinical information observed at lesion incidence for predicting the recovery of white matter lesions in multiple sclerosis (MS) patients. Demographic, clinical, and magnetic resonance imaging (MRI) data were obtained from 60 subjects with MS as part of a natural history study at the National Institute of Neurological Disorders and Stroke. A total of 401 lesions met the inclusion criteria and were used in the study. Imaging features were extracted from the intensity-normalized T1-weighted (T1w) and T2-weighted sequences as well as magnetization transfer ratio (MTR) sequence acquired at lesion incidence. T1w and MTR signatures were also extracted from images acquired one-year post-incidence. Imaging features were integrated with clinical and demographic data observed at lesion incidence to create statistical prediction models for long-term damage within the lesion. The performance of the T1w and MTR predictions was assessed in two ways: first, the predictive accuracy was measured quantitatively using leave-one-lesion-out cross-validated (CV) mean-squared predictive error. Then, to assess the prediction performance from the perspective of expert clinicians, three board-certified MS clinicians were asked to individually score how similar the CV model-predicted one-year appearance was to the true one-year appearance for a random sample of 100 lesions. The cross-validated root-mean-square predictive error was 0.95 for normalized T1w and 0.064 for MTR, compared to the estimated measurement errors of 0.48 and 0.078 respectively. The three expert raters agreed that T1w and MTR predictions closely resembled the true one-year follow-up appearance of the lesions in both degree and pattern of recovery within lesions. This study demonstrates that by using only information from a single visit at incidence, we can predict how a new lesion will recover using relatively simple statistical techniques. The

Roles of dopamine neurons in mediating the prediction error in aversive learning in insects.

Science.gov (United States)

Terao, Kanta; Mizunami, Makoto

2017-10-31

In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. The prediction error theory has been proposed to account for the finding of a blocking phenomenon, in which pairing of a stimulus X with an unconditioned stimulus (US) could block subsequent association of a second stimulus Y to the US when the two stimuli were paired in compound with the same US. Evidence for this theory, however, has been imperfect since blocking can also be accounted for by competitive theories. We recently reported blocking in classical conditioning of an odor with water reward in crickets. We also reported an "auto-blocking" phenomenon in appetitive learning, which supported the prediction error theory and rejected alternative theories. The presence of auto-blocking also suggested that octopamine neurons mediate reward prediction error signals. Here we show that blocking and auto-blocking occur in aversive learning to associate an odor with salt water (US) in crickets, and our results suggest that dopamine neurons mediate aversive prediction error signals. We conclude that the prediction error theory is applicable to both appetitive learning and aversive learning in insects.

Threat and error management for anesthesiologists: a predictive risk taxonomy

Science.gov (United States)

Ruskin, Keith J.; Stiegler, Marjorie P.; Park, Kellie; Guffey, Patrick; Kurup, Viji; Chidester, Thomas

2015-01-01

Purpose of review Patient care in the operating room is a dynamic interaction that requires cooperation among team members and reliance upon sophisticated technology. Most human factors research in medicine has been focused on analyzing errors and implementing system-wide changes to prevent them from recurring. We describe a set of techniques that has been used successfully by the aviation industry to analyze errors and adverse events and explain how these techniques can be applied to patient care. Recent findings Threat and error management (TEM) describes adverse events in terms of risks or challenges that are present in an operational environment (threats) and the actions of specific personnel that potentiate or exacerbate those threats (errors). TEM is a technique widely used in aviation, and can be adapted for the use in a medical setting to predict high-risk situations and prevent errors in the perioperative period. A threat taxonomy is a novel way of classifying and predicting the hazards that can occur in the operating room. TEM can be used to identify error-producing situations, analyze adverse events, and design training scenarios. Summary TEM offers a multifaceted strategy for identifying hazards, reducing errors, and training physicians. A threat taxonomy may improve analysis of critical events with subsequent development of specific interventions, and may also serve as a framework for training programs in risk mitigation. PMID:24113268

Cognitive emotion regulation enhances aversive prediction error activity while reducing emotional responses.

Science.gov (United States)

Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian

2015-12-01

Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

International Nuclear Information System (INIS)

Voisin, Sophie; Tourassi, Georgia D.; Pinto, Frank; Morin-Ducote, Garnetta; Hudson, Kathleen B.

2013-01-01

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content

Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

Energy Technology Data Exchange (ETDEWEB)

Voisin, Sophie; Tourassi, Georgia D. [Biomedical Science and Engineering Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Pinto, Frank [School of Engineering, Science, and Technology, Virginia State University, Petersburg, Virginia 23806 (United States); Morin-Ducote, Garnetta; Hudson, Kathleen B. [Department of Radiology, University of Tennessee Medical Center at Knoxville, Knoxville, Tennessee 37920 (United States)

2013-10-15

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.

Mathematical modeling of tetrahydroimidazole benzodiazepine-1-one derivatives as an anti HIV agent

Science.gov (United States)

Ojha, Lokendra Kumar

2017-07-01

The goal of the present work is the study of drug receptor interaction via QSAR (Quantitative Structure-Activity Relationship) analysis for 89 set of TIBO (Tetrahydroimidazole Benzodiazepine-1-one) derivatives. MLR (Multiple Linear Regression) method is utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and biological activity (IC50). The best QSAR model was selected having a correlation coefficient (r) of 0.9299 and Standard Error of Estimation (SEE) of 0.5022, Fisher Ratio (F) of 159.822 and Quality factor (Q) of 1.852. This model is statistically significant and strongly favours the substitution of sulphur atom, IS i.e. indicator parameter for -Z position of the TIBO derivatives. Two other parameter logP (octanol-water partition coefficient) and SAG (Surface Area Grid) also played a vital role in the generation of best QSAR model. All three descriptor shows very good stability towards data variation in leave-one-out (LOO).

Improved model predictive control of resistive wall modes by error field estimator in EXTRAP T2R

Science.gov (United States)

Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.

2016-12-01

Many implementations of a model-based approach for toroidal plasma have shown better control performance compared to the conventional type of feedback controller. One prerequisite of model-based control is the availability of a control oriented model. This model can be obtained empirically through a systematic procedure called system identification. Such a model is used in this work to design a model predictive controller to stabilize multiple resistive wall modes in EXTRAP T2R reversed-field pinch. Model predictive control is an advanced control method that can optimize the future behaviour of a system. Furthermore, this paper will discuss an additional use of the empirical model which is to estimate the error field in EXTRAP T2R. Two potential methods are discussed that can estimate the error field. The error field estimator is then combined with the model predictive control and yields better radial magnetic field suppression.

Reward Prediction Errors in Drug Addiction and Parkinson's Disease: from Neurophysiology to Neuroimaging.

Science.gov (United States)

García-García, Isabel; Zeighami, Yashar; Dagher, Alain

2017-06-01

Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease. By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

Error-related brain activity and error awareness in an error classification paradigm.

Science.gov (United States)

Di Gregorio, Francesco; Steinhauser, Marco; Maier, Martin E

2016-10-01

Error-related brain activity has been linked to error detection enabling adaptive behavioral adjustments. However, it is still unclear which role error awareness plays in this process. Here, we show that the error-related negativity (Ne/ERN), an event-related potential reflecting early error monitoring, is dissociable from the degree of error awareness. Participants responded to a target while ignoring two different incongruent distractors. After responding, they indicated whether they had committed an error, and if so, whether they had responded to one or to the other distractor. This error classification paradigm allowed distinguishing partially aware errors, (i.e., errors that were noticed but misclassified) and fully aware errors (i.e., errors that were correctly classified). The Ne/ERN was larger for partially aware errors than for fully aware errors. Whereas this speaks against the idea that the Ne/ERN foreshadows the degree of error awareness, it confirms the prediction of a computational model, which relates the Ne/ERN to post-response conflict. This model predicts that stronger distractor processing - a prerequisite of error classification in our paradigm - leads to lower post-response conflict and thus a smaller Ne/ERN. This implies that the relationship between Ne/ERN and error awareness depends on how error awareness is related to response conflict in a specific task. Our results further indicate that the Ne/ERN but not the degree of error awareness determines adaptive performance adjustments. Taken together, we conclude that the Ne/ERN is dissociable from error awareness and foreshadows adaptive performance adjustments. Our results suggest that the relationship between the Ne/ERN and error awareness is correlative and mediated by response conflict. Copyright © 2016 Elsevier Inc. All rights reserved.

Learning from Errors

OpenAIRE

Martínez-Legaz, Juan Enrique; Soubeyran, Antoine

2003-01-01

We present a model of learning in which agents learn from errors. If an action turns out to be an error, the agent rejects not only that action but also neighboring actions. We find that, keeping memory of his errors, under mild assumptions an acceptable solution is asymptotically reached. Moreover, one can take advantage of big errors for a faster learning.

A two-dimensional matrix correction for off-axis portal dose prediction errors

International Nuclear Information System (INIS)

Bailey, Daniel W.; Kumaraswamy, Lalith; Bakhtiari, Mohammad; Podgorsak, Matthew B.

2013-01-01

Purpose: This study presents a follow-up to a modified calibration procedure for portal dosimetry published by Bailey et al. [“An effective correction algorithm for off-axis portal dosimetry errors,” Med. Phys. 36, 4089–4094 (2009)]. A commercial portal dose prediction system exhibits disagreement of up to 15% (calibrated units) between measured and predicted images as off-axis distance increases. The previous modified calibration procedure accounts for these off-axis effects in most regions of the detecting surface, but is limited by the simplistic assumption of radial symmetry. Methods: We find that a two-dimensional (2D) matrix correction, applied to each calibrated image, accounts for off-axis prediction errors in all regions of the detecting surface, including those still problematic after the radial correction is performed. The correction matrix is calculated by quantitative comparison of predicted and measured images that span the entire detecting surface. The correction matrix was verified for dose-linearity, and its effectiveness was verified on a number of test fields. The 2D correction was employed to retrospectively examine 22 off-axis, asymmetric electronic-compensation breast fields, five intensity-modulated brain fields (moderate-high modulation) manipulated for far off-axis delivery, and 29 intensity-modulated clinical fields of varying complexity in the central portion of the detecting surface. Results: Employing the matrix correction to the off-axis test fields and clinical fields, predicted vs measured portal dose agreement improves by up to 15%, producing up to 10% better agreement than the radial correction in some areas of the detecting surface. Gamma evaluation analyses (3 mm, 3% global, 10% dose threshold) of predicted vs measured portal dose images demonstrate pass rate improvement of up to 75% with the matrix correction, producing pass rates that are up to 30% higher than those resulting from the radial correction technique alone. As

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa.

Science.gov (United States)

DeGuzman, Marisa; Shott, Megan E; Yang, Tony T; Riederer, Justin; Frank, Guido K W

2017-06-01

Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Female adolescents with anorexia nervosa (N=21; mean age, 16.4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 15.2 years [SD=2.4]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs.

Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures.

Science.gov (United States)

Keller, Joshua P; Chang, Howard H; Strickland, Matthew J; Szpiro, Adam A

2017-05-01

Air pollution cohort studies are frequently analyzed in two stages, first modeling exposure then using predicted exposures to estimate health effects in a second regression model. The difference between predicted and unobserved true exposures introduces a form of measurement error in the second stage health model. Recent methods for spatial data correct for measurement error with a bootstrap and by requiring the study design ensure spatial compatibility, that is, monitor and subject locations are drawn from the same spatial distribution. These methods have not previously been applied to spatiotemporal exposure data. We analyzed the association between fine particulate matter (PM2.5) and birth weight in the US state of Georgia using records with estimated date of conception during 2002-2005 (n = 403,881). We predicted trimester-specific PM2.5 exposure using a complex spatiotemporal exposure model. To improve spatial compatibility, we restricted to mothers residing in counties with a PM2.5 monitor (n = 180,440). We accounted for additional measurement error via a nonparametric bootstrap. Third trimester PM2.5 exposure was associated with lower birth weight in the uncorrected (-2.4 g per 1 μg/m difference in exposure; 95% confidence interval [CI]: -3.9, -0.8) and bootstrap-corrected (-2.5 g, 95% CI: -4.2, -0.8) analyses. Results for the unrestricted analysis were attenuated (-0.66 g, 95% CI: -1.7, 0.35). This study presents a novel application of measurement error correction for spatiotemporal air pollution exposures. Our results demonstrate the importance of spatial compatibility between monitor and subject locations and provide evidence of the association between air pollution exposure and birth weight.

Error analysis of short term wind power prediction models

International Nuclear Information System (INIS)

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)

Error analysis of short term wind power prediction models

Energy Technology Data Exchange (ETDEWEB)

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)

Mean Bias in Seasonal Forecast Model and ENSO Prediction Error.

Science.gov (United States)

Kim, Seon Tae; Jeong, Hye-In; Jin, Fei-Fei

2017-07-20

This study uses retrospective forecasts made using an APEC Climate Center seasonal forecast model to investigate the cause of errors in predicting the amplitude of El Niño Southern Oscillation (ENSO)-driven sea surface temperature variability. When utilizing Bjerknes coupled stability (BJ) index analysis, enhanced errors in ENSO amplitude with forecast lead times are found to be well represented by those in the growth rate estimated by the BJ index. ENSO amplitude forecast errors are most strongly associated with the errors in both the thermocline slope response and surface wind response to forcing over the tropical Pacific, leading to errors in thermocline feedback. This study concludes that upper ocean temperature bias in the equatorial Pacific, which becomes more intense with increasing lead times, is a possible cause of forecast errors in the thermocline feedback and thus in ENSO amplitude.

Testing the prediction error difference between two predictors

NARCIS (Netherlands)

van de Wiel, M.A.; Berkhof, J.; van Wieringen, W.N.

2009-01-01

We develop an inference framework for the difference in errors between 2 prediction procedures. The 2 procedures may differ in any aspect and possibly utilize different sets of covariates. We apply training and testing on the same data set, which is accommodated by sample splitting. For each split,

Motivational state controls the prediction error in Pavlovian appetitive-aversive interactions.

Science.gov (United States)

Laurent, Vincent; Balleine, Bernard W; Westbrook, R Frederick

2018-01-01

Contemporary theories of learning emphasize the role of a prediction error signal in driving learning, but the nature of this signal remains hotly debated. Here, we used Pavlovian conditioning in rats to investigate whether primary motivational and emotional states interact to control prediction error. We initially generated cues that positively or negatively predicted an appetitive food outcome. We then assessed how these cues modulated aversive conditioning when a novel cue was paired with a foot shock. We found that a positive predictor of food enhances, whereas a negative predictor of that same food impairs, aversive conditioning. Critically, we also showed that the enhancement produced by the positive predictor is removed by reducing the value of its associated food. In contrast, the impairment triggered by the negative predictor remains insensitive to devaluation of its associated food. These findings provide compelling evidence that the motivational value attributed to a predicted food outcome can directly control appetitive-aversive interactions and, therefore, that motivational processes can modulate emotional processes to generate the final error term on which subsequent learning is based. Copyright © 2017 Elsevier Inc. All rights reserved.

Accuracy Enhancement with Processing Error Prediction and Compensation of a CNC Flame Cutting Machine Used in Spatial Surface Operating Conditions

Directory of Open Access Journals (Sweden)

Shenghai Hu

2017-04-01

Full Text Available This study deals with the precision performance of the CNC flame-cutting machine used in spatial surface operating conditions and presents an accuracy enhancement method based on processing error modeling prediction and real-time compensation. Machining coordinate systems and transformation matrix models were established for the CNC flame processing system considering both geometric errors and thermal deformation effects. Meanwhile, prediction and compensation models were constructed related to the actual cutting situation. Focusing on the thermal deformation elements, finite element analysis was used to measure the testing data of thermal errors, the grey system theory was applied to optimize the key thermal points, and related thermal dynamics models were carried out to achieve high-precision prediction values. Comparison experiments between the proposed method and the teaching method were conducted on the processing system after performing calibration. The results showed that the proposed method is valid and the cutting quality could be improved by more than 30% relative to the teaching method. Furthermore, the proposed method can be used under any working condition by making a few adjustments to the prediction and compensation models.

Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

Science.gov (United States)

Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean

2016-01-01

A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

Human error prediction and countermeasures based on CREAM in spent nuclear fuel (SNF) transportation

International Nuclear Information System (INIS)

Kim, Jae San

2007-02-01

Since the 1980s, in order to secure the storage capacity of spent nuclear fuel (SNF) at NPPs, SNF assemblies have been transported on-site from one unit to another unit nearby. However in the future the amount of the spent fuel will approach capacity in the areas used, and some of these SNFs will have to be transported to an off-site spent fuel repository. Most SNF materials used at NPPs will be transported by general cargo ships from abroad, and these SNFs will be stored in an interim storage facility. In the process of transporting SNF, human interactions will involve inspecting and preparing the cask and spent fuel, loading the cask onto the vehicle or ship, transferring the cask as well as storage or monitoring the cask. The transportation of SNF involves a number of activities that depend on reliable human performance. In the case of the transport of a cask, human errors may include spent fuel bundle misidentification or cask transport accidents among others. Reviews of accident events when transporting the Radioactive Material (RAM) throughout the world indicate that human error is the major causes for more than 65% of significant events. For the safety of SNF transportation, it is very important to predict human error and to deduce a method that minimizes the human error. This study examines the human factor effects on the safety of transporting spent nuclear fuel (SNF). It predicts and identifies the possible human errors in the SNF transport process (loading, transfer and storage of the SNF). After evaluating the human error mode in each transport process, countermeasures to minimize the human error are deduced. The human errors in SNF transportation were analyzed using Hollnagel's Cognitive Reliability and Error Analysis Method (CREAM). After determining the important factors for each process, countermeasures to minimize human error are provided in three parts: System design, Operational environment, and Human ability

One-Step-Ahead Predictive Control for Hydroturbine Governor

Directory of Open Access Journals (Sweden)

Zhihuai Xiao

2015-01-01

Full Text Available The hydroturbine generator regulating system can be considered as one system synthetically integrating water, machine, and electricity. It is a complex and nonlinear system, and its configuration and parameters are time-dependent. A one-step-ahead predictive control based on on-line trained neural networks (NNs for hydroturbine governor with variation in gate position is described in this paper. The proposed control algorithm consists of a one-step-ahead neuropredictor that tracks the dynamic characteristics of the plant and predicts its output and a neurocontroller to generate the optimal control signal. The weights of two NNs, initially trained off-line, are updated on-line according to the scalar error. The proposed controller can thus track operating conditions in real-time and produce the optimal control signal over the wide operating range. Only the inputs and outputs of the generator are measured and there is no need to determine the other states of the generator. Simulations have been performed with varying operating conditions and different disturbances to compare the performance of the proposed controller with that of a conventional PID controller and validate the feasibility of the proposed approach.

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

Science.gov (United States)

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

Prediction of the GC-MS Retention Indices for a Diverse Set of Terpenes as Constituent Components of Camu-camu (Myrciaria dubia (HBK Mc Vaugh Volatile Oil, Using Particle Swarm Optimization-Multiple Linear Regression (PSO-MLR

Directory of Open Access Journals (Sweden)

Majid Mohammadhosseini

2014-05-01

Full Text Available A reliable quantitative structure retention relationship (QSRR study has been evaluated to predict the retention indices (RIs of a broad spectrum of compounds, namely 118 non-linear, cyclic and heterocyclic terpenoids (both saturated and unsaturated, on an HP-5MS fused silica column. A principal component analysis showed that seven compounds lay outside of the main cluster. After elimination of the outliers, the data set was divided into training and test sets involving 80 and 28 compounds. The method was tested by application of the particle swarm optimization (PSO method to find the most effective molecular descriptors, followed by multiple linear regressions (MLR. The PSO-MLR model was further confirmed through “leave one out cross validation” (LOO-CV and “leave group out cross validation” (LGO-CV, as well as external validations. The promising statistical figures of merit associated with the proposed model (R2train=0.936, Q2LOO=0.928, Q2LGO=0.921, F=376.4 confirm its high ability to predict RIs with negligible relative errors of predictions (REP train=4.8%, REP test=6.0%.

Mini-review: Prediction errors, attention and associative learning.

Science.gov (United States)

Holland, Peter C; Schiffino, Felipe L

2016-05-01

Most modern theories of associative learning emphasize a critical role for prediction error (PE, the difference between received and expected events). One class of theories, exemplified by the Rescorla-Wagner (1972) model, asserts that PE determines the effectiveness of the reinforcer or unconditioned stimulus (US): surprising reinforcers are more effective than expected ones. A second class, represented by the Pearce-Hall (1980) model, argues that PE determines the associability of conditioned stimuli (CSs), the rate at which they may enter into new learning: the surprising delivery or omission of a reinforcer enhances subsequent processing of the CSs that were present when PE was induced. In this mini-review we describe evidence, mostly from our laboratory, for PE-induced changes in the associability of both CSs and USs, and the brain systems involved in the coding, storage and retrieval of these altered associability values. This evidence favors a number of modifications to behavioral models of how PE influences event processing, and suggests the involvement of widespread brain systems in animals' responses to PE. Copyright © 2016 Elsevier Inc. All rights reserved.

Development and performance evaluation of a prototype system for prediction of the group error at the maintenance work

International Nuclear Information System (INIS)

Yoshino, Kenji; Hirotsu, Yuko

2000-01-01

In order to attain zero-izing of much more error rather than it can set to a nuclear power plant, Authors development and its system-izing of the error prediction causal model which predicts group error action at the time of maintenance work were performed. This prototype system has the following feature. (1) When a user inputs the existence and the grade of the existence of the 'feature factor of the maintenance work' as a prediction object, 'an organization and an organization factor', and a 'group PSF (Performance Shaping Factor) factor' into this system. The maintenance group error to target can be predicted through the prediction model which consists of a class of seven stages. (2) This system by utilizing the information on a prediction result database, it can use not only for prediction of a maintenance group error but for various safe activity, such as KYT (dangerous forecast training) and TBM (Tool Box Meeting). (3) This system predicts a cooperation error' at highest rate, and, subsequently predicts the detection error' at a high rate. And to the 'decision-making error', the transfer error' and the 'state cognitive error', it has the characteristic predicted at almost same rate. (4) If it has full knowledge even of the features, such as the enforcement conditions of maintenance work, and organization, even if the user has neither the knowledge about a human factor, nor experience, anyone of this system is slight about the existence, its extent, etc. of generating of a maintenance group error made difficult from the former logically and systematically easily, it can predict in business time for about 15 minutes. (author)

SU-F-J-208: Prompt Gamma Imaging-Based Prediction of Bragg Peak Position for Realistic Treatment Error Scenarios

Energy Technology Data Exchange (ETDEWEB)

Xing, Y; Macq, B; Bondar, L [Universite catholique de Louvain, Louvain-la-Neuve (Belgium); Janssens, G [IBA, Louvain-la-Neuve (Belgium)

2016-06-15

Purpose: To quantify the accuracy in predicting the Bragg peak position using simulated in-room measurements of prompt gamma (PG) emissions for realistic treatment error scenarios that combine several sources of errors. Methods: Prompt gamma measurements by a knife-edge slit camera were simulated using an experimentally validated analytical simulation tool. Simulations were performed, for 143 treatment error scenarios, on an anthropomorphic phantom and a pencil beam scanning plan for nasal cavity. Three types of errors were considered: translation along each axis, rotation around each axis, and CT-calibration errors with magnitude ranging respectively, between −3 and 3 mm, −5 and 5 degrees, and between −5 and +5%. We investigated the correlation between the Bragg peak (BP) shift and the horizontal shift of PG profiles. The shifts were calculated between the planned (reference) position and the position by the error scenario. The prediction error for one spot was calculated as the absolute difference between the PG profile shift and the BP shift. Results: The PG shift was significantly and strongly correlated with the BP shift for 92% of the cases (p<0.0001, Pearson correlation coefficient R>0.8). Moderate but significant correlations were obtained for all cases that considered only CT-calibration errors and for 1 case that combined translation and CT-errors (p<0.0001, R ranged between 0.61 and 0.8). The average prediction errors for the simulated scenarios ranged between 0.08±0.07 and 1.67±1.3 mm (grand mean 0.66±0.76 mm). The prediction error was moderately correlated with the value of the BP shift (p=0, R=0.64). For the simulated scenarios the average BP shift ranged between −8±6.5 mm and 3±1.1 mm. Scenarios that considered combinations of the largest treatment errors were associated with large BP shifts. Conclusion: Simulations of in-room measurements demonstrate that prompt gamma profiles provide reliable estimation of the Bragg peak position for

Statistical errors in Monte Carlo estimates of systematic errors

Energy Technology Data Exchange (ETDEWEB)

Roe, Byron P. [Department of Physics, University of Michigan, Ann Arbor, MI 48109 (United States)]. E-mail: byronroe@umich.edu

2007-01-01

For estimating the effects of a number of systematic errors on a data sample, one can generate Monte Carlo (MC) runs with systematic parameters varied and examine the change in the desired observed result. Two methods are often used. In the unisim method, the systematic parameters are varied one at a time by one standard deviation, each parameter corresponding to a MC run. In the multisim method (see ), each MC run has all of the parameters varied; the amount of variation is chosen from the expected distribution of each systematic parameter, usually assumed to be a normal distribution. The variance of the overall systematic error determination is derived for each of the two methods and comparisons are made between them. If one focuses not on the error in the prediction of an individual systematic error, but on the overall error due to all systematic errors in the error matrix element in data bin m, the number of events needed is strongly reduced because of the averaging effect over all of the errors. For simple models presented here the multisim model was far better if the statistical error in the MC samples was larger than an individual systematic error, while for the reverse case, the unisim model was better. Exact formulas and formulas for the simple toy models are presented so that realistic calculations can be made. The calculations in the present note are valid if the errors are in a linear region. If that region extends sufficiently far, one can have the unisims or multisims correspond to k standard deviations instead of one. This reduces the number of events required by a factor of k{sup 2}.

Statistical errors in Monte Carlo estimates of systematic errors

International Nuclear Information System (INIS)

Roe, Byron P.

2007-01-01

For estimating the effects of a number of systematic errors on a data sample, one can generate Monte Carlo (MC) runs with systematic parameters varied and examine the change in the desired observed result. Two methods are often used. In the unisim method, the systematic parameters are varied one at a time by one standard deviation, each parameter corresponding to a MC run. In the multisim method (see ), each MC run has all of the parameters varied; the amount of variation is chosen from the expected distribution of each systematic parameter, usually assumed to be a normal distribution. The variance of the overall systematic error determination is derived for each of the two methods and comparisons are made between them. If one focuses not on the error in the prediction of an individual systematic error, but on the overall error due to all systematic errors in the error matrix element in data bin m, the number of events needed is strongly reduced because of the averaging effect over all of the errors. For simple models presented here the multisim model was far better if the statistical error in the MC samples was larger than an individual systematic error, while for the reverse case, the unisim model was better. Exact formulas and formulas for the simple toy models are presented so that realistic calculations can be made. The calculations in the present note are valid if the errors are in a linear region. If that region extends sufficiently far, one can have the unisims or multisims correspond to k standard deviations instead of one. This reduces the number of events required by a factor of k 2

Period, epoch, and prediction errors of ephemerides from continuous sets of timing measurements

Science.gov (United States)

Deeg, H. J.

2015-06-01

Space missions such as Kepler and CoRoT have led to large numbers of eclipse or transit measurements in nearly continuous time series. This paper shows how to obtain the period error in such measurements from a basic linear least-squares fit, and how to correctly derive the timing error in the prediction of future transit or eclipse events. Assuming strict periodicity, a formula for the period error of these time series is derived, σP = σT (12 / (N3-N))1 / 2, where σP is the period error, σT the timing error of a single measurement, and N the number of measurements. Compared to the iterative method for period error estimation by Mighell & Plavchan (2013), this much simpler formula leads to smaller period errors, whose correctness has been verified through simulations. For the prediction of times of future periodic events, usual linear ephemeris were epoch errors are quoted for the first time measurement, are prone to an overestimation of the error of that prediction. This may be avoided by a correction for the duration of the time series. An alternative is the derivation of ephemerides whose reference epoch and epoch error are given for the centre of the time series. For long continuous or near-continuous time series whose acquisition is completed, such central epochs should be the preferred way for the quotation of linear ephemerides. While this work was motivated from the analysis of eclipse timing measures in space-based light curves, it should be applicable to any other problem with an uninterrupted sequence of discrete timings for which the determination of a zero point, of a constant period and of the associated errors is needed.

Prediction-error identification of LPV systems : present and beyond

NARCIS (Netherlands)

Toth, R.; Heuberger, P.S.C.; Hof, Van den P.M.J.; Mohammadpour, J.; Scherer, C. W.

2012-01-01

The proposed chapter aims at presenting a unified framework of prediction-error based identification of LPV systems using freshly developed theoretical results. Recently, these methods have got a considerable attention as they have certain advantages in terms of computational complexity, optimality

[Determination of steviol in Stevia Rebaudiana leaves by near infrared spectroscopy].

Science.gov (United States)

Tang, Qi-Kun; Wang, Yul; Wu, Yue-Jin; Min, Di; Chen, Da-Wei; Hu, Tong-Hua

2014-10-01

The objective of the present study is to develop a method for rapid determination of the content of stevioside (ST) and rebaudioside A (RA) in Stevia Rebaudiana leaves. One hundred and five samples of stevia from different areas containing ST of 0.27%-1.40% and RA of 0.61%-3.98% were used. The 105 groups' NIRS diagram was processed by different methods including subtracting a straight line (SLS), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD) and so on, and then all data were analyzed by partial least square (PLS). The study showed that SLS can be used to extracted spectra information thoroughly to analyze the contents of ST, the correlation coefficients of calibration (Re), the root-mean-square errors of calibration (RMSEC) and prediction (RMSEP), and the residual predictive deviation (RPD) were 0.986, 0.341, 1.00 and 2.8, respectively. The correlation coefficients of RA was 0.967, RMSEC was 1.50, RMSEP was 1.98 and RPD was 4.17. The results indicated that near infrared spectroscopy (NIRS) technique offers effective quantitative capability for ST and RA in Stevia Rebaudiana leaves. Then the model of stevia dried leaves was used to compare with the stevia powder near infrared model whose correlation coefficients of ST was 0.986, RMSEC was 0.32, RMSEP was 0.601 and RPD was 2.86 and the correlation coefficients of RA was 0.968, RMSEC was 1.50, RMSEP was 1.48 and RPD was 4.2. The result showed that there was no significant difference between the model of dried leaves and that of the powders. However, the dried leaves NIR model reduces the unnecessary the steps of drying and grinding in the actual detection process, saving the time and reducing the workload.

Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains.

Science.gov (United States)

Schroeder, Scott R; Salomon, Meghan M; Galanter, William L; Schiff, Gordon D; Vaida, Allen J; Gaunt, Michael J; Bryson, Michelle L; Rash, Christine; Falck, Suzanne; Lambert, Bruce L

2017-05-01

Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors. Published by the BMJ

JCSC_128_4_633-647_SI.docx

Indian Academy of Sciences (India)

Dell

R2, , PRESS (predicted residual sum of squares) and F values, S3-S4. List of compounds selected as member of test set, S4. Root of squared error and linear correlation For Table 1 in leave-one-out validation method (Figure S1), S5. Root of squared error and linear correlation For Table 1 for doing regression (Figure S2) ...

Prediction and error of baldcypress stem volume from stump diameter

Science.gov (United States)

Bernard R. Parresol

1998-01-01

The need to estimate the volume of removals occurs for many reasons, such as in trespass cases, severance tax reports, and post-harvest assessments. A logarithmic model is presented for prediction of baldcypress total stem cubic foot volume using stump diameter as the independent variable. Because the error of prediction is as important as the volume estimate, the...

Practical guidance on representing the heteroscedasticity of residual errors of hydrological predictions

Science.gov (United States)

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Kuczera, George

2016-04-01

Appropriate representation of residual errors in hydrological modelling is essential for accurate and reliable probabilistic streamflow predictions. In particular, residual errors of hydrological predictions are often heteroscedastic, with large errors associated with high runoff events. Although multiple approaches exist for representing this heteroscedasticity, few if any studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating a range of approaches for representing heteroscedasticity in residual errors. These approaches include the 'direct' weighted least squares approach and 'transformational' approaches, such as logarithmic, Box-Cox (with and without fitting the transformation parameter), logsinh and the inverse transformation. The study reports (1) theoretical comparison of heteroscedasticity approaches, (2) empirical evaluation of heteroscedasticity approaches using a range of multiple catchments / hydrological models / performance metrics and (3) interpretation of empirical results using theory to provide practical guidance on the selection of heteroscedasticity approaches. Importantly, for hydrological practitioners, the results will simplify the choice of approaches to represent heteroscedasticity. This will enhance their ability to provide hydrological probabilistic predictions with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality).

Uncertainties of predictions from parton distributions 1, experimental errors

CERN Document Server

Martin, A D; Stirling, William James; Thorne, R S; CERN. Geneva

2003-01-01

We determine the uncertainties on observables arising from the errors on the experimental data that are fitted in the global MRST2001 parton analysis. By diagonalizing the error matrix we produce sets of partons suitable for use within the framework of linear propagation of errors, which is the most convenient method for calculating the uncertainties. Despite the potential limitations of this approach we find that it can be made to work well in practice. This is confirmed by our alternative approach of using the more rigorous Lagrange multiplier method to determine the errors on physical quantities directly. As particular examples we determine the uncertainties on the predictions of the charged-current deep-inelastic structure functions, on the cross-sections for W production and for Higgs boson production via gluon--gluon fusion at the Tevatron and the LHC, on the ratio of W-minus to W-plus production at the LHC and on the moments of the non-singlet quark distributions. We discuss the corresponding uncertain...

Reversible Watermarking Using Prediction-Error Expansion and Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Guangyong Gao

2015-01-01

Full Text Available Currently, the research for reversible watermarking focuses on the decreasing of image distortion. Aiming at this issue, this paper presents an improvement method to lower the embedding distortion based on the prediction-error expansion (PE technique. Firstly, the extreme learning machine (ELM with good generalization ability is utilized to enhance the prediction accuracy for image pixel value during the watermarking embedding, and the lower prediction error results in the reduction of image distortion. Moreover, an optimization operation for strengthening the performance of ELM is taken to further lessen the embedding distortion. With two popular predictors, that is, median edge detector (MED predictor and gradient-adjusted predictor (GAP, the experimental results for the classical images and Kodak image set indicate that the proposed scheme achieves improvement for the lowering of image distortion compared with the classical PE scheme proposed by Thodi et al. and outperforms the improvement method presented by Coltuc and other existing approaches.

Dopamine reward prediction error responses reflect marginal utility.

Science.gov (United States)

Stauffer, William R; Lak, Armin; Schultz, Wolfram

2014-11-03

Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions' shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Analysts forecast error : A robust prediction model and its short term trading

NARCIS (Netherlands)

Boudt, Kris; de Goeij, Peter; Thewissen, James; Van Campenhout, Geert

We examine the profitability of implementing a short term trading strategy based on predicting the error in analysts' earnings per share forecasts using publicly available information. Since large earnings surprises may lead to extreme values in the forecast error series that disrupt their smooth

Estimation of Mechanical Signals in Induction Motors using the Recursive Prediction Error Method

DEFF Research Database (Denmark)

Børsting, H.; Knudsen, Morten; Rasmussen, Henrik

1993-01-01

Sensor feedback of mechanical quantities for control applications in induction motors is troublesome and relative expensive. In this paper a recursive prediction error (RPE) method has successfully been used to estimate the angular rotor speed ........Sensor feedback of mechanical quantities for control applications in induction motors is troublesome and relative expensive. In this paper a recursive prediction error (RPE) method has successfully been used to estimate the angular rotor speed .....

Remote one-qubit information concentration and decoding of operator quantum error-correction codes

International Nuclear Information System (INIS)

Hsu Liyi

2007-01-01

We propose the general scheme of remote one-qubit information concentration. To achieve the task, the Bell-correlated mixed states are exploited. In addition, the nonremote one-qubit information concentration is equivalent to the decoding of the quantum error-correction code. Here we propose how to decode the stabilizer codes. In particular, the proposed scheme can be used for the operator quantum error-correction codes. The encoded state can be recreated on the errorless qubit, regardless how many bit-flip errors and phase-flip errors have occurred

Current error vector based prediction control of the section winding permanent magnet linear synchronous motor

Energy Technology Data Exchange (ETDEWEB)

Hong Junjie, E-mail: hongjjie@mail.sysu.edu.cn [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China); Li Liyi, E-mail: liliyi@hit.edu.cn [Dept. Electrical Engineering, Harbin Institute of Technology, Harbin 150000 (China); Zong Zhijian; Liu Zhongtu [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China)

2011-10-15

Highlights: {yields} The structure of the permanent magnet linear synchronous motor (SW-PMLSM) is new. {yields} A new current control method CEVPC is employed in this motor. {yields} The sectional power supply method is different to the others and effective. {yields} The performance gets worse with voltage and current limitations. - Abstract: To include features such as greater thrust density, higher efficiency without reducing the thrust stability, this paper proposes a section winding permanent magnet linear synchronous motor (SW-PMLSM), whose iron core is continuous, whereas winding is divided. The discrete system model of the motor is derived. With the definition of the current error vector and selection of the value function, the theory of the current error vector based prediction control (CEVPC) for the motor currents is explained clearly. According to the winding section feature, the motion region of the mover is divided into five zones, in which the implementation of the current predictive control method is proposed. Finally, the experimental platform is constructed and experiments are carried out. The results show: the current control effect has good dynamic response, and the thrust on the mover remains constant basically.

The Human Bathtub: Safety and Risk Predictions Including the Dynamic Probability of Operator Errors

International Nuclear Information System (INIS)

Duffey, Romney B.; Saull, John W.

2006-01-01

Reactor safety and risk are dominated by the potential and major contribution for human error in the design, operation, control, management, regulation and maintenance of the plant, and hence to all accidents. Given the possibility of accidents and errors, now we need to determine the outcome (error) probability, or the chance of failure. Conventionally, reliability engineering is associated with the failure rate of components, or systems, or mechanisms, not of human beings in and interacting with a technological system. The probability of failure requires a prior knowledge of the total number of outcomes, which for any predictive purposes we do not know or have. Analysis of failure rates due to human error and the rate of learning allow a new determination of the dynamic human error rate in technological systems, consistent with and derived from the available world data. The basis for the analysis is the 'learning hypothesis' that humans learn from experience, and consequently the accumulated experience defines the failure rate. A new 'best' equation has been derived for the human error, outcome or failure rate, which allows for calculation and prediction of the probability of human error. We also provide comparisons to the empirical Weibull parameter fitting used in and by conventional reliability engineering and probabilistic safety analysis methods. These new analyses show that arbitrary Weibull fitting parameters and typical empirical hazard function techniques cannot be used to predict the dynamics of human errors and outcomes in the presence of learning. Comparisons of these new insights show agreement with human error data from the world's commercial airlines, the two shuttle failures, and from nuclear plant operator actions and transient control behavior observed in transients in both plants and simulators. The results demonstrate that the human error probability (HEP) is dynamic, and that it may be predicted using the learning hypothesis and the minimum

Influence of precision of emission characteristic parameters on model prediction error of VOCs/formaldehyde from dry building material.

Directory of Open Access Journals (Sweden)

Wenjuan Wei

Full Text Available Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0, the diffusion coefficient (D, and the partition coefficient (K, can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C.

Haptic Data Processing for Teleoperation Systems: Prediction, Compression and Error Correction

OpenAIRE

Lee, Jae-young

2013-01-01

This thesis explores haptic data processing methods for teleoperation systems, including prediction, compression, and error correction. In the proposed haptic data prediction method, unreliable network conditions, such as time-varying delay and packet loss, are detected by a transport layer protocol. Given the information from the transport layer, a Bayesian approach is introduced to predict position and force data in haptic teleoperation systems. Stability of the proposed method within stoch...

Real-time prediction of atmospheric Lagrangian coherent structures based on forecast data: An application and error analysis

Science.gov (United States)

BozorgMagham, Amir E.; Ross, Shane D.; Schmale, David G.

2013-09-01

The language of Lagrangian coherent structures (LCSs) provides a new means for studying transport and mixing of passive particles advected by an atmospheric flow field. Recent observations suggest that LCSs govern the large-scale atmospheric motion of airborne microorganisms, paving the way for more efficient models and management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. In addition, having reliable predictions of the timing of hyperbolic LCSs may contribute to improved aerobiological sampling of microorganisms with unmanned aerial vehicles and LCS-based early warning systems. Chaotic atmospheric dynamics lead to unavoidable forecasting errors in the wind velocity field, which compounds errors in LCS forecasting. In this study, we reveal the cumulative effects of errors of (short-term) wind field forecasts on the finite-time Lyapunov exponent (FTLE) fields and the associated LCSs when realistic forecast plans impose certain limits on the forecasting parameters. Objectives of this paper are to (a) quantify the accuracy of prediction of FTLE-LCS features and (b) determine the sensitivity of such predictions to forecasting parameters. Results indicate that forecasts of attracting LCSs exhibit less divergence from the archive-based LCSs than the repelling features. This result is important since attracting LCSs are the backbone of long-lived features in moving fluids. We also show under what circumstances one can trust the forecast results if one merely wants to know if an LCS passed over a region and does not need to precisely know the passage time.

Regression model of support vector machines for least squares prediction of crystallinity of cracking catalysts by infrared spectroscopy

International Nuclear Information System (INIS)

Comesanna Garcia, Yumirka; Dago Morales, Angel; Talavera Bustamante, Isneri

2010-01-01

The recently introduction of the least squares support vector machines method for regression purposes in the field of Chemometrics has provided several advantages to linear and nonlinear multivariate calibration methods. The objective of the paper was to propose the use of the least squares support vector machine as an alternative multivariate calibration method for the prediction of the percentage of crystallinity of fluidized catalytic cracking catalysts, by means of Fourier transform mid-infrared spectroscopy. A linear kernel was used in the calculations of the regression model. The optimization of its gamma parameter was carried out using the leave-one-out cross-validation procedure. The root mean square error of prediction was used to measure the performance of the model. The accuracy of the results obtained with the application of the method is in accordance with the uncertainty of the X-ray powder diffraction reference method. To compare the generalization capability of the developed method, a comparison study was carried out, taking into account the results achieved with the new model and those reached through the application of linear calibration methods. The developed method can be easily implemented in refinery laboratories

One- and two-stage Arrhenius models for pharmaceutical shelf life prediction.

Science.gov (United States)

Fan, Zhewen; Zhang, Lanju

2015-01-01

One of the most challenging aspects of the pharmaceutical development is the demonstration and estimation of chemical stability. It is imperative that pharmaceutical products be stable for two or more years. Long-term stability studies are required to support such shelf life claim at registration. However, during drug development to facilitate formulation and dosage form selection, an accelerated stability study with stressed storage condition is preferred to quickly obtain a good prediction of shelf life under ambient storage conditions. Such a prediction typically uses Arrhenius equation that describes relationship between degradation rate and temperature (and humidity). Existing methods usually rely on the assumption of normality of the errors. In addition, shelf life projection is usually based on confidence band of a regression line. However, the coverage probability of a method is often overlooked or under-reported. In this paper, we introduce two nonparametric bootstrap procedures for shelf life estimation based on accelerated stability testing, and compare them with a one-stage nonlinear Arrhenius prediction model. Our simulation results demonstrate that one-stage nonlinear Arrhenius method has significant lower coverage than nominal levels. Our bootstrap method gave better coverage and led to a shelf life prediction closer to that based on long-term stability data.

Statistical errors in Monte Carlo estimates of systematic errors

Science.gov (United States)

Roe, Byron P.

2007-01-01

For estimating the effects of a number of systematic errors on a data sample, one can generate Monte Carlo (MC) runs with systematic parameters varied and examine the change in the desired observed result. Two methods are often used. In the unisim method, the systematic parameters are varied one at a time by one standard deviation, each parameter corresponding to a MC run. In the multisim method (see ), each MC run has all of the parameters varied; the amount of variation is chosen from the expected distribution of each systematic parameter, usually assumed to be a normal distribution. The variance of the overall systematic error determination is derived for each of the two methods and comparisons are made between them. If one focuses not on the error in the prediction of an individual systematic error, but on the overall error due to all systematic errors in the error matrix element in data bin m, the number of events needed is strongly reduced because of the averaging effect over all of the errors. For simple models presented here the multisim model was far better if the statistical error in the MC samples was larger than an individual systematic error, while for the reverse case, the unisim model was better. Exact formulas and formulas for the simple toy models are presented so that realistic calculations can be made. The calculations in the present note are valid if the errors are in a linear region. If that region extends sufficiently far, one can have the unisims or multisims correspond to k standard deviations instead of one. This reduces the number of events required by a factor of k2. The specific terms unisim and multisim were coined by Peter Meyers and Steve Brice, respectively, for the MiniBooNE experiment. However, the concepts have been developed over time and have been in general use for some time.

Burned Out Or Just Frustrated? Reasons Why Physical Education Teachers Leave Their Profession

Directory of Open Access Journals (Sweden)

Cieśliński Ryszard

2014-09-01

Full Text Available This work focuses on the reasons why physical education (PE teachers leave their profession. The study included 80 individuals who decided to leave a teaching profession in 2013. A diagnostic poll method with the use of the QWL (Quality of Work Life index was employed in the study. It was observed that there are usually a number of reasons why they give up their job, the most important being financial reasons. Their decision is influenced by the accumulation of professional and personal problems as well as their inability to solve them. The findings showed that teachers‘ departure from the profession is generally associated with the issue of burnout; however, financial reasons are most frequently ones that directly affect this decision.

Analysts’ forecast error: A robust prediction model and its short term trading profitability

NARCIS (Netherlands)

Boudt, K.M.R.; de Goei, P.; Thewissen, J.; van Campenhout, G.

2015-01-01

This paper contributes to the empirical evidence on the investment horizon salient to trading based on predicting the error in analysts' earnings forecasts. An econometric framework is proposed that accommodates the stylized fact of extreme values in the forecast error series. We find that between

Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error.

Science.gov (United States)

Shipitsin, M; Small, C; Choudhury, S; Giladi, E; Friedlander, S; Nardone, J; Hussain, S; Hurley, A D; Ernst, C; Huang, Y E; Chang, H; Nifong, T P; Rimm, D L; Dunyak, J; Loda, M; Berman, D M; Blume-Jensen, P

2014-09-09

Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation. Prostatectomy samples from a large patient cohort with long follow-up were blindly assessed by expert pathologists who identified the tissue regions with the highest and lowest Gleason grade from each patient. To simulate biopsy-sampling error, a core from a high- and a low-Gleason area from each patient sample was used to generate a 'high' and a 'low' tumour microarray, respectively. Using a quantitative proteomics approach, we identified from 160 candidates 12 biomarkers that predicted prostate cancer aggressiveness (surgical Gleason and TNM stage) and lethal outcome robustly in both high- and low-Gleason areas. Conversely, a previously reported lethal outcome-predictive marker signature for prostatectomy tissue was unable to perform under circumstances of maximal sampling error. Our results have important implications for cancer biomarker discovery in general and development of a sampling error-resistant clinical biopsy test for prediction of prostate cancer aggressiveness.

Filtering and prediction

CERN Document Server

Fristedt, B; Krylov, N

2007-01-01

Filtering and prediction is about observing moving objects when the observations are corrupted by random errors. The main focus is then on filtering out the errors and extracting from the observations the most precise information about the object, which itself may or may not be moving in a somewhat random fashion. Next comes the prediction step where, using information about the past behavior of the object, one tries to predict its future path. The first three chapters of the book deal with discrete probability spaces, random variables, conditioning, Markov chains, and filtering of discrete Markov chains. The next three chapters deal with the more sophisticated notions of conditioning in nondiscrete situations, filtering of continuous-space Markov chains, and of Wiener process. Filtering and prediction of stationary sequences is discussed in the last two chapters. The authors believe that they have succeeded in presenting necessary ideas in an elementary manner without sacrificing the rigor too much. Such rig...

Predicting treatment effect from surrogate endpoints and historical trials: an extrapolation involving probabilities of a binary outcome or survival to a specific time.

Science.gov (United States)

Baker, Stuart G; Sargent, Daniel J; Buyse, Marc; Burzykowski, Tomasz

2012-03-01

Using multiple historical trials with surrogate and true endpoints, we consider various models to predict the effect of treatment on a true endpoint in a target trial in which only a surrogate endpoint is observed. This predicted result is computed using (1) a prediction model (mixture, linear, or principal stratification) estimated from historical trials and the surrogate endpoint of the target trial and (2) a random extrapolation error estimated from successively leaving out each trial among the historical trials. The method applies to either binary outcomes or survival to a particular time that is computed from censored survival data. We compute a 95% confidence interval for the predicted result and validate its coverage using simulation. To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download. © 2011, The International Biometric Society No claim to original US government works.

Predicting positional error of MLC using volumetric analysis

International Nuclear Information System (INIS)

Hareram, E.S.

2008-01-01

IMRT normally using multiple beamlets (small width of the beam) for a particular field to deliver so that it is imperative to maintain the positional accuracy of the MLC in order to deliver integrated computed dose accurately. Different manufacturers have reported high precession on MLC devices with leaf positional accuracy nearing 0.1 mm but measuring and rectifying the error in this accuracy is very difficult. Various methods are used to check MLC position and among this volumetric analysis is one of the technique. Volumetric approach was adapted in our method using primus machine and 0.6cc chamber at 5 cm depth In perspex. MLC of 1 mm error introduces an error of 20%, more sensitive to other methods

A Conceptual Framework for Predicting Error in Complex Human-Machine Environments

Science.gov (United States)

Freed, Michael; Remington, Roger; Null, Cynthia H. (Technical Monitor)

1998-01-01

We present a Goals, Operators, Methods, and Selection Rules-Model Human Processor (GOMS-MHP) style model-based approach to the problem of predicting human habit capture errors. Habit captures occur when the model fails to allocate limited cognitive resources to retrieve task-relevant information from memory. Lacking the unretrieved information, decision mechanisms act in accordance with implicit default assumptions, resulting in error when relied upon assumptions prove incorrect. The model helps interface designers identify situations in which such failures are especially likely.

[Precautionary maternity leave in Tirol].

Science.gov (United States)

Ludescher, K; Baumgartner, E; Roner, A; Brezinka, C

1998-01-01

Under Austrian law, precautionary maternity leave is a decree issued by the district public health physician. It forbids a pregnant woman to work and mandates immediate maternity leave. Regular maternity leave for all women employed in all jobs begins at 32 weeks of gestation. Women who work in workplaces deemed dangerous and women with a history of obstetric problems such as premature or growth-retarded babies from previous pregnancies are regularly 'sent' into precautionary maternity leave. The public health physicians of Tirol's nine administrative districts were interviewed and supplied data on precautionary maternity leave from their districts. In 100 women who attended the clinic for pregnancies at risk of the Obstetrics/Gynecology Department of Innsbruck University Hospital and who had already obtained precautionary maternity leave, the medical/administrative procedure was studied in each case and correlated with pregnancy outcome. The town district of Innsbruck and the district that comprises the suburbs of the provincial capital had the highest rates of precautionary maternity leave. The town district of Innsbruck had a rate of 24.3% of all pregnant women (employed and not employed) in precautionary maternity leave in 1997, whereas the whole province of Tirol had 13.4%. More than 80% of decrees for precautionary maternity leave are issued by district public health physicians on the basis of written recommendations from gynecologists. One third of women who are sent into precautionary maternity leave are issued the decree prior to 12 weeks of gestation - mostly cases of multiple pregnancies and women with previous miscarriages. The present system of precautionary maternity leave appears to work in the sense that most working pregnant women with risk factors are correctly identified - with most errors on the side of caution. As the system also helps employers - the employee's pay is paid from the federal family support fund and state insurance once she is in

Different populations of subthalamic neurons encode cocaine vs. sucrose reward and predict future error.

Science.gov (United States)

Lardeux, Sylvie; Paleressompoulle, Dany; Pernaud, Remy; Cador, Martine; Baunez, Christelle

2013-10-01

The search for treatment of cocaine addiction raises the challenge to find a way to diminish motivation for the drug without decreasing it for natural rewards. Subthalamic nucleus (STN) inactivation decreases motivation for cocaine while increasing motivation for food, suggesting that STN can dissociate different rewards. Here, we investigated how rat STN neurons respond to cues predicting cocaine or sucrose and to reward delivery while rats are performing a discriminative stimuli task. We show that different neuronal populations of STN neurons encode cocaine and sucrose. In addition, we show that STN activity at the cue onset predicts future error. When changing the reward predicted unexpectedly, STN neurons show capacities of adaptation, suggesting a role in reward-prediction error. Furthermore, some STN neurons show a response to executive error (i.e., "oops neurons") that is specific to the missed reward. These results position the STN as a nexus where natural rewards and drugs of abuse are coded differentially and can influence the performance. Therefore, STN can be viewed as a structure where action could be taken for the treatment of cocaine addiction.

Predicting areas of sustainable error growth in quasigeostrophic flows using perturbation alignment properties

Science.gov (United States)

Rivière, G.; Hua, B. L.

2004-10-01

A new perturbation initialization method is used to quantify error growth due to inaccuracies of the forecast model initial conditions in a quasigeostrophic box ocean model describing a wind-driven double gyre circulation. This method is based on recent analytical results on Lagrangian alignment dynamics of the perturbation velocity vector in quasigeostrophic flows. More specifically, it consists in initializing a unique perturbation from the sole knowledge of the control flow properties at the initial time of the forecast and whose velocity vector orientation satisfies a Lagrangian equilibrium criterion. This Alignment-based Initialization method is hereafter denoted as the AI method.In terms of spatial distribution of the errors, we have compared favorably the AI error forecast with the mean error obtained with a Monte-Carlo ensemble prediction. It is shown that the AI forecast is on average as efficient as the error forecast initialized with the leading singular vector for the palenstrophy norm, and significantly more efficient than that for total energy and enstrophy norms. Furthermore, a more precise examination shows that the AI forecast is systematically relevant for all control flows whereas the palenstrophy singular vector forecast leads sometimes to very good scores and sometimes to very bad ones.A principal component analysis at the final time of the forecast shows that the AI mode spatial structure is comparable to that of the first eigenvector of the error covariance matrix for a "bred mode" ensemble. Furthermore, the kinetic energy of the AI mode grows at the same constant rate as that of the "bred modes" from the initial time to the final time of the forecast and is therefore characterized by a sustained phase of error growth. In this sense, the AI mode based on Lagrangian dynamics of the perturbation velocity orientation provides a rationale of the "bred mode" behavior.

Correction for Measurement Error from Genotyping-by-Sequencing in Genomic Variance and Genomic Prediction Models

DEFF Research Database (Denmark)

Ashraf, Bilal; Janss, Luc; Jensen, Just

sample). The GBSeq data can be used directly in genomic models in the form of individual SNP allele-frequency estimates (e.g., reference reads/total reads per polymorphic site per individual), but is subject to measurement error due to the low sequencing depth per individual. Due to technical reasons....... In the current work we show how the correction for measurement error in GBSeq can also be applied in whole genome genomic variance and genomic prediction models. Bayesian whole-genome random regression models are proposed to allow implementation of large-scale SNP-based models with a per-SNP correction...... for measurement error. We show correct retrieval of genomic explained variance, and improved genomic prediction when accounting for the measurement error in GBSeq data...

Identifying afterloading PDR and HDR brachytherapy errors using real-time fiber-coupled Al2O3:C dosimetry and a novel statistical error decision criterion

International Nuclear Information System (INIS)

Kertzscher, Gustavo; Andersen, Claus E.; Siebert, Frank-Andre; Nielsen, Soren Kynde; Lindegaard, Jacob C.; Tanderup, Kari

2011-01-01

Background and purpose: The feasibility of a real-time in vivo dosimeter to detect errors has previously been demonstrated. The purpose of this study was to: (1) quantify the sensitivity of the dosimeter to detect imposed treatment errors under well controlled and clinically relevant experimental conditions, and (2) test a new statistical error decision concept based on full uncertainty analysis. Materials and methods: Phantom studies of two gynecological cancer PDR and one prostate cancer HDR patient treatment plans were performed using tandem ring applicators or interstitial needles. Imposed treatment errors, including interchanged pairs of afterloader guide tubes and 2-20 mm source displacements, were monitored using a real-time fiber-coupled carbon doped aluminum oxide (Al 2 O 3 :C) crystal dosimeter that was positioned in the reconstructed tumor region. The error detection capacity was evaluated at three dose levels: dwell position, source channel, and fraction. The error criterion incorporated the correlated source position uncertainties and other sources of uncertainty, and it was applied both for the specific phantom patient plans and for a general case (source-detector distance 5-90 mm and position uncertainty 1-4 mm). Results: Out of 20 interchanged guide tube errors, time-resolved analysis identified 17 while fraction level analysis identified two. Channel and fraction level comparisons could leave 10 mm dosimeter displacement errors unidentified. Dwell position dose rate comparisons correctly identified displacements ≥5 mm. Conclusion: This phantom study demonstrates that Al 2 O 3 :C real-time dosimetry can identify applicator displacements ≥5 mm and interchanged guide tube errors during PDR and HDR brachytherapy. The study demonstrates the shortcoming of a constant error criterion and the advantage of a statistical error criterion.

Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves

Directory of Open Access Journals (Sweden)

M.S. Borhan

2017-12-01

Full Text Available Facilitating non-contact measurement, a computer-imaging system was devised and evaluated to predict the chlorophyll content in potato leaves. A charge-coupled device (CCD camera paired with two optical filters and light chamber was used to acquire green (550 ± 40 nm and red band (700 ± 40 nm images from the same leaf. Potato leaves from 15 plants differing in coloration (green to yellow and age were selected for this study. Histogram based image features, such as mean and variances of green and red band images, were extracted from the histogram. Regression analyses demonstrated that the variations in SPAD meter reading could be explained by the mean gray and variances of gray scale values. The fitted least square models based on the mean gray scale levels were inversely related to the chlorophyll content of the potato leaf with a R2 of 0.87 using a green band image and with an R2 of 0.79 using a red band image. With the extracted four image features, the developed multiple linear regression model predicted the chlorophyll content with a high R2 of 0.88. The multiple regression model (using all features provided an average prediction accuracy of 85.08% and a maximum accuracy of 99.8%. The prediction model using only mean gray value of red band showed an average accuracy of 81.6% with a maximum accuracy of 99.14%. Keywords: Computer imaging, Chlorophyll, SPAD meter, Regression, Prediction accuracy

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

Science.gov (United States)

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families. PMID:27783639

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

Directory of Open Access Journals (Sweden)

Nanna Hellum Nielsen

Full Text Available Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

Patient identification error among prostate needle core biopsy specimens--are we ready for a DNA time-out?

Science.gov (United States)

Suba, Eric J; Pfeifer, John D; Raab, Stephen S

2007-10-01

Patient identification errors in surgical pathology often involve switches of prostate or breast needle core biopsy specimens among patients. We assessed strategies for decreasing the occurrence of these uncommon and yet potentially catastrophic events. Root cause analyses were performed following 3 cases of patient identification error involving prostate needle core biopsy specimens. Patient identification errors in surgical pathology result from slips and lapses of automatic human action that may occur at numerous steps during pre-laboratory, laboratory and post-laboratory work flow processes. Patient identification errors among prostate needle biopsies may be difficult to entirely prevent through the optimization of work flow processes. A DNA time-out, whereby DNA polymorphic microsatellite analysis is used to confirm patient identification before radiation therapy or radical surgery, may eliminate patient identification errors among needle biopsies.

Predictive QSAR Models for the Toxicity of Disinfection Byproducts

Directory of Open Access Journals (Sweden)

Litang Qin

2017-10-01

Full Text Available Several hundred disinfection byproducts (DBPs in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2 > 0.7, explained variance in leave-one-out prediction (Q2LOO and in leave-many-out prediction (Q2LMO > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3 > 0.7, and concordance correlation coefficient (CCC > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

Predictive QSAR Models for the Toxicity of Disinfection Byproducts.

Science.gov (United States)

Qin, Litang; Zhang, Xin; Chen, Yuhan; Mo, Lingyun; Zeng, Honghu; Liang, Yanpeng

2017-10-09

Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination ( R ²) > 0.7, explained variance in leave-one-out prediction ( Q ² LOO ) and in leave-many-out prediction ( Q ² LMO ) > 0.6, variance explained in external prediction ( Q ² F1 , Q ² F2 , and Q ² F3 ) > 0.7, and concordance correlation coefficient ( CCC ) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

Hawthorn (Crataegus pinnatifida Bunge) leave flavonoids attenuate atherosclerosis development in apoE knock-out mice.

Science.gov (United States)

Dong, Pengzhi; Pan, Lanlan; Zhang, Xiting; Zhang, Wenwen; Wang, Xue; Jiang, Meixiu; Chen, Yuanli; Duan, Yajun; Wu, Honghua; Xu, Yantong; Zhang, Peng; Zhu, Yan

2017-02-23

Hawthorn (Crataegus pinnatifida Bunge) leave have been used to treat cardiovascular diseases in China and Europe. Hawthorn leave flavonoids (HLF) are the main part of extraction. Whether hawthorn leave flavonoids could attenuate the development of atherosclerosis and the possible mechanism remain unknown. High-fat diet (HFD) mixed with HLF at concentrations of 5mg/kg and 20mg/kg were administered to apolipoprotein E (apoE) knock out mice. 16 weeks later, mouse serum was collected to determine the lipid profile while the mouse aorta dissected was prepared to measure the lesion area. Hepatic mRNA of genes involved in lipid metabolism were determined. Peritoneal macrophages were collected to study the impact of HLF on cholesterol efflux, formation of foam cell and the expression of ATP binding cassette transporter A1 (ABCA1). Besides, in vivo reverse cholesterol transport (RCT) was conducted. HLF attenuated the development of atherosclerosis that the mean atherosclerotic lesion area in en face aortas was reduced by 23.1% (Pflavonoids can slow down the development of atherosclerosis in apoE knockout mice via induced expression of genes involved in antioxidant activities, inhibition of the foam cell formation and promotion of RCT in vivo, which implies the potential use in the prevention of atherosclerosis. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Rotational error in path integration: encoding and execution errors in angle reproduction.

Science.gov (United States)

Chrastil, Elizabeth R; Warren, William H

2017-06-01

Path integration is fundamental to human navigation. When a navigator leaves home on a complex outbound path, they are able to keep track of their approximate position and orientation and return to their starting location on a direct homebound path. However, there are several sources of error during path integration. Previous research has focused almost exclusively on encoding error-the error in registering the outbound path in memory. Here, we also consider execution error-the error in the response, such as turning and walking a homebound trajectory. In two experiments conducted in ambulatory virtual environments, we examined the contribution of execution error to the rotational component of path integration using angle reproduction tasks. In the reproduction tasks, participants rotated once and then rotated again to face the original direction, either reproducing the initial turn or turning through the supplementary angle. One outstanding difficulty in disentangling encoding and execution error during a typical angle reproduction task is that as the encoding angle increases, so does the required response angle. In Experiment 1, we dissociated these two variables by asking participants to report each encoding angle using two different responses: by turning to walk on a path parallel to the initial facing direction in the same (reproduction) or opposite (supplementary angle) direction. In Experiment 2, participants reported the encoding angle by turning both rightward and leftward onto a path parallel to the initial facing direction, over a larger range of angles. The results suggest that execution error, not encoding error, is the predominant source of error in angular path integration. These findings also imply that the path integrator uses an intrinsic (action-scaled) rather than an extrinsic (objective) metric.

In/Out Status Monitoring in Mobile Asset Tracking with Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Kwangsoo Kim

2010-03-01

Full Text Available A mobile asset with a sensor node in a mobile asset tracking system moves around a monitoring area, leaves it, and then returns to the region repeatedly. The system monitors the in/out status of the mobile asset. Due to the continuous movement of the mobile asset, the system may generate an error for the in/out status of the mobile asset. When the mobile asset is inside the region, the system might determine that it is outside, or vice versa. In this paper, we propose a method to detect and correct the incorrect in/out status of the mobile asset. To solve this problem, our approach uses data about the connection state transition and the battery lifetime of the mobile node attached to the mobile asset. The connection state transition is used to classify the mobile node as normal or abnormal. The battery lifetime is used to predict a valid working period for the mobile node. We evaluate our method using real data generated by a medical asset tracking system. The experimental results show that our method, by using the estimated battery life time or by using the invalid connection state, can detect and correct most cases of incorrect in/out statuses generated by the conventional approach.

In/out status monitoring in mobile asset tracking with wireless sensor networks.

Science.gov (United States)

Kim, Kwangsoo; Chung, Chin-Wan

2010-01-01

A mobile asset with a sensor node in a mobile asset tracking system moves around a monitoring area, leaves it, and then returns to the region repeatedly. The system monitors the in/out status of the mobile asset. Due to the continuous movement of the mobile asset, the system may generate an error for the in/out status of the mobile asset. When the mobile asset is inside the region, the system might determine that it is outside, or vice versa. In this paper, we propose a method to detect and correct the incorrect in/out status of the mobile asset. To solve this problem, our approach uses data about the connection state transition and the battery lifetime of the mobile node attached to the mobile asset. The connection state transition is used to classify the mobile node as normal or abnormal. The battery lifetime is used to predict a valid working period for the mobile node. We evaluate our method using real data generated by a medical asset tracking system. The experimental results show that our method, by using the estimated battery life time or by using the invalid connection state, can detect and correct most cases of incorrect in/out statuses generated by the conventional approach.

Tax revenue and inflation rate predictions in Banda Aceh using Vector Error Correction Model (VECM)

Science.gov (United States)

Maulia, Eva; Miftahuddin; Sofyan, Hizir

2018-05-01

A country has some important parameters to achieve the welfare of the economy, such as tax revenues and inflation. One of the largest revenues of the state budget in Indonesia comes from the tax sector. Besides, the rate of inflation occurring in a country can be used as one measure, to measure economic problems that the country facing. Given the importance of tax revenue and inflation rate control in achieving economic prosperity, it is necessary to analyze the relationship and forecasting tax revenue and inflation rate. VECM (Vector Error Correction Model) was chosen as the method used in this research, because of the data used in the form of multivariate time series data. This study aims to produce a VECM model with optimal lag and to predict the tax revenue and inflation rate of the VECM model. The results show that the best model for data of tax revenue and the inflation rate in Banda Aceh City is VECM with 3rd optimal lag or VECM (3). Of the seven models formed, there is a significant model that is the acceptance model of income tax. The predicted results of tax revenue and the inflation rate in Kota Banda Aceh for the next 6, 12 and 24 periods (months) obtained using VECM (3) are considered valid, since they have a minimum error value compared to other models.

Pupil dilation indicates the coding of past prediction errors: Evidence for attentional learning theory.

Science.gov (United States)

Koenig, Stephan; Uengoer, Metin; Lachnit, Harald

2018-04-01

The attentional learning theory of Pearce and Hall () predicts more attention to uncertain cues that have caused a high prediction error in the past. We examined how the cue-elicited pupil dilation during associative learning was linked to such error-driven attentional processes. In three experiments, participants were trained to acquire associations between different cues and their appetitive (Experiment 1), motor (Experiment 2), or aversive (Experiment 3) outcomes. All experiments were designed to examine differences in the processing of continuously reinforced cues (consistently followed by the outcome) versus partially reinforced, uncertain cues (randomly followed by the outcome). We measured the pupil dilation elicited by the cues in anticipation of the outcome and analyzed how this conditioned pupil response changed over the course of learning. In all experiments, changes in pupil size complied with the same basic pattern: During early learning, consistently reinforced cues elicited greater pupil dilation than uncertain, randomly reinforced cues, but this effect gradually reversed to yield a greater pupil dilation for uncertain cues toward the end of learning. The pattern of data accords with the changes in prediction error and error-driven attention formalized by the Pearce-Hall theory. © 2017 The Authors. Psychophysiology published by Wiley Periodicals, Inc. on behalf of Society for Psychophysiological Research.

Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation

International Nuclear Information System (INIS)

Gershgorin, B.; Harlim, J.; Majda, A.J.

2010-01-01

The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates

A new method for class prediction based on signed-rank algorithms applied to Affymetrix® microarray experiments

Directory of Open Access Journals (Sweden)

Vassal Aurélien

2008-01-01

Full Text Available Abstract Background The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix® technology provides both a quantitative fluorescence signal and a decision (detection call: absent or present based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM patients. Results After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM. Conclusion This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with

Signed reward prediction errors drive declarative learning.

Directory of Open Access Journals (Sweden)

Esther De Loof

Full Text Available Reward prediction errors (RPEs are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning. However, empirical evidence on whether RPEs drive declarative learning-a quintessentially human form of learning-remains surprisingly absent. We therefore coupled RPEs to the acquisition of Dutch-Swahili word pairs in a declarative learning paradigm. Signed RPEs (SRPEs; "better-than-expected" signals during declarative learning improved recognition in a follow-up test, with increasingly positive RPEs leading to better recognition. In addition, classic declarative memory mechanisms such as time-on-task failed to explain recognition performance. The beneficial effect of SRPEs on recognition was subsequently affirmed in a replication study with visual stimuli.

Signed reward prediction errors drive declarative learning.

Science.gov (United States)

De Loof, Esther; Ergo, Kate; Naert, Lien; Janssens, Clio; Talsma, Durk; Van Opstal, Filip; Verguts, Tom

2018-01-01

Reward prediction errors (RPEs) are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning). However, empirical evidence on whether RPEs drive declarative learning-a quintessentially human form of learning-remains surprisingly absent. We therefore coupled RPEs to the acquisition of Dutch-Swahili word pairs in a declarative learning paradigm. Signed RPEs (SRPEs; "better-than-expected" signals) during declarative learning improved recognition in a follow-up test, with increasingly positive RPEs leading to better recognition. In addition, classic declarative memory mechanisms such as time-on-task failed to explain recognition performance. The beneficial effect of SRPEs on recognition was subsequently affirmed in a replication study with visual stimuli.

Improved techniques for predicting spacecraft power

International Nuclear Information System (INIS)

Chmielewski, A.B.

1987-01-01

Radioisotope Thermoelectric Generators (RTGs) are going to supply power for the NASA Galileo and Ulysses spacecraft now scheduled to be launched in 1989 and 1990. The duration of the Galileo mission is expected to be over 8 years. This brings the total RTG lifetime to 13 years. In 13 years, the RTG power drops more than 20 percent leaving a very small power margin over what is consumed by the spacecraft. Thus it is very important to accurately predict the RTG performance and be able to assess the magnitude of errors involved. The paper lists all the error sources involved in the RTG power predictions and describes a statistical method for calculating the tolerance

Modeling and Predicting the Electrical Conductivity of Composite Cathode for Solid Oxide Fuel Cell by Using Support Vector Regression

Science.gov (United States)

Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J.

2012-07-01

The electrical conductivity of solid oxide fuel cell (SOFC) cathode is one of the most important indices affecting the efficiency of SOFC. In order to improve the performance of fuel cell system, it is advantageous to have accurate model with which one can predict the electrical conductivity. In this paper, a model utilizing support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter optimization was established to modeling and predicting the electrical conductivity of Ba0.5Sr0.5Co0.8Fe0.2 O3-δ-xSm0.5Sr0.5CoO3-δ (BSCF-xSSC) composite cathode under two influence factors, including operating temperature (T) and SSC content (x) in BSCF-xSSC composite cathode. The leave-one-out cross validation (LOOCV) test result by SVR strongly supports that the generalization ability of SVR model is high enough. The absolute percentage error (APE) of 27 samples does not exceed 0.05%. The mean absolute percentage error (MAPE) of all 30 samples is only 0.09% and the correlation coefficient (R2) as high as 0.999. This investigation suggests that the hybrid PSO-SVR approach may be not only a promising and practical methodology to simulate the properties of fuel cell system, but also a powerful tool to be used for optimal designing or controlling the operating process of a SOFC system.

[Prediction of SPAD value in oilseed rape leaves using hyperspectral imaging technique].

Science.gov (United States)

Ding, Xi-bin; Liu, Fei; Zhang, Chu; He, Yong

2015-02-01

In the present work, prediction models of SPAD value (Soil and Plant Analyzer Development, often used as a parameter to indicate chlorophyll content) in oilseed rape leaves were successfully built using hyperspectral imaging technique. The hy perspectral images of 160 oilseed rape leaf samples in the spectral range of 380-1030 nm were acquired. Average spectrum was extracted from the region of interest (ROI) of each sample. We chose spectral data in the spectral range of 500-900 nm for analysis. Using Monte Carlo partial least squares(MC-PLS) algorithm, 13 samples were identified as outliers and eliminated. Based on the spectral information and measured SPAD values of the rest 147 samples, several estimation models have been built based on different parameters using different algorithms for comparison, including: (1) a SPAD value estimation model based on partial least squares(PLS) in the whole wavelength region of 500-900 nm; (2) a SPAD value estimation model based on successive projections algorithmcombined with PLS(SPA-PLS); (3) 4 kind of simple experience SPAD value estimation models in which red edge position was used as an argument; (4) 4 kind of simple experience SPAD value estimation models in which three vegetation indexes R710/R760, (R750-R705)/(R750-R705) and R860/(R550 x R708), which all have been proved to have a good relevance with chlorophyll content, were used as an argument respectively; (5) a SPAD value estimation model based on PLS using the 3 vegetation indexes mentioned above. The results indicate that the optimal prediction performance is achieved by PLS model in the whole wavelength region of 500-900 nm, which has a correlation coefficient(r(p)) of 0.8339 and a root mean squares error of predicted (RMSEP) of 1.52. The SPA-PLS model can provide avery close prediction result while the calibration computation has been significantly reduced and the calibration speed has been accelerated sharply. For simple experience models based on red edge

Basic human error probabilities in advanced MCRs when using soft control

International Nuclear Information System (INIS)

Jang, In Seok; Seong, Poong Hyun; Kang, Hyun Gook; Lee, Seung Jun

2012-01-01

In a report on one of the renowned HRA methods, Technique for Human Error Rate Prediction (THERP), it is pointed out that 'The paucity of actual data on human performance continues to be a major problem for estimating HEPs and performance times in nuclear power plant (NPP) task'. However, another critical difficulty is that most current HRA databases deal with operation in conventional type of MCRs. With the adoption of new human system interfaces that are based on computer based technologies, the operation environment of MCRs in NPPs has changed. The MCRs including these digital and computer technologies, such as large display panels, computerized procedures, soft controls, and so on, are called advanced MCRs. Because of the different interfaces, different Basic Human Error Probabilities (BHEPs) should be considered in human reliability analyses (HRAs) for advanced MCRs. This study carries out an empirical analysis of human error considering soft controls. The aim of this work is not only to compile a database using the simulator for advanced MCRs but also to compare BHEPs with those of a conventional MCR database

Modeling coherent errors in quantum error correction

Science.gov (United States)

Greenbaum, Daniel; Dutton, Zachary

2018-01-01

Analysis of quantum error correcting codes is typically done using a stochastic, Pauli channel error model for describing the noise on physical qubits. However, it was recently found that coherent errors (systematic rotations) on physical data qubits result in both physical and logical error rates that differ significantly from those predicted by a Pauli model. Here we examine the accuracy of the Pauli approximation for noise containing coherent errors (characterized by a rotation angle ɛ) under the repetition code. We derive an analytic expression for the logical error channel as a function of arbitrary code distance d and concatenation level n, in the small error limit. We find that coherent physical errors result in logical errors that are partially coherent and therefore non-Pauli. However, the coherent part of the logical error is negligible at fewer than {ε }-({dn-1)} error correction cycles when the decoder is optimized for independent Pauli errors, thus providing a regime of validity for the Pauli approximation. Above this number of correction cycles, the persistent coherent logical error will cause logical failure more quickly than the Pauli model would predict, and this may need to be combated with coherent suppression methods at the physical level or larger codes.

Modeling Anti-HIV Activity of HEPT Derivatives Revisited. Multiregression Models Are Not Inferior Ones

International Nuclear Information System (INIS)

Basic, Ivan; Nadramija, Damir; Flajslik, Mario; Amic, Dragan; Lucic, Bono

2007-01-01

Several quantitative structure-activity studies for this data set containing 107 HEPT derivatives have been performed since 1997, using the same set of molecules by (more or less) different classes of molecular descriptors. Multivariate Regression (MR) and Artificial Neural Network (ANN) models were developed and in each study the authors concluded that ANN models are superior to MR ones. We re-calculated multivariate regression models for this set of molecules using the same set of descriptors, and compared our results with the previous ones. Two main reasons for overestimation of the quality of the ANN models in previous studies comparing with MR models are: (1) wrong calculation of leave-one-out (LOO) cross-validated (CV) correlation coefficient for MR models in Luco et al., J. Chem. Inf. Comput. Sci. 37 392-401 (1997), and (2) incorrect estimation/interpretation of leave-one-out (LOO) cross-validated and predictive performance and power of ANN models. More precise and fairer comparison of fit and LOO CV statistical parameters shows that MR models are more stable. In addition, MR models are much simpler than ANN ones. For real testing the predictive performance of both classes of models we need more HEPT derivatives, because all ANN models that presented results for external set of molecules used experimental values in optimization of modeling procedure and model parameters

Effect of NN correlations on predictions of nuclear transparencies for protons, knocked out in high Q2 (e,e'p) reactions

International Nuclear Information System (INIS)

Rinat, A.S.; Taragin, M.F.

1996-01-01

We study the transparency T of nuclei for nucleons knocked out in high-energy semi-inclusive (e,e'p) reactions, using an improved theoretical input, discussed by Nikolaev et al. We establish that neglect of NN correlations between the knocked-out and core nucleons reduces nuclear transparencies by ∼15 % for light, to ∼10% for heavy nuclei. About the same is predicted for transparencies, integrated over the transverse or longitudinal momentum of the outgoing proton. Hadron dynamics predicts a roughly constant T beyond Q 2 ∼2 GeV 2 , whereas for all targets the largest measured data point Q 2 =6.7 GeV 2 appears to lie above that plateau. Large error bars on those data points preclude a conclusion regarding the onset of colour transparency. (orig.)

Study on the methodology for predicting and preventing errors to improve reliability of maintenance task in nuclear power plant

International Nuclear Information System (INIS)

Hanafusa, Hidemitsu; Iwaki, Toshio; Embrey, D.

2000-01-01

The objective of this study was to develop and effective methodology for predicting and preventing errors in nuclear power plant maintenance tasks. A method was established by which chief maintenance personnel can predict and reduce errors when reviewing the maintenance procedures and while referring to maintenance supporting systems and methods in other industries including aviation and chemical plant industries. The method involves the following seven steps: 1. Identification of maintenance tasks. 2. Specification of important tasks affecting safety. 3. Assessment of human errors occurring during important tasks. 4. Identification of Performance Degrading Factors. 5. Dividing important tasks into sub-tasks. 6. Extraction of errors using Predictive Human Error Analysis (PHEA). 7. Development of strategies for reducing errors and for recovering from errors. By way of a trial, this method was applied to the pump maintenance procedure in nuclear power plants. This method is believed to be capable of identifying the expected errors in important tasks and supporting the development of error reduction measures. By applying this method, the number of accidents resulting form human errors during maintenance can be reduced. Moreover, the maintenance support base using computers was developed. (author)

Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors

International Nuclear Information System (INIS)

Nelms, Benjamin E.; Zhen Heming; Tome, Wolfgang A.

2011-01-01

Purpose: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. Methods: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as ''simulated measurements'' (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. Results: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called ''false negatives.'' The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa. Conclusions: There is a lack of correlation between

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

Directory of Open Access Journals (Sweden)

Boulesteix Anne-Laure

2009-12-01

Full Text Available Abstract Background In biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data, since such analyses are particularly exposed to this kind of bias. Methods In our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure. Results We assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case and the bias resulting from the choice of the classification method are examined both separately and jointly. Conclusions The median minimal error rate over the investigated classifiers was as low as 31% and 41% based on permuted uninformative predictors from studies on colon cancer and prostate cancer, respectively. We conclude that the strategy to present only the optimal result is not acceptable because it yields a substantial bias in error rate estimation, and suggest alternative approaches for properly reporting classification accuracy.

Sick Leave and Factors Influencing Sick Leave in Adult Patients with Atopic Dermatitis: A Cross-Sectional Study.

Science.gov (United States)

van Os-Medendorp, Harmieke; Appelman-Noordermeer, Simone; Bruijnzeel-Koomen, Carla; de Bruin-Weller, Marjolein

2015-03-27

Little is known about the prevalence of sick leave due to atopic dermatitis (AD). The current literature on factors influencing sick leave is mostly derived from other chronic inflammatory diseases. This study aimed to determine the prevalence of sick leave due to AD and to identify influencing factors. A cross-sectional study was carried out in adult patients with AD. sick leave during the two-week and one-year periods, socio-demographic characteristics, disease severity, quality of life and socio-occupational factors. Logistic regression analyses were used to determine influencing factors on sick leave over the two-week period. In total, 253 patients were included; 12% of the patients had to take sick leave in the last two weeks due to AD and 42% in the past year. A higher level of symptom interference (OR 1.26; 95% CI 1.13-1.40) or perfectionism/diligence (OR 0.90; 95% CI 0.83-0.96) may respectively increase or decrease the number of sick leave days. Sick leave in patients with AD is a common problem and symptom interference and perfectionism/diligence appeared to influence it. Novel approaches are needed to deal with symptoms at work or school to reduce the amount of sick leave due to AD.

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

Science.gov (United States)

Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

2012-01-31

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

Predicting students drop out : a case study

NARCIS (Netherlands)

Dekker, G.W.; Pechenizkiy, M.; Vleeshouwers, J.M.; Barnes, T.; Desmarais, M.; Romero, C.; Ventura, S.

2009-01-01

The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors

Science.gov (United States)

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Lerat, Julien; Kuczera, George

2017-03-01

Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. This study focuses on approaches for representing error heteroscedasticity with respect to simulated streamflow, i.e., the pattern of larger errors in higher streamflow predictions. We evaluate eight common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter λ) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and the United States, and two lumped hydrological models. Performance is quantified using predictive reliability, precision, and volumetric bias metrics. We find the choice of heteroscedastic error modeling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with λ of 0.2 and 0.5, and the log scheme (λ = 0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Paradoxically, calibration of λ is often counterproductive: in perennial catchments, it tends to overfit low flows at the expense of abysmal precision in high flows. The log-sinh transformation is dominated by the simpler Pareto optimal schemes listed above. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors

Science.gov (United States)

David, McInerney; Mark, Thyer; Dmitri, Kavetski; George, Kuczera

2017-04-01

This study provides guidance to hydrological researchers which enables them to provide probabilistic predictions of daily streamflow with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality). Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. It is commonly known that hydrological model residual errors are heteroscedastic, i.e. there is a pattern of larger errors in higher streamflow predictions. Although multiple approaches exist for representing this heteroscedasticity, few studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating 8 common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter, lambda) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and USA, and two lumped hydrological models. We find the choice of heteroscedastic error modelling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with lambda of 0.2 and 0.5, and the log scheme (lambda=0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Comparative morphology on leaves of Daphniphyllum (Daphniphyllaceae)

NARCIS (Netherlands)

Tang, M.-S.; Yang, Y.-P.; Sheue, C.-R.

2009-01-01

A comparative anatomical study on the leaves of nine out of 29 species of the genus Daphniphyllum was performed to seek support for the present infrageneric classification. Daphniphyllum is composed of two sections, Lunata (with one subsection Lunata) and Daphniphyllum (with two subsections,

Social learning through prediction error in the brain

Science.gov (United States)

Joiner, Jessica; Piva, Matthew; Turrin, Courtney; Chang, Steve W. C.

2017-06-01

Learning about the world is critical to survival and success. In social animals, learning about others is a necessary component of navigating the social world, ultimately contributing to increasing evolutionary fitness. How humans and nonhuman animals represent the internal states and experiences of others has long been a subject of intense interest in the developmental psychology tradition, and, more recently, in studies of learning and decision making involving self and other. In this review, we explore how psychology conceptualizes the process of representing others, and how neuroscience has uncovered correlates of reinforcement learning signals to explore the neural mechanisms underlying social learning from the perspective of representing reward-related information about self and other. In particular, we discuss self-referenced and other-referenced types of reward prediction errors across multiple brain structures that effectively allow reinforcement learning algorithms to mediate social learning. Prediction-based computational principles in the brain may be strikingly conserved between self-referenced and other-referenced information.

Medical leave granted to psychiatric inpatients--a one-year retrospective review.

Science.gov (United States)

Koh, K G; Ang, A W

2000-09-01

Of the 676 patients warded in 1998 at the National University Hospital (NUH) Department of Psychological Medicine, over a third (n = 268) required certification of absence from work. Duration of inpatient stay and immediate post-discharge medical leave were examined for this group. These durations were correlated against the patients' diagnoses and their demographic variables. The mental health morbidity of teachers was specifically studied. In this retrospective study, we used medical certificate counterfoils to determine the lengths of admission and post-discharge medical leave duration. ANOVA and Kruskal-Wallis tests of the SPSS computer package were used for statistical analysis. The sex and marital status of these patients did not affect either duration significantly. However, those 45 years and older were granted longer outpatient medical leave. Patients diagnosed with mood and psychotic disorders required longer inpatient stay and were granted longer outpatient medical leave, as compared with other diagnostic groups. It was found that the teachers admitted were largely 45 years and older, had a diagnosis of depression and required extended periods of outpatient medical leave compared to other occupational groups. The mean number of days of inpatient stay and outpatient medical leave may serve as a helpful guideline of current practice. As introduced in this paper, the use of medical certificate counterfoils is a simple yet effective way of measuring days off-work. With the inclusion of those psychiatric patients not working and the medical leave granted long after discharge, calculations of the economic costs of specific mental disorders to Singapore can then be attempted.

Validation of sick leave measures: self-reported sick leave and sickness benefit data from a Danish national register compared to multiple workplace-registered sick leave spells in a Danish municipality.

Science.gov (United States)

Stapelfeldt, Christina Malmose; Jensen, Chris; Andersen, Niels Trolle; Fleten, Nils; Nielsen, Claus Vinther

2012-08-15

Previous validation studies of sick leave measures have focused on self-reports. Register-based sick leave data are considered to be valid; however methodological problems may be associated with such data. A Danish national register on sickness benefit (DREAM) has been widely used in sick leave research. On the basis of sick leave records from 3,554 and 2,311 eldercare workers in 14 different workplaces, the aim of this study was to: 1) validate registered sickness benefit data from DREAM against workplace-registered sick leave spells of at least 15 days; 2) validate self-reported sick leave days during one year against workplace-registered sick leave. Agreement between workplace-registered sick leave and DREAM-registered sickness benefit was reported as sensitivities, specificities and positive predictive values. A receiver-operating characteristic curve and a Bland-Altman plot were used to study the concordance with sick leave duration of the first spell. By means of an analysis of agreement between self-reported and workplace-registered sick leave sensitivity and specificity was calculated. Ninety-five percent confidence intervals (95% CI) were used. The probability that registered DREAM data on sickness benefit agrees with workplace-registered sick leave of at least 15 days was 96.7% (95% CI: 95.6-97.6). Specificity was close to 100% (95% CI: 98.3-100). The registered DREAM data on sickness benefit overestimated the duration of sick leave spells by an average of 1.4 (SD: 3.9) weeks. Separate analysis on pregnancy-related sick leave revealed a maximum sensitivity of 20% (95% CI: 4.3-48.1).The sensitivity of self-reporting at least one or at least 56 sick leave day/s was 94.5 (95% CI: 93.4 - 95.5) % and 58.5 (95% CI: 51.1 - 65.6) % respectively. The corresponding specificities were 85.3 (95% CI: 81.4 - 88.6) % and 98.9 (95% CI: 98.3 - 99.3) %. The DREAM register offered valid measures of sick leave spells of at least 15 days among eldercare employees. Pregnancy

Validation of sick leave measures: self-reported sick leave and sickness benefit data from a Danish national register compared to multiple workplace-registered sick leave spells in a Danish municipality

Directory of Open Access Journals (Sweden)

Stapelfeldt Christina Malmose

2012-08-01

Full Text Available Abstract Background Previous validation studies of sick leave measures have focused on self-reports. Register-based sick leave data are considered to be valid; however methodological problems may be associated with such data. A Danish national register on sickness benefit (DREAM has been widely used in sick leave research. On the basis of sick leave records from 3,554 and 2,311 eldercare workers in 14 different workplaces, the aim of this study was to: 1 validate registered sickness benefit data from DREAM against workplace-registered sick leave spells of at least 15 days; 2 validate self-reported sick leave days during one year against workplace-registered sick leave. Methods Agreement between workplace-registered sick leave and DREAM-registered sickness benefit was reported as sensitivities, specificities and positive predictive values. A receiver-operating characteristic curve and a Bland-Altman plot were used to study the concordance with sick leave duration of the first spell. By means of an analysis of agreement between self-reported and workplace-registered sick leave sensitivity and specificity was calculated. Ninety-five percent confidence intervals (95% CI were used. Results The probability that registered DREAM data on sickness benefit agrees with workplace-registered sick leave of at least 15 days was 96.7% (95% CI: 95.6-97.6. Specificity was close to 100% (95% CI: 98.3-100. The registered DREAM data on sickness benefit overestimated the duration of sick leave spells by an average of 1.4 (SD: 3.9 weeks. Separate analysis on pregnancy-related sick leave revealed a maximum sensitivity of 20% (95% CI: 4.3-48.1. The sensitivity of self-reporting at least one or at least 56 sick leave day/s was 94.5 (95% CI: 93.4 – 95.5 % and 58.5 (95% CI: 51.1 – 65.6 % respectively. The corresponding specificities were 85.3 (95% CI: 81.4 – 88.6 % and 98.9 (95% CI: 98.3 – 99.3 %. Conclusions The DREAM register offered valid measures of sick

Mindfulness meditation modulates reward prediction errors in the striatum in a passive conditioning task

Directory of Open Access Journals (Sweden)

Ulrich eKirk

2015-02-01

Full Text Available Reinforcement learning models have demonstrated that phasic activity of dopamine neurons during reward expectation encodes information about the predictability of rewards and cues that predict reward. Evidence indicates that mindfulness-based approaches reduce reward anticipation signal in the striatum to negative and positive incentives suggesting the hypothesis that such training influence basic reward processing. Using a passive conditioning task and fMRI in a group of experienced mindfulness meditators and age-matched controls, we tested the hypothesis that mindfulness meditation influence reward and reward prediction error signals. We found diminished positive and negative prediction error-related blood-oxygen level-dependent (BOLD responses in the putamen in meditators compared with controls. In the meditators, this decrease in striatal BOLD responses to reward prediction was paralleled by increased activity in posterior insula, a primary interoceptive region. Critically, responses in the putamen during early trials of the conditioning procedure (run 1 were elevated in both meditators and controls. These results provide evidence that experienced mindfulness meditators show attenuated reward prediction signals to valenced stimuli, which may be related to interoceptive processes encoded in the posterior insula.

Soil pH Errors Propagation from Measurements to Spatial Predictions - Cost Benefit Analysis and Risk Assessment Implications for Practitioners and Modelers

Science.gov (United States)

Owens, P. R.; Libohova, Z.; Seybold, C. A.; Wills, S. A.; Peaslee, S.; Beaudette, D.; Lindbo, D. L.

2017-12-01

The measurement errors and spatial prediction uncertainties of soil properties in the modeling community are usually assessed against measured values when available. However, of equal importance is the assessment of errors and uncertainty impacts on cost benefit analysis and risk assessments. Soil pH was selected as one of the most commonly measured soil properties used for liming recommendations. The objective of this study was to assess the error size from different sources and their implications with respect to management decisions. Error sources include measurement methods, laboratory sources, pedotransfer functions, database transections, spatial aggregations, etc. Several databases of measured and predicted soil pH were used for this study including the United States National Cooperative Soil Survey Characterization Database (NCSS-SCDB), the US Soil Survey Geographic (SSURGO) Database. The distribution of errors among different sources from measurement methods to spatial aggregation showed a wide range of values. The greatest RMSE of 0.79 pH units was from spatial aggregation (SSURGO vs Kriging), while the measurement methods had the lowest RMSE of 0.06 pH units. Assuming the order of data acquisition based on the transaction distance i.e. from measurement method to spatial aggregation the RMSE increased from 0.06 to 0.8 pH units suggesting an "error propagation". This has major implications for practitioners and modeling community. Most soil liming rate recommendations are based on 0.1 pH unit increments, while the desired soil pH level increments are based on 0.4 to 0.5 pH units. Thus, even when the measured and desired target soil pH are the same most guidelines recommend 1 ton ha-1 lime, which translates in 111 ha-1 that the farmer has to factor in the cost-benefit analysis. However, this analysis need to be based on uncertainty predictions (0.5-1.0 pH units) rather than measurement errors (0.1 pH units) which would translate in 555-1,111 investment that

Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors

Energy Technology Data Exchange (ETDEWEB)

Nelms, Benjamin E.; Zhen Heming; Tome, Wolfgang A. [Canis Lupus LLC and Department of Human Oncology, University of Wisconsin, Merrimac, Wisconsin 53561 (United States); Department of Medical Physics, University of Wisconsin, Madison, Wisconsin 53705 (United States); Departments of Human Oncology, Medical Physics, and Biomedical Engineering, University of Wisconsin, Madison, Wisconsin 53792 (United States)

2011-02-15

Purpose: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. Methods: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as ''simulated measurements'' (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. Results: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called ''false negatives.'' The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa

Sick Leave and Factors Influencing Sick Leave in Adult Patients with Atopic Dermatitis: A Cross-Sectional Study

Directory of Open Access Journals (Sweden)

Harmieke van Os-Medendorp

2015-03-01

Full Text Available Background: Little is known about the prevalence of sick leave due to atopic dermatitis (AD. The current literature on factors influencing sick leave is mostly derived from other chronic inflammatory diseases. This study aimed to determine the prevalence of sick leave due to AD and to identify influencing factors. Methods: A cross-sectional study was carried out in adult patients with AD. Outcome measures: sick leave during the two-week and one-year periods, socio-demographic characteristics, disease severity, quality of life and socio-occupational factors. Logistic regression analyses were used to determine influencing factors on sick leave over the two-week period. Results: In total, 253 patients were included; 12% of the patients had to take sick leave in the last two weeks due to AD and 42% in the past year. A higher level of symptom interference (OR 1.26; 95% CI 1.13–1.40 or perfectionism/diligence (OR 0.90; 95% CI 0.83–0.96 may respectively increase or decrease the number of sick leave days. Conclusion: Sick leave in patients with AD is a common problem and symptom interference and perfectionism/diligence appeared to influence it. Novel approaches are needed to deal with symptoms at work or school to reduce the amount of sick leave due to AD.

Errors, error detection, error correction and hippocampal-region damage: data and theories.

Science.gov (United States)

MacKay, Donald G; Johnson, Laura W

2013-11-01

This review and perspective article outlines 15 observational constraints on theories of errors, error detection, and error correction, and their relation to hippocampal-region (HR) damage. The core observations come from 10 studies with H.M., an amnesic with cerebellar and HR damage but virtually no neocortical damage. Three studies examined the detection of errors planted in visual scenes (e.g., a bird flying in a fish bowl in a school classroom) and sentences (e.g., I helped themselves to the birthday cake). In all three experiments, H.M. detected reliably fewer errors than carefully matched memory-normal controls. Other studies examined the detection and correction of self-produced errors, with controls for comprehension of the instructions, impaired visual acuity, temporal factors, motoric slowing, forgetting, excessive memory load, lack of motivation, and deficits in visual scanning or attention. In these studies, H.M. corrected reliably fewer errors than memory-normal and cerebellar controls, and his uncorrected errors in speech, object naming, and reading aloud exhibited two consistent features: omission and anomaly. For example, in sentence production tasks, H.M. omitted one or more words in uncorrected encoding errors that rendered his sentences anomalous (incoherent, incomplete, or ungrammatical) reliably more often than controls. Besides explaining these core findings, the theoretical principles discussed here explain H.M.'s retrograde amnesia for once familiar episodic and semantic information; his anterograde amnesia for novel information; his deficits in visual cognition, sentence comprehension, sentence production, sentence reading, and object naming; and effects of aging on his ability to read isolated low frequency words aloud. These theoretical principles also explain a wide range of other data on error detection and correction and generate new predictions for future test. Copyright © 2013 Elsevier Ltd. All rights reserved.

Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

Science.gov (United States)

Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant

2015-01-01

Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets. PMID:25993029

Predicting Students Drop Out: A Case Study

Science.gov (United States)

Dekker, Gerben W.; Pechenizkiy, Mykola; Vleeshouwers, Jan M.

2009-01-01

The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program…

Human Error Prediction and Countermeasures based on CREAM in Loading and Storage Phase of Spent Nuclear Fuel (SNF)

International Nuclear Information System (INIS)

Kim, Jae San; Kim, Min Su; Jo, Seong Youn

2007-01-01

With the steady demands for nuclear power energy in Korea, the amount of accumulated SNF has inevitably increased year by year. Thus far, SNF has been on-site transported from one unit to a nearby unit or an on-site dry storage facility. In the near future, as the amount of SNF generated approaches the capacity of these facilities, a percentage of it will be transported to another SNF storage facility. In the process of transporting SNF, human interactions involve inspecting and preparing the cask and spent fuel, loading the cask, transferring the cask and storage or monitoring the cask, etc. So, human actions play a significant role in SNF transportation. In analyzing incidents that have occurred during transport operations, several recent studies have indicated that 'human error' is a primary cause. Therefore, the objectives of this study are to predict and identify possible human errors during the loading and storage of SNF. Furthermore, after evaluating human error for each process, countermeasures to minimize human error are deduced

The Errors of Our Ways: Understanding Error Representations in Cerebellar-Dependent Motor Learning.

Science.gov (United States)

Popa, Laurentiu S; Streng, Martha L; Hewitt, Angela L; Ebner, Timothy J

2016-04-01

The cerebellum is essential for error-driven motor learning and is strongly implicated in detecting and correcting for motor errors. Therefore, elucidating how motor errors are represented in the cerebellum is essential in understanding cerebellar function, in general, and its role in motor learning, in particular. This review examines how motor errors are encoded in the cerebellar cortex in the context of a forward internal model that generates predictions about the upcoming movement and drives learning and adaptation. In this framework, sensory prediction errors, defined as the discrepancy between the predicted consequences of motor commands and the sensory feedback, are crucial for both on-line movement control and motor learning. While many studies support the dominant view that motor errors are encoded in the complex spike discharge of Purkinje cells, others have failed to relate complex spike activity with errors. Given these limitations, we review recent findings in the monkey showing that complex spike modulation is not necessarily required for motor learning or for simple spike adaptation. Also, new results demonstrate that the simple spike discharge provides continuous error signals that both lead and lag the actual movements in time, suggesting errors are encoded as both an internal prediction of motor commands and the actual sensory feedback. These dual error representations have opposing effects on simple spike discharge, consistent with the signals needed to generate sensory prediction errors used to update a forward internal model.

Neural correlates of sensory prediction errors in monkeys: evidence for internal models of voluntary self-motion in the cerebellum.

Science.gov (United States)

Cullen, Kathleen E; Brooks, Jessica X

2015-02-01

During self-motion, the vestibular system makes essential contributions to postural stability and self-motion perception. To ensure accurate perception and motor control, it is critical to distinguish between vestibular sensory inputs that are the result of externally applied motion (exafference) and that are the result of our own actions (reafference). Indeed, although the vestibular sensors encode vestibular afference and reafference with equal fidelity, neurons at the first central stage of sensory processing selectively encode vestibular exafference. The mechanism underlying this reafferent suppression compares the brain's motor-based expectation of sensory feedback with the actual sensory consequences of voluntary self-motion, effectively computing the sensory prediction error (i.e., exafference). It is generally thought that sensory prediction errors are computed in the cerebellum, yet it has been challenging to explicitly demonstrate this. We have recently addressed this question and found that deep cerebellar nuclei neurons explicitly encode sensory prediction errors during self-motion. Importantly, in everyday life, sensory prediction errors occur in response to changes in the effector or world (muscle strength, load, etc.), as well as in response to externally applied sensory stimulation. Accordingly, we hypothesize that altering the relationship between motor commands and the actual movement parameters will result in the updating in the cerebellum-based computation of exafference. If our hypothesis is correct, under these conditions, neuronal responses should initially be increased--consistent with a sudden increase in the sensory prediction error. Then, over time, as the internal model is updated, response modulation should decrease in parallel with a reduction in sensory prediction error, until vestibular reafference is again suppressed. The finding that the internal model predicting the sensory consequences of motor commands adapts for new

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation using Data from Patients with Type 1 Diabetes

DEFF Research Database (Denmark)

Wendt, Sabrina Lyngbye; Ranjan, Ajenthen; Møller, Jan Kloppenborg

2017-01-01

three PD model test fits in each of the seven subjects. Thus, we successfully validated the PD model by leave-one-out cross-validation in seven out of eight T1D patients. Conclusions: The PD model accurately simulates glucose excursions based on plasma insulin and glucagon concentrations. The reported...... for concentrations of glucagon, insulin, and glucose. We fitted pharmacokinetic (PK) models to insulin and glucagon data using maximum likelihood and maximum a posteriori estimation methods. Similarly, we fitted a pharmacodynamic (PD) model to glucose data. The PD model included multiplicative effects of insulin...... and glucagon on EGP. Bias and precision of PD model test fits were assessed by mean predictive error (MPE) and mean absolute predictive error (MAPE). Results: Assuming constant variables in a subject across nonoutlier visits and using thresholds of ±15% MPE and 20% MAPE, we accepted at least one and at most...

An investigation into multi-dimensional prediction models to estimate the pose error of a quadcopter in a CSP plant setting

Science.gov (United States)

Lock, Jacobus C.; Smit, Willie J.; Treurnicht, Johann

2016-05-01

The Solar Thermal Energy Research Group (STERG) is investigating ways to make heliostats cheaper to reduce the total cost of a concentrating solar power (CSP) plant. One avenue of research is to use unmanned aerial vehicles (UAVs) to automate and assist with the heliostat calibration process. To do this, the pose estimation error of each UAV must be determined and integrated into a calibration procedure. A computer vision (CV) system is used to measure the pose of a quadcopter UAV. However, this CV system contains considerable measurement errors. Since this is a high-dimensional problem, a sophisticated prediction model must be used to estimate the measurement error of the CV system for any given pose measurement vector. This paper attempts to train and validate such a model with the aim of using it to determine the pose error of a quadcopter in a CSP plant setting.

Error Resilient Video Compression Using Behavior Models

Directory of Open Access Journals (Sweden)

Jacco R. Taal

2004-03-01

Full Text Available Wireless and Internet video applications are inherently subjected to bit errors and packet errors, respectively. This is especially so if constraints on the end-to-end compression and transmission latencies are imposed. Therefore, it is necessary to develop methods to optimize the video compression parameters and the rate allocation of these applications that take into account residual channel bit errors. In this paper, we study the behavior of a predictive (interframe video encoder and model the encoders behavior using only the statistics of the original input data and of the underlying channel prone to bit errors. The resulting data-driven behavior models are then used to carry out group-of-pictures partitioning and to control the rate of the video encoder in such a way that the overall quality of the decoded video with compression and channel errors is optimized.

A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

Science.gov (United States)

Ye, Min; Nagar, Swati; Korzekwa, Ken

2015-01-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

Prediction of DVH parameter changes due to setup errors for breast cancer treatment based on 2D portal dosimetry

International Nuclear Information System (INIS)

Nijsten, S. M. J. J. G.; Elmpt, W. J. C. van; Mijnheer, B. J.; Minken, A. W. H.; Persoon, L. C. G. G.; Lambin, P.; Dekker, A. L. A. J.

2009-01-01

Electronic portal imaging devices (EPIDs) are increasingly used for portal dosimetry applications. In our department, EPIDs are clinically used for two-dimensional (2D) transit dosimetry. Predicted and measured portal dose images are compared to detect dose delivery errors caused for instance by setup errors or organ motion. The aim of this work is to develop a model to predict dose-volume histogram (DVH) changes due to setup errors during breast cancer treatment using 2D transit dosimetry. First, correlations between DVH parameter changes and 2D gamma parameters are investigated for different simulated setup errors, which are described by a binomial logistic regression model. The model calculates the probability that a DVH parameter changes more than a specific tolerance level and uses several gamma evaluation parameters for the planning target volume (PTV) projection in the EPID plane as input. Second, the predictive model is applied to clinically measured portal images. Predicted DVH parameter changes are compared to calculated DVH parameter changes using the measured setup error resulting from a dosimetric registration procedure. Statistical accuracy is investigated by using receiver operating characteristic (ROC) curves and values for the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Changes in the mean PTV dose larger than 5%, and changes in V 90 and V 95 larger than 10% are accurately predicted based on a set of 2D gamma parameters. Most pronounced changes in the three DVH parameters are found for setup errors in the lateral-medial direction. AUC, sensitivity, specificity, and negative predictive values were between 85% and 100% while the positive predictive values were lower but still higher than 54%. Clinical predictive value is decreased due to the occurrence of patient rotations or breast deformations during treatment, but the overall reliability of the predictive model remains high. Based on our

Predicting Factors of Drop Out Counseling Process in University Psychological Counseling and Guidance Center

Directory of Open Access Journals (Sweden)

Omer OZER

2015-03-01

Full Text Available Objective: Objective: The purpose of this study is to evaluate the predicting factors the drop out the counseling process. Methods: The study group consists of 555 college students admitted to a Counseling and Guidance Center (CGC and participated in at least one session of counseling after the first view in the 2013-2014 academic year. As a data collection tool, an “Application Form” on the demographic information and the “Brief Symptom Inventory” was applied to the students; and independent samples t-test and binary logistic regression techniques were used in the analysis of the collected data. Results: According to the analysis results, the age of the students attending the counseling process was found to be higher than those who drop out, but no significant difference was found in their psychometric properties in terms of continuation of the counseling process. Only the age of clients and their previous psychiatric help history was found to predict the dropping out counseling process early. Conclusion: Drop outs are less frequently observed in clients having a previous psychiatric help experience. In addition, it was determined that older clients less frequently drop out the counseling process

Self-Stigma and Coming Out about One's Mental Illness

Science.gov (United States)

Corrigan, Patrick W.; Morris, Scott; Larson, Jon; Rafacz, Jennifer; Wassel, Abigail; Michaels, Patrick; Wilkniss, Sandra; Batia, Karen; Rusch, Nicolas

2010-01-01

Self-stigma can undermine self-esteem and self-efficacy of people with serious mental illness. Coming out may be one way of handling self-stigma and it was expected that coming out would mediate the effects of self-stigma on quality of life. This study compares coming out to other approaches of controlling self-stigma. Eighty-five people with…

Phasic dopamine as a prediction error of intrinsic and extrinsic reinforcements driving both action acquisition and reward maximization: a simulated robotic study.

Science.gov (United States)

Mirolli, Marco; Santucci, Vieri G; Baldassarre, Gianluca

2013-03-01

An important issue of recent neuroscientific research is to understand the functional role of the phasic release of dopamine in the striatum, and in particular its relation to reinforcement learning. The literature is split between two alternative hypotheses: one considers phasic dopamine as a reward prediction error similar to the computational TD-error, whose function is to guide an animal to maximize future rewards; the other holds that phasic dopamine is a sensory prediction error signal that lets the animal discover and acquire novel actions. In this paper we propose an original hypothesis that integrates these two contrasting positions: according to our view phasic dopamine represents a TD-like reinforcement prediction error learning signal determined by both unexpected changes in the environment (temporary, intrinsic reinforcements) and biological rewards (permanent, extrinsic reinforcements). Accordingly, dopamine plays the functional role of driving both the discovery and acquisition of novel actions and the maximization of future rewards. To validate our hypothesis we perform a series of experiments with a simulated robotic system that has to learn different skills in order to get rewards. We compare different versions of the system in which we vary the composition of the learning signal. The results show that only the system reinforced by both extrinsic and intrinsic reinforcements is able to reach high performance in sufficiently complex conditions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error

Science.gov (United States)

Khair, Ummul; Fahmi, Hasanul; Hakim, Sarudin Al; Rahim, Robbi

2017-12-01

Prediction using a forecasting method is one of the most important things for an organization, the selection of appropriate forecasting methods is also important but the percentage error of a method is more important in order for decision makers to adopt the right culture, the use of the Mean Absolute Deviation and Mean Absolute Percentage Error to calculate the percentage of mistakes in the least square method resulted in a percentage of 9.77% and it was decided that the least square method be worked for time series and trend data.

Quantifying uncertainty for predictions with model error in non-Gaussian systems with intermittency

International Nuclear Information System (INIS)

Branicki, Michal; Majda, Andrew J

2012-01-01

This paper discusses a range of important mathematical issues arising in applications of a newly emerging stochastic-statistical framework for quantifying and mitigating uncertainties associated with prediction of partially observed and imperfectly modelled complex turbulent dynamical systems. The need for such a framework is particularly severe in climate science where the true climate system is vastly more complicated than any conceivable model; however, applications in other areas, such as neural networks and materials science, are just as important. The mathematical tools employed here rely on empirical information theory and fluctuation–dissipation theorems (FDTs) and it is shown that they seamlessly combine into a concise systematic framework for measuring and optimizing consistency and sensitivity of imperfect models. Here, we utilize a simple statistically exactly solvable ‘perfect’ system with intermittent hidden instabilities and with time-periodic features to address a number of important issues encountered in prediction of much more complex dynamical systems. These problems include the role and mitigation of model error due to coarse-graining, moment closure approximations, and the memory of initial conditions in producing short, medium and long-range predictions. Importantly, based on a suite of increasingly complex imperfect models of the perfect test system, we show that the predictive skill of the imperfect models and their sensitivity to external perturbations is improved by ensuring their consistency on the statistical attractor (i.e. the climate) with the perfect system. Furthermore, the discussed link between climate fidelity and sensitivity via the FDT opens up an enticing prospect of developing techniques for improving imperfect model sensitivity based on specific tests carried out in the training phase of the unperturbed statistical equilibrium/climate. (paper)

Early behavioral inhibition and increased error monitoring predict later social phobia symptoms in childhood.

Science.gov (United States)

Lahat, Ayelet; Lamm, Connie; Chronis-Tuscano, Andrea; Pine, Daniel S; Henderson, Heather A; Fox, Nathan A

2014-04-01

Behavioral inhibition (BI) is an early childhood temperament characterized by fearful responses to novelty and avoidance of social interactions. During adolescence, a subset of children with stable childhood BI develop social anxiety disorder and concurrently exhibit increased error monitoring. The current study examines whether increased error monitoring in 7-year-old, behaviorally inhibited children prospectively predicts risk for symptoms of social phobia at age 9 years. A total of 291 children were characterized on BI at 24 and 36 months of age. Children were seen again at 7 years of age, when they performed a Flanker task, and event-related potential (ERP) indices of response monitoring were generated. At age 9, self- and maternal-report of social phobia symptoms were obtained. Children high in BI, compared to those low in BI, displayed increased error monitoring at age 7, as indexed by larger (i.e., more negative) error-related negativity (ERN) amplitudes. In addition, early BI was related to later childhood social phobia symptoms at age 9 among children with a large difference in amplitude between ERN and correct-response negativity (CRN) at age 7. Heightened error monitoring predicts risk for later social phobia symptoms in children with high BI. Research assessing response monitoring in children with BI may refine our understanding of the mechanisms underlying risk for later anxiety disorders and inform prevention efforts. Copyright © 2014 American Academy of Child and Adolescent Psychiatry. All rights reserved.

Nonmarital romantic relationship commitment and leave behavior: the mediating role of dissolution consideration.

Science.gov (United States)

Vanderdrift, Laura E; Agnew, Christopher R; Wilson, Juan E

2009-09-01

Two studies investigated the process by which individuals in nonmarital romantic relationships characterized by low commitment move toward enacting leave behaviors. Predictions based on the behavioral, goal, and implementation intention literatures were tested using a measure of dissolution consideration developed for this research. Dissolution consideration assesses how salient relationship termination is for an individual while one's relationship is intact. Study 1 developed and validated a measure of dissolution consideration and Study 2 was a longitudinal test of the utility of dissolution consideration in predicting the enactment of leave behaviors. Results indicated that dissolution consideration mediates the association between commitment and enacting leave behaviors, is associated with taking more immediate action, and provides unique explanatory power in leave behavior beyond the effect of commitment alone. Collectively, the findings suggest that dissolution consideration is an intermediate step between commitment and stay/leave behavior in close relationships.

Thermal-Induced Errors Prediction and Compensation for a Coordinate Boring Machine Based on Time Series Analysis

Directory of Open Access Journals (Sweden)

Jun Yang

2014-01-01

Full Text Available To improve the CNC machine tools precision, a thermal error modeling for the motorized spindle was proposed based on time series analysis, considering the length of cutting tools and thermal declined angles, and the real-time error compensation was implemented. A five-point method was applied to measure radial thermal declinations and axial expansion of the spindle with eddy current sensors, solving the problem that the three-point measurement cannot obtain the radial thermal angle errors. Then the stationarity of the thermal error sequences was determined by the Augmented Dickey-Fuller Test Algorithm, and the autocorrelation/partial autocorrelation function was applied to identify the model pattern. By combining both Yule-Walker equations and information criteria, the order and parameters of the models were solved effectively, which improved the prediction accuracy and generalization ability. The results indicated that the prediction accuracy of the time series model could reach up to 90%. In addition, the axial maximum error decreased from 39.6 μm to 7 μm after error compensation, and the machining accuracy was improved by 89.7%. Moreover, the X/Y-direction accuracy can reach up to 77.4% and 86%, respectively, which demonstrated that the proposed methods of measurement, modeling, and compensation were effective.

Prediction of gas chromatography/electron capture detector retention times of chlorinated pesticides, herbicides, and organohalides by multivariate chemometrics methods

International Nuclear Information System (INIS)

Ghasemi, Jahanbakhsh; Asadpour, Saeid; Abdolmaleki, Azizeh

2007-01-01

A quantitative structure-retention relationship (QSRR) study, has been carried out on the gas chromatograph/electron capture detector (GC/ECD) system retention times (t R s) of 38 diverse chlorinated pesticides, herbicides, and organohalides by using molecular structural descriptors. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and partial least squares (PLS) regression. The stepwise regression using SPSS was used for the selection of the variables that resulted in the best-fitted models. Appropriate models with low standard errors and high correlation coefficients were obtained. Three types of molecular descriptors including electronic, steric and thermodynamic were used to develop a quantitative relationship between the retention times and structural properties. MLR and PLS analysis has been carried out to derive the best QSRR models. After variables selection, MLR and PLS methods used with leave-one-out cross validation for building the regression models. The predictive quality of the QSRR models were tested for an external prediction set of 12 compounds randomly chosen from 38 compounds. The PLS regression method was used to model the structure-retention relationships, more accurately. However, the results surprisingly showed more or less the same quality for MLR and PLS modeling according to squared regression coefficients R 2 which were 0.951 and 0.948 for MLR and PLS, respectively

One new diphenylmethane glycoside from the leaves of Psidium guajava L.

Science.gov (United States)

Shu, Ji-Cheng; Chou, Gui-Xin; Wang, Zheng-Tao

2012-11-01

To investigate the chemical constituents of Psidium guajava L, the EtOH/H(2)O extract of the fresh leaves was subjected to various chromatography. One diphenylmethane, one benzophenone, and eight flavonoids were isolated and elucidated as 2,6-dihydroxy-3-formaldehyde-5-methyl-4-O-(6″-O-galloyl-β-D-glucopyranosyl)-diphenylmethane (1), 2,6-dihydroxy-3,5-dimethyl-4-O-(6″-O-galloyl-β-D-glucopyranosyl)-benzophenone (2), kaempferol (3), quercetin (4), quercitrin (5), isoquercitrin (6), guaijaverin (7), avicularin (8), hyperoside (9), reynoutrin (10) by spectroscopic methods, including 1D and 2D NMR and HR-ESI-MS spectrometry as well as by comparison with published data. Compounds 5 and 10 are obtained from P. guajava for the first time, and compound 1 is a new diphenylmethane compound.

Chronology of prescribing error during the hospital stay and prediction of pharmacist's alerts overriding: a prospective analysis

Directory of Open Access Journals (Sweden)

Bruni Vanida

2010-01-01

Full Text Available Abstract Background Drug prescribing errors are frequent in the hospital setting and pharmacists play an important role in detection of these errors. The objectives of this study are (1 to describe the drug prescribing errors rate during the patient's stay, (2 to find which characteristics for a prescribing error are the most predictive of their reproduction the next day despite pharmacist's alert (i.e. override the alert. Methods We prospectively collected all medication order lines and prescribing errors during 18 days in 7 medical wards' using computerized physician order entry. We described and modelled the errors rate according to the chronology of hospital stay. We performed a classification and regression tree analysis to find which characteristics of alerts were predictive of their overriding (i.e. prescribing error repeated. Results 12 533 order lines were reviewed, 117 errors (errors rate 0.9% were observed and 51% of these errors occurred on the first day of the hospital stay. The risk of a prescribing error decreased over time. 52% of the alerts were overridden (i.e error uncorrected by prescribers on the following day. Drug omissions were the most frequently taken into account by prescribers. The classification and regression tree analysis showed that overriding pharmacist's alerts is first related to the ward of the prescriber and then to either Anatomical Therapeutic Chemical class of the drug or the type of error. Conclusions Since 51% of prescribing errors occurred on the first day of stay, pharmacist should concentrate his analysis of drug prescriptions on this day. The difference of overriding behavior between wards and according drug Anatomical Therapeutic Chemical class or type of error could also guide the validation tasks and programming of electronic alerts.

Predicting Taxi-Out Time at Congested Airports with Optimization-Based Support Vector Regression Methods

Directory of Open Access Journals (Sweden)

Guan Lian

2018-01-01

Full Text Available Accurate prediction of taxi-out time is significant precondition for improving the operationality of the departure process at an airport, as well as reducing the long taxi-out time, congestion, and excessive emission of greenhouse gases. Unfortunately, several of the traditional methods of predicting taxi-out time perform unsatisfactorily at congested airports. This paper describes and tests three of those conventional methods which include Generalized Linear Model, Softmax Regression Model, and Artificial Neural Network method and two improved Support Vector Regression (SVR approaches based on swarm intelligence algorithm optimization, which include Particle Swarm Optimization (PSO and Firefly Algorithm. In order to improve the global searching ability of Firefly Algorithm, adaptive step factor and Lévy flight are implemented simultaneously when updating the location function. Six factors are analysed, of which delay is identified as one significant factor in congested airports. Through a series of specific dynamic analyses, a case study of Beijing International Airport (PEK is tested with historical data. The performance measures show that the proposed two SVR approaches, especially the Improved Firefly Algorithm (IFA optimization-based SVR method, not only perform as the best modelling measures and accuracy rate compared with the representative forecast models, but also can achieve a better predictive performance when dealing with abnormal taxi-out time states.

How to regress and predict in a Bland-Altman plot? Review and contribution based on tolerance intervals and correlated-errors-in-variables models.

Science.gov (United States)

Francq, Bernard G; Govaerts, Bernadette

2016-06-30

Two main methodologies for assessing equivalence in method-comparison studies are presented separately in the literature. The first one is the well-known and widely applied Bland-Altman approach with its agreement intervals, where two methods are considered interchangeable if their differences are not clinically significant. The second approach is based on errors-in-variables regression in a classical (X,Y) plot and focuses on confidence intervals, whereby two methods are considered equivalent when providing similar measures notwithstanding the random measurement errors. This paper reconciles these two methodologies and shows their similarities and differences using both real data and simulations. A new consistent correlated-errors-in-variables regression is introduced as the errors are shown to be correlated in the Bland-Altman plot. Indeed, the coverage probabilities collapse and the biases soar when this correlation is ignored. Novel tolerance intervals are compared with agreement intervals with or without replicated data, and novel predictive intervals are introduced to predict a single measure in an (X,Y) plot or in a Bland-Atman plot with excellent coverage probabilities. We conclude that the (correlated)-errors-in-variables regressions should not be avoided in method comparison studies, although the Bland-Altman approach is usually applied to avert their complexity. We argue that tolerance or predictive intervals are better alternatives than agreement intervals, and we provide guidelines for practitioners regarding method comparison studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Policies on documentation and disciplinary action in hospital pharmacies after a medication error.

Science.gov (United States)

Bauman, A N; Pedersen, C A; Schommer, J C; Griffith, N L

2001-06-15

Hospital pharmacies were surveyed about policies on medication error documentation and actions taken against pharmacists involved in an error. The survey was mailed to 500 randomly selected hospital pharmacy directors in the United States. Data were collected on the existence of medication error reporting policies, what types of errors were documented and how, and hospital demographics. The response rate was 28%. Virtually all of the hospitals had policies and procedures for medication error reporting. Most commonly, documentation of oral and written reprimand was placed in the personnel file of a pharmacist involved in an error. One sixth of respondents had no policy on documentation or disciplinary action in the event of an error. Approximately one fourth of respondents reported that suspension or termination had been used as a form of disciplinary action; legal action was rarely used. Many respondents said errors that caused harm (42%) or death (40%) to the patient were documented in the personnel file, but 34% of hospitals did not document errors in the personnel file regardless of error type. Nearly three fourths of respondents differentiated between errors caught and not caught before a medication leaves the pharmacy and between errors caught and not caught before administration to the patient. More emphasis is needed on documentation of medication errors in hospital pharmacies.

'Filling one's days': managing sick leave legitimacy in an online forum.

Science.gov (United States)

Flinkfeldt, Marie

2011-07-01

An inherent part of the general understanding of illness is that it is incapacitating, making those who are ill unable to do things that they would normally do. Staying at home from work is a common consequence, and what `ill' people do while at home then becomes accountable. This article explores online discourse about the kinds of activities people engage in when on sick leave. It employs a discursive psychological framework for analysis, drawing heavily on conversation analysis. A Swedish internet forum thread on sick leave is examined, focusing on how the participants describe and account for the things they do when staying home from work due to illness. The analysis suggests that the participants' accounts of their activities delicately manage the legitimacy of their sick leave. In examining how this is done in practice, the analysis makes visible the balancing act between being ill enough to stay home from work and well enough for other activities. In the context of recent debates in Sweden and elsewhere about the legitimacy of sick leave in different situations, the analysis of how legitimacy is actually negotiated is an important concern, making visible the moral work of being on sick leave. © 2011 The Author. Sociology of Health & Illness © 2011 Foundation for the Sociology of Health & Illness/Blackwell Publishing Ltd.

Learning from One's Own Errors and Those of Others

Science.gov (United States)

Metcalfe, Janet; Xu, Judy

2017-01-01

Three experiments investigated the effects of making errors oneself, as compared to just hearing the correct answer without error generation, hearing another person make an error, or being "on-the-hook," that is, possibly but not necessarily being the person who would be "called-on" to give a response. In all three experiments,…

Detection of layup errors in prepreg laminates using shear ultrasonic waves

Science.gov (United States)

Hsu, David K.; Fischer, Brent A.

1996-11-01

The highly anisotropic elastic properties of the plies in a composite laminate manufactured from unidirectional prepregs interact strongly with the polarization direction of shear ultrasonic waves propagating through its thickness. The received signals in a 'crossed polarizer' transmission configuration are particularly sensitive to ply orientation and layup sequence in a laminate. Such measurements can therefore serve as an NDE tool for detecting layup errors. For example, it was shown experimentally recently that the sensitivity for detecting the presence of misoriented plies is better than one ply out of a 48-ply laminate of graphite epoxy. A physical model based on the decomposition and recombination of the shear polarization vector has been constructed and used in the interpretation and prediction of test results. Since errors should be detected early in the manufacturing process, this work also addresses the inspection of 'green' composite laminates using electromagnetic acoustic transducers (EMAT). Preliminary results for ply error detection obtained with EMAT probes are described.

Displacement of carbon-14 labelled amino acids from leaves

International Nuclear Information System (INIS)

Schiller, R.

1973-01-01

The displacement of amino acids from nature leaves was investigated. The amino acids (Ala, Asn, Asp, Glu, Gln, Val, Leu, Lys, Ser, Pro) were applied on the leaves in L-form, uniformly labelled with 14 C, and the type and direction of displacement have been observed. Most of the studies have been carried out on bush beans aged 3 to 4 weeks. The experiments were carried out in climatic chambers; in one case, barley plants just reaching maturity were used. In order to find out whether the applied amino acids were also displaced in their original form, freeze-dried plants were extracted and the 14 C activity of the various fraction was determined. The radioactivity of some free amino acids was determined after two-dimensional separation by thin film chromatography. (orig./HK) [de

Dynamical Predictability of Monthly Means.

Science.gov (United States)

Shukla, J.

1981-12-01

We have attempted to determine the theoretical upper limit of dynamical predictability of monthly means for prescribed nonfluctuating external forcings. We have extended the concept of `classical' predictability, which primarily refers to the lack of predictability due mainly to the instabilities of synoptic-scale disturbances, to the predictability of time averages, which are determined by the predictability of low-frequency planetary waves. We have carded out 60-day integrations of a global general circulation model with nine different initial conditions but identical boundary conditions of sea surface temperature, snow, sea ice and soil moisture. Three of these initial conditions are the observed atmospheric conditions on 1 January of 1975, 1976 and 1977. The other six initial conditions are obtained by superimposing over the observed initial conditions a random perturbation comparable to the errors of observation. The root-mean-square (rms) error of random perturbations at all the grid points and all the model levels is 3 m s1 in u and v components of wind. The rms vector wind error between the observed initial conditions is >15 m s1.It is hypothesized that for a given averaging period, if the rms error among the time averages predicted from largely different initial conditions becomes comparable to the rms error among the time averages predicted from randomly perturbed initial conditions, the time averages are dynamically unpredictable. We have carried out the analysis of variance to compare the variability, among the three groups, due to largely different initial conditions, and within each group due to random perturbations.It is found that the variances among the first 30-day means, predicted from largely different initial conditions, are significantly different from the variances due to random perturbations in the initial conditions, whereas the variances among 30-day means for days 31-60 are not distinguishable from the variances due to random initial

Predictive Value of the School-leaving Grade and Prognosis of Different Admission Groups for Academic Performance and Continuity in the Medical Course – a Longitudinal Study

Science.gov (United States)

Kadmon, Guni; Resch, Franz; Duelli, Roman; Kadmon, Martina

2014-01-01

Background: The school-leaving GPA and the time since completion of secondary education are the major criteria for admission to German medical schools. However, the predictive value of the school-leaving grade and the admission delay have not been thoroughly examined since the amendment of the Medical Licensing Regulations and the introduction of reformed curricula in 2002. Detailed information on the prognosis of the different admission groups is also missing. Aim: To examine the predictive values of the school-leaving grade and the age at enrolment for academic performance and continuity throughout the reformed medical course. Methods: The study includes the central admission groups “GPA-best” and “delayed admission” as well as the primary and secondary local admission groups of three consecutive cohorts. The relationship between the criteria academic performance and continuity and the predictors school-leaving GPA, enrolment age, and admission group affiliation were examined up to the beginning of the final clerkship year. Results: The academic performance and the prolongation of the pre-clinical part of undergraduate training were significantly related to the school-leaving GPA. Conversely, the dropout rate was related to age at enrolment. The students of the GPA-best group and the primary local admission group performed best and had the lowest dropout rates. The students of the delayed admission group and secondary local admission group performed significantly worse. More than 20% of these students dropped out within the pre-clinical course, half of them due to poor academic performance. However, the academic performance of all of the admission groups was highly variable and only about 35% of the students of each group reached the final clerkship year within the regular time. Discussion: The school-leaving grade and age appear to have different prognostic implications for academic performance and continuity. Both factors have consequences for the

Influence of accelerometer type and placement on physical activity energy expenditure prediction in manual wheelchair users.

Directory of Open Access Journals (Sweden)

Tom Edward Nightingale

Full Text Available To assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE in manual wheelchair users (MWUs.Seventeen MWUs (36 ± 10 yrs, 72 ± 11 kg completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km·hr-1 and propulsion at 4km·hr-1 (with an additional 8% body mass, 2% and 3% gradient on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W and upper arm (UA. Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis.Accelerometer outputs at each anatomical location were significantly (p < .01 associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88. Mean ± SD PAEE estimation errors for all activities combined were 15 ± 45%, 14 ± 50%, 3 ± 25% and 4 ± 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W.The results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device.

Influence of accelerometer type and placement on physical activity energy expenditure prediction in manual wheelchair users.

Science.gov (United States)

Nightingale, Tom Edward; Walhin, Jean-Philippe; Thompson, Dylan; Bilzon, James Lee John

2015-01-01

To assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE) in manual wheelchair users (MWUs). Seventeen MWUs (36 ± 10 yrs, 72 ± 11 kg) completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km·hr-1) and propulsion at 4km·hr-1 (with an additional 8% body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W) and upper arm (UA). Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis. Accelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88). Mean ± SD PAEE estimation errors for all activities combined were 15 ± 45%, 14 ± 50%, 3 ± 25% and 4 ± 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W. The results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device.

Error estimation for CFD aeroheating prediction under rarefied flow condition

Science.gov (United States)

Jiang, Yazhong; Gao, Zhenxun; Jiang, Chongwen; Lee, Chunhian

2014-12-01

Both direct simulation Monte Carlo (DSMC) and Computational Fluid Dynamics (CFD) methods have become widely used for aerodynamic prediction when reentry vehicles experience different flow regimes during flight. The implementation of slip boundary conditions in the traditional CFD method under Navier-Stokes-Fourier (NSF) framework can extend the validity of this approach further into transitional regime, with the benefit that much less computational cost is demanded compared to DSMC simulation. Correspondingly, an increasing error arises in aeroheating calculation as the flow becomes more rarefied. To estimate the relative error of heat flux when applying this method for a rarefied flow in transitional regime, theoretical derivation is conducted and a dimensionless parameter ɛ is proposed by approximately analyzing the ratio of the second order term to first order term in the heat flux expression in Burnett equation. DSMC simulation for hypersonic flow over a cylinder in transitional regime is performed to test the performance of parameter ɛ, compared with two other parameters, Knρ and MaṡKnρ.

STUDY OF ORGANIC ACIDS IN ALMOND LEAVES

Directory of Open Access Journals (Sweden)

Lenchyk L.V.

2015-05-01

Full Text Available Introduction. Almond (Amygdalus communis is a stone fruit, from the Rosaceae family, closest to the peach. It is spread throughout the entire Mediterranean region and afterwards to the Southwestern USA, Northern Africa, Turkey, Iran, Australia and South Africa. It is sensitive to wet conditions, and therefore is not grown in wet climates. Iran is located in the semi-arid region of the world. Because of its special tolerance to water stress, almond is one of the main agricultural products in rainfed condition in Iran. Almond leaves have been investigated for their phenolic content and antioxidant activity. It was found that total antioxidant activity and phenolic compounds exhibited variations according to season, plant organ (leaf and stem and variety. Analysis of previous research on almonds focused on investigating compounds mostly in seeds and phenolic compounds in leaves, but organic acids in leaves have not been studied. Aim of this study was investigation of organic acids in leaves of almond variety which is distributed in Razavi Khorasan province of Iran. Materials and Methods. In August 2012 almond leaves were collected in Iran, dried and grinded. The study of qualitative composition and quantitative determination of carboxylic acids in almond leaves was carried out by gas chromatography with mass spectrometric detection. For determination organic acids content, to 50 mg of dried plant material in 2 ml vial internal standard (50 μg of tridecane in hexane was added and filled up with 1.0 ml of methylating agent (14 % BCl3 in methanol, Supelco 3-3033. The mixture was kept in a sealed vial during 8 hours at 65 °C. At this time fatty oil was fully extracted, and hydrolyzed into its constituent fatty acids and their methylation was done. At the same time free organic and phenolcarbonic acids were methylated too. The reaction mixture was poured from the plant material sediment and was diluted with 1 ml of distilled water. To extract methyl

Conically scanning lidar error in complex terrain

Directory of Open Access Journals (Sweden)

Ferhat Bingöl

2009-05-01

Full Text Available Conically scanning lidars assume the flow to be homogeneous in order to deduce the horizontal wind speed. However, in mountainous or complex terrain this assumption is not valid implying a risk that the lidar will derive an erroneous wind speed. The magnitude of this error is measured by collocating a meteorological mast and a lidar at two Greek sites, one hilly and one mountainous. The maximum error for the sites investigated is of the order of 10 %. In order to predict the error for various wind directions the flows at both sites are simulated with the linearized flow model, WAsP Engineering 2.0. The measurement data are compared with the model predictions with good results for the hilly site, but with less success at the mountainous site. This is a deficiency of the flow model, but the methods presented in this paper can be used with any flow model.

At least some errors are randomly generated (Freud was wrong)

Science.gov (United States)

Sellen, A. J.; Senders, J. W.

1986-01-01

An experiment was carried out to expose something about human error generating mechanisms. In the context of the experiment, an error was made when a subject pressed the wrong key on a computer keyboard or pressed no key at all in the time allotted. These might be considered, respectively, errors of substitution and errors of omission. Each of seven subjects saw a sequence of three digital numbers, made an easily learned binary judgement about each, and was to press the appropriate one of two keys. Each session consisted of 1,000 presentations of randomly permuted, fixed numbers broken into 10 blocks of 100. One of two keys should have been pressed within one second of the onset of each stimulus. These data were subjected to statistical analyses in order to probe the nature of the error generating mechanisms. Goodness of fit tests for a Poisson distribution for the number of errors per 50 trial interval and for an exponential distribution of the length of the intervals between errors were carried out. There is evidence for an endogenous mechanism that may best be described as a random error generator. Furthermore, an item analysis of the number of errors produced per stimulus suggests the existence of a second mechanism operating on task driven factors producing exogenous errors. Some errors, at least, are the result of constant probability generating mechanisms with error rate idiosyncratically determined for each subject.

Assessing explicit error reporting in the narrative electronic medical record using keyword searching.

Science.gov (United States)

Cao, Hui; Stetson, Peter; Hripcsak, George

2003-01-01

Many types of medical errors occur in and outside of hospitals, some of which have very serious consequences and increase cost. Identifying errors is a critical step for managing and preventing them. In this study, we assessed the explicit reporting of medical errors in the electronic record. We used five search terms "mistake," "error," "incorrect," "inadvertent," and "iatrogenic" to survey several sets of narrative reports including discharge summaries, sign-out notes, and outpatient notes from 1991 to 2000. We manually reviewed all the positive cases and identified them based on the reporting of physicians. We identified 222 explicitly reported medical errors. The positive predictive value varied with different keywords. In general, the positive predictive value for each keyword was low, ranging from 3.4 to 24.4%. Therapeutic-related errors were the most common reported errors and these reported therapeutic-related errors were mainly medication errors. Keyword searches combined with manual review indicated some medical errors that were reported in medical records. It had a low sensitivity and a moderate positive predictive value, which varied by search term. Physicians were most likely to record errors in the Hospital Course and History of Present Illness sections of discharge summaries. The reported errors in medical records covered a broad range and were related to several types of care providers as well as non-health care professionals.

Forecast errors in dust vertical distributions over Rome (Italy): Multiple particle size representation and cloud contributions

Science.gov (United States)

Kishcha, P.; Alpert, P.; Shtivelman, A.; Krichak, S. O.; Joseph, J. H.; Kallos, G.; Katsafados, P.; Spyrou, C.; Gobbi, G. P.; Barnaba, F.; Nickovic, S.; PéRez, C.; Baldasano, J. M.

2007-08-01

In this study, forecast errors in dust vertical distributions were analyzed. This was carried out by using quantitative comparisons between dust vertical profiles retrieved from lidar measurements over Rome, Italy, performed from 2001 to 2003, and those predicted by models. Three models were used: the four-particle-size Dust Regional Atmospheric Model (DREAM), the older one-particle-size version of the SKIRON model from the University of Athens (UOA), and the pre-2006 one-particle-size Tel Aviv University (TAU) model. SKIRON and DREAM are initialized on a daily basis using the dust concentration from the previous forecast cycle, while the TAU model initialization is based on the Total Ozone Mapping Spectrometer aerosol index (TOMS AI). The quantitative comparison shows that (1) the use of four-particle-size bins in the dust modeling instead of only one-particle-size bins improves dust forecasts; (2) cloud presence could contribute to noticeable dust forecast errors in SKIRON and DREAM; and (3) as far as the TAU model is concerned, its forecast errors were mainly caused by technical problems with TOMS measurements from the Earth Probe satellite. As a result, dust forecast errors in the TAU model could be significant even under cloudless conditions. The DREAM versus lidar quantitative comparisons at different altitudes show that the model predictions are more accurate in the middle part of dust layers than in the top and bottom parts of dust layers.

Study on Apparent Kinetic Prediction Model of the Smelting Reduction Based on the Time-Series

Directory of Open Access Journals (Sweden)

Guo-feng Fan

2012-01-01

Full Text Available A series of direct smelting reduction experiment has been carried out with high phosphorous iron ore of the different bases by thermogravimetric analyzer. The derivative thermogravimetric (DTG data have been obtained from the experiments. One-step forward local weighted linear (LWL method , one of the most suitable ways of predicting chaotic time-series methods which focus on the errors, is used to predict DTG. In the meanwhile, empirical mode decomposition-autoregressive (EMD-AR, a data mining technique in signal processing, is also used to predict DTG. The results show that (1 EMD-AR(4 is the most appropriate and its error is smaller than the former; (2 root mean square error (RMSE has decreased about two-thirds; (3 standardized root mean square error (NMSE has decreased in an order of magnitude. Finally in this paper, EMD-AR method has been improved by golden section weighting; its error would be smaller than before. Therefore, the improved EMD-AR model is a promising alternative for apparent reaction rate (DTG. The analytical results have been an important reference in the field of industrial control.

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

Science.gov (United States)

Ye, Min; Nagar, Swati; Korzekwa, Ken

2016-04-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Pro-Inflammatory Cytokines Predict Relapse-Free Survival after One Month of Interferon-α but Not Observation in Intermediate Risk Melanoma Patients.

Directory of Open Access Journals (Sweden)

Ahmad A Tarhini

Full Text Available E1697 was a phase III trial of adjuvant interferon (IFN-α2b for one month (Arm B versus observation (Arm A in patients with resected melanoma at intermediate risk. We evaluated the levels of candidate serum cytokines, the HLA genotype, polymorphisms of CTLA4 and FOXP3 genes and the development of autoantibodies for their association with relapse free survival (RFS in Arm A and Arm B among 268 patients with banked biospecimens.ELISA was used to test 5 autoantibodies. Luminex/One Lambda LABTypeRSSO was used for HLA Genotyping. Selected CTLA4 and FOXP3 Single nucleotide polymorphisms (SNPs and microsatellites were tested for by polymerase chain reaction (PCR. Sixteen serum cytokines were tested at baseline and one month by Luminex xMAP multiplex technology. Cox Proportional Hazards model was applied and the Wald test was used to test the marginal association of each individual marker and RFS. We used the Lasso approach to select the markers to be included in a multi-marker Cox Proportional Hazards model. The ability of the resulting models to predict one year RFS was evaluated by the time-dependent ROC curve. The leave-one-out method of cross validation (LOOCV was used to avoid over-fitting of the data.In the multi-marker modeling analysis conducted in Arm B, one month serum IL2Rα, IL-12p40 and IFNα levels predicted one year RFS with LOOCV AUC = 82%. Among the three markers selected, IL2Rα and IFNα were the most stable (selected in all the cross validation cycles. The risk score (linear combination of the 3 markers separated the RFS curves of low and high risk groups well (p = 0.05. This model did not hold for Arm A, indicating a differential marker profile in Arm B linked to the intervention (adjuvant therapy.Early on-treatment proinflammatory serum markers (IL2Rα, IL-12p40, IFNα significantly predict RFS in our cohort of patients treated with adjuvant IFN-α2b and warrant further study.

SimCommSys: taking the errors out of error-correcting code simulations

Directory of Open Access Journals (Sweden)

Johann A. Briffa

2014-06-01

Full Text Available In this study, we present SimCommSys, a simulator of communication systems that we are releasing under an open source license. The core of the project is a set of C + + libraries defining communication system components and a distributed Monte Carlo simulator. Of principal interest is the error-control coding component, where various kinds of binary and non-binary codes are implemented, including turbo, LDPC, repeat-accumulate and Reed–Solomon. The project also contains a number of ready-to-build binaries implementing various stages of the communication system (such as the encoder and decoder, a complete simulator and a system benchmark. Finally, SimCommSys also provides a number of shell and python scripts to encapsulate routine use cases. As long as the required components are already available in SimCommSys, the user may simulate complete communication systems of their own design without any additional programming. The strict separation of development (needed only to implement new components and use (to simulate specific constructions encourages reproducibility of experimental work and reduces the likelihood of error. Following an overview of the framework, we provide some examples of how to use the framework, including the implementation of a simple codec, the specification of communication systems and their simulation.

Modeling Input Errors to Improve Uncertainty Estimates for Sediment Transport Model Predictions

Science.gov (United States)

Jung, J. Y.; Niemann, J. D.; Greimann, B. P.

2016-12-01

Bayesian methods using Markov chain Monte Carlo algorithms have recently been applied to sediment transport models to assess the uncertainty in the model predictions due to the parameter values. Unfortunately, the existing approaches can only attribute overall uncertainty to the parameters. This limitation is critical because no model can produce accurate forecasts if forced with inaccurate input data, even if the model is well founded in physical theory. In this research, an existing Bayesian method is modified to consider the potential errors in input data during the uncertainty evaluation process. The input error is modeled using Gaussian distributions, and the means and standard deviations are treated as uncertain parameters. The proposed approach is tested by coupling it to the Sedimentation and River Hydraulics - One Dimension (SRH-1D) model and simulating a 23-km reach of the Tachia River in Taiwan. The Wu equation in SRH-1D is used for computing the transport capacity for a bed material load of non-cohesive material. Three types of input data are considered uncertain: (1) the input flowrate at the upstream boundary, (2) the water surface elevation at the downstream boundary, and (3) the water surface elevation at a hydraulic structure in the middle of the reach. The benefits of modeling the input errors in the uncertainty analysis are evaluated by comparing the accuracy of the most likely forecast and the coverage of the observed data by the credible intervals to those of the existing method. The results indicate that the internal boundary condition has the largest uncertainty among those considered. Overall, the uncertainty estimates from the new method are notably different from those of the existing method for both the calibration and forecast periods.

Predicting Drop-Out from Social Behaviour of Students

Science.gov (United States)

Bayer, Jaroslav; Bydzovska, Hana; Geryk, Jan; Obsivac, Tomas; Popelinsky, Lubomir

2012-01-01

This paper focuses on predicting drop-outs and school failures when student data has been enriched with data derived from students social behaviour. These data describe social dependencies gathered from e-mail and discussion board conversations, among other sources. We describe an extraction of new features from both student data and behaviour…

Complex terrain wind resource estimation with the wind-atlas method: Prediction errors using linearized and nonlinear CFD micro-scale models

DEFF Research Database (Denmark)

Troen, Ib; Bechmann, Andreas; Kelly, Mark C.

2014-01-01

Using the Wind Atlas methodology to predict the average wind speed at one location from measured climatological wind frequency distributions at another nearby location we analyse the relative prediction errors using a linearized flow model (IBZ) and a more physically correct fully non-linear 3D...... flow model (CFD) for a number of sites in very complex terrain (large terrain slopes). We first briefly describe the Wind Atlas methodology as implemented in WAsP and the specifics of the “classical” model setup and the new setup allowing the use of the CFD computation engine. We discuss some known...

Reward prediction error signal enhanced by striatum-amygdala interaction explains the acceleration of probabilistic reward learning by emotion.

Science.gov (United States)

Watanabe, Noriya; Sakagami, Masamichi; Haruno, Masahiko

2013-03-06

Learning does not only depend on rationality, because real-life learning cannot be isolated from emotion or social factors. Therefore, it is intriguing to determine how emotion changes learning, and to identify which neural substrates underlie this interaction. Here, we show that the task-independent presentation of an emotional face before a reward-predicting cue increases the speed of cue-reward association learning in human subjects compared with trials in which a neutral face is presented. This phenomenon was attributable to an increase in the learning rate, which regulates reward prediction errors. Parallel to these behavioral findings, functional magnetic resonance imaging demonstrated that presentation of an emotional face enhanced reward prediction error (RPE) signal in the ventral striatum. In addition, we also found a functional link between this enhanced RPE signal and increased activity in the amygdala following presentation of an emotional face. Thus, this study revealed an acceleration of cue-reward association learning by emotion, and underscored a role of striatum-amygdala interactions in the modulation of the reward prediction errors by emotion.

Data Analysis & Statistical Methods for Command File Errors

Science.gov (United States)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes.

Science.gov (United States)

Das, Barnan; Cook, Diane J; Krishnan, Narayanan C; Schmitter-Edgecombe, Maureen

2016-08-01

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step towards automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.

Properties of leaves particleboard for sheathing application

Science.gov (United States)

Nuryawan, Arif; Rahmawaty

2018-03-01

Manufacturing particleboard (PB) made of leaves was carried out to make non-structural building components, such as insulation, partition, wall, and sheathing. Raw materials used dry leaves originated from plantation (palm oil leaves) and forest plantation (magahony leaves). The adhesive used was interior type thermosetting commercial resins, namely 10% urea-formaldehyde (UF) based on oven dry leaves. Hardener used for UF resin was 1% and 3% ammonium chloride (NH4Cl) 20% (w/w), respectively. Technically, the target density of PB was 0.8 g/cm3 with the dimension’s size of (250 x 250 x 10) mm3. The pressure, temperature, and time of pressing of the hot press were 25 kgf/cm2, 120C, and 10 minutes, respectively. After conditioning for one week, the PB then was evaluated their physical and mechanical properties according to Japanese Industrial Standard (JIS) A 5908 (2003). Results of this work showed: 1) Both types of PB (palm oil and mahagony leaves) were feasible to be produced for non-structural applications; 2) Addition of hardener enhanced the physical and mechanical properties of PB; 3) It was recommended to enhance the performance of the PB by manipulation of the raw materials and the design.

When theory and biology differ: The relationship between reward prediction errors and expectancy.

Science.gov (United States)

Williams, Chad C; Hassall, Cameron D; Trska, Robert; Holroyd, Clay B; Krigolson, Olave E

2017-10-01

Comparisons between expectations and outcomes are critical for learning. Termed prediction errors, the violations of expectancy that occur when outcomes differ from expectations are used to modify value and shape behaviour. In the present study, we examined how a wide range of expectancy violations impacted neural signals associated with feedback processing. Participants performed a time estimation task in which they had to guess the duration of one second while their electroencephalogram was recorded. In a key manipulation, we varied task difficulty across the experiment to create a range of different feedback expectancies - reward feedback was either very expected, expected, 50/50, unexpected, or very unexpected. As predicted, the amplitude of the reward positivity, a component of the human event-related brain potential associated with feedback processing, scaled inversely with expectancy (e.g., unexpected feedback yielded a larger reward positivity than expected feedback). Interestingly, the scaling of the reward positivity to outcome expectancy was not linear as would be predicted by some theoretical models. Specifically, we found that the amplitude of the reward positivity was about equivalent for very expected and expected feedback, and for very unexpected and unexpected feedback. As such, our results demonstrate a sigmoidal relationship between reward expectancy and the amplitude of the reward positivity, with interesting implications for theories of reinforcement learning. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantifying and handling errors in instrumental measurements using the measurement error theory

DEFF Research Database (Denmark)

Andersen, Charlotte Møller; Bro, R.; Brockhoff, P.B.

2003-01-01

. This is a new way of using the measurement error theory. Reliability ratios illustrate that the models for the two fish species are influenced differently by the error. However, the error seems to influence the predictions of the two reference measures in the same way. The effect of using replicated x...... measurements. A new general formula is given for how to correct the least squares regression coefficient when a different number of replicated x-measurements is used for prediction than for calibration. It is shown that the correction should be applied when the number of replicates in prediction is less than...

[Teacher sick leave: Prevalence, duration, reasons and covariates].

Science.gov (United States)

Vercambre-Jacquot, M-N; Gilbert, F; Billaudeau, N

2018-02-01

Absences from work have considerable social and economic impact. In the education sector, the phenomenon is particularly worrying since teacher sick leave has an impact on the overall performance of the education system. Yet, available data are scarce. In April-June 2013, 2653 teachers responded to a population-based postal survey on their quality of life (enquête Qualité de vie des enseignants, MGEN Foundation/Ministry of education, response rate 53 %). Besides questions on work environment and health, teachers were asked to describe their eventual sick leave(s) since the beginning of the school year: duration, type and medical reasons. Self-reported information was reinforced by administrative data from ministerial databases and weighted to be extrapolated to all French teachers. Tobit models adjusted for individual factors of a private nature were used to investigate different occupational risk factors of teacher sick leave, taking into account both the estimated effect on the probability of sick leave and the length of it. More than one in three teachers (36 %) reported having had at least one day of sick leave since the beginning of the school year. Respiratory/ENT diseases were the leading reason for sick leave (37 %). However, and because sick leave duration depended on the underlying health problem, such diseases came in third place among justifications of sick leave days (14 %), far behind musculoskeletal problems (27 %) and neurological and psychological disorders (25 %). Tobit models suggested that some occupational factors significantly associated with the risk of sick leave may represent promising preventive targets, including high psychological demand, workplace violence and unfavorable socio-environmental context. Our study provides objective evidence about the issue of sick leave among French teachers, highlighting the usefulness of implementing actions to minimize its weight. To this end, the study findings point-out the importance of

Safety analysis methodology with assessment of the impact of the prediction errors of relevant parameters

International Nuclear Information System (INIS)

Galia, A.V.

2011-01-01

The best estimate plus uncertainty approach (BEAU) requires the use of extensive resources and therefore it is usually applied for cases in which the available safety margin obtained with a conservative methodology can be questioned. Outside the BEAU methodology, there is not a clear approach on how to deal with the issue of considering the uncertainties resulting from prediction errors in the safety analyses performed for licensing submissions. However, the regulatory document RD-310 mentions that the analysis method shall account for uncertainties in the analysis data and models. A possible approach is presented, that is simple and reasonable, representing just the author's views, to take into account the impact of prediction errors and other uncertainties when performing safety analysis in line with regulatory requirements. The approach proposes taking into account the prediction error of relevant parameters. Relevant parameters would be those plant parameters that are surveyed and are used to initiate the action of a mitigating system or those that are representative of the most challenging phenomena for the integrity of a fission barrier. Examples of the application of the methodology are presented involving a comparison between the results with the new approach and a best estimate calculation during the blowdown phase for two small breaks in a generic CANDU 6 station. The calculations are performed with the CATHENA computer code. (author)

The error in total error reduction.

Science.gov (United States)

Witnauer, James E; Urcelay, Gonzalo P; Miller, Ralph R

2014-02-01

Most models of human and animal learning assume that learning is proportional to the discrepancy between a delivered outcome and the outcome predicted by all cues present during that trial (i.e., total error across a stimulus compound). This total error reduction (TER) view has been implemented in connectionist and artificial neural network models to describe the conditions under which weights between units change. Electrophysiological work has revealed that the activity of dopamine neurons is correlated with the total error signal in models of reward learning. Similar neural mechanisms presumably support fear conditioning, human contingency learning, and other types of learning. Using a computational modeling approach, we compared several TER models of associative learning to an alternative model that rejects the TER assumption in favor of local error reduction (LER), which assumes that learning about each cue is proportional to the discrepancy between the delivered outcome and the outcome predicted by that specific cue on that trial. The LER model provided a better fit to the reviewed data than the TER models. Given the superiority of the LER model with the present data sets, acceptance of TER should be tempered. Copyright © 2013 Elsevier Inc. All rights reserved.

Quantifying the predictive consequences of model error with linear subspace analysis

Science.gov (United States)

White, Jeremy T.; Doherty, John E.; Hughes, Joseph D.

2014-01-01

All computer models are simplified and imperfect simulators of complex natural systems. The discrepancy arising from simplification induces bias in model predictions, which may be amplified by the process of model calibration. This paper presents a new method to identify and quantify the predictive consequences of calibrating a simplified computer model. The method is based on linear theory, and it scales efficiently to the large numbers of parameters and observations characteristic of groundwater and petroleum reservoir models. The method is applied to a range of predictions made with a synthetic integrated surface-water/groundwater model with thousands of parameters. Several different observation processing strategies and parameterization/regularization approaches are examined in detail, including use of the Karhunen-Loève parameter transformation. Predictive bias arising from model error is shown to be prediction specific and often invisible to the modeler. The amount of calibration-induced bias is influenced by several factors, including how expert knowledge is applied in the design of parameterization schemes, the number of parameters adjusted during calibration, how observations and model-generated counterparts are processed, and the level of fit with observations achieved through calibration. Failure to properly implement any of these factors in a prediction-specific manner may increase the potential for predictive bias in ways that are not visible to the calibration and uncertainty analysis process.

Dopamine prediction errors in reward learning and addiction: from theory to neural circuitry

Science.gov (United States)

Keiflin, Ronald; Janak, Patricia H.

2015-01-01

Summary Midbrain dopamine (DA) neurons are proposed to signal reward prediction error (RPE), a fundamental parameter in associative learning models. This RPE hypothesis provides a compelling theoretical framework for understanding DA function in reward learning and addiction. New studies support a causal role for DA-mediated RPE activity in promoting learning about natural reward; however, this question has not been explicitly tested in the context of drug addiction. In this review, we integrate theoretical models with experimental findings on the activity of DA systems, and on the causal role of specific neuronal projections and cell types, to provide a circuit-based framework for probing DA-RPE function in addiction. By examining error-encoding DA neurons in the neural network in which they are embedded, hypotheses regarding circuit-level adaptations that possibly contribute to pathological error-signaling and addiction can be formulated and tested. PMID:26494275

Dopamine Prediction Errors in Reward Learning and Addiction: From Theory to Neural Circuitry.

Science.gov (United States)

Keiflin, Ronald; Janak, Patricia H

2015-10-21

Midbrain dopamine (DA) neurons are proposed to signal reward prediction error (RPE), a fundamental parameter in associative learning models. This RPE hypothesis provides a compelling theoretical framework for understanding DA function in reward learning and addiction. New studies support a causal role for DA-mediated RPE activity in promoting learning about natural reward; however, this question has not been explicitly tested in the context of drug addiction. In this review, we integrate theoretical models with experimental findings on the activity of DA systems, and on the causal role of specific neuronal projections and cell types, to provide a circuit-based framework for probing DA-RPE function in addiction. By examining error-encoding DA neurons in the neural network in which they are embedded, hypotheses regarding circuit-level adaptations that possibly contribute to pathological error signaling and addiction can be formulated and tested. Copyright © 2015 Elsevier Inc. All rights reserved.

Is ozone model bias driven by errors in cloud predictions? A quantitative assessment using satellite cloud retrievals in WRF-Chem

Science.gov (United States)

Ryu, Y. H.; Hodzic, A.; Barré, J.; Descombes, G.; Minnis, P.

2017-12-01

Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much of the bias in O3 predictions is caused by inaccurate cloud predictions. This study quantifies the errors in surface O3 predictions associated with clouds in summertime over CONUS using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Cloud fields used for photochemistry are corrected based on satellite cloud retrievals in sensitivity simulations. It is found that the WRF-Chem model is able to detect about 60% of clouds in the right locations and generally underpredicts cloud optical depths. The errors in hourly O3 due to the errors in cloud predictions can be up to 60 ppb. On average in summertime over CONUS, the errors in 8-h average O3 of 1-6 ppb are found to be attributable to those in cloud predictions under cloudy sky conditions. The contribution of changes in photolysis rates due to clouds is found to be larger ( 80 % on average) than that of light-dependent BVOC emissions. The effects of cloud corrections on O­3 are about 2 times larger in VOC-limited than NOx-limited regimes, suggesting that the benefits of accurate cloud predictions would be greater in VOC-limited than NOx-limited regimes.

Prediction of the residual strength of clay using functional networks

Directory of Open Access Journals (Sweden)

S.Z. Khan

2016-01-01

Full Text Available Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks (FN using data available in the literature. The performance of FN was compared with support vector machine (SVM and artificial neural network (ANN based on statistical parameters like correlation coefficient (R, Nash--Sutcliff coefficient of efficiency (E, absolute average error (AAE, maximum average error (MAE and root mean square error (RMSE. Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output.

Effect of heteroscedasticity treatment in residual error models on model calibration and prediction uncertainty estimation

Science.gov (United States)

Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli

2017-11-01

The heteroscedasticity treatment in residual error models directly impacts the model calibration and prediction uncertainty estimation. This study compares three methods to deal with the heteroscedasticity, including the explicit linear modeling (LM) method and nonlinear modeling (NL) method using hyperbolic tangent function, as well as the implicit Box-Cox transformation (BC). Then a combined approach (CA) combining the advantages of both LM and BC methods has been proposed. In conjunction with the first order autoregressive model and the skew exponential power (SEP) distribution, four residual error models are generated, namely LM-SEP, NL-SEP, BC-SEP and CA-SEP, and their corresponding likelihood functions are applied to the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin, China. Results show that the LM-SEP yields the poorest streamflow predictions with the widest uncertainty band and unrealistic negative flows. The NL and BC methods can better deal with the heteroscedasticity and hence their corresponding predictive performances are improved, yet the negative flows cannot be avoided. The CA-SEP produces the most accurate predictions with the highest reliability and effectively avoids the negative flows, because the CA approach is capable of addressing the complicated heteroscedasticity over the study basin.

Is writing style predictive of scientific fraud?

DEFF Research Database (Denmark)

Braud, Chloé Elodie; Søgaard, Anders

2017-01-01

The problem of detecting scientific fraud using machine learning was recently introduced, with initial, positive results from a model taking into account various general indicators. The results seem to suggest that writing style is predictive of scientific fraud. We revisit these initial experime......The problem of detecting scientific fraud using machine learning was recently introduced, with initial, positive results from a model taking into account various general indicators. The results seem to suggest that writing style is predictive of scientific fraud. We revisit these initial...... experiments, and show that the leave-one-out testing procedure they used likely leads to a slight over-estimate of the predictability, but also that simple models can outperform their proposed model by some margin. We go on to explore more abstract linguistic features, such as linguistic complexity...

Maternity leave, women's employment, and marital incompatibility.

Science.gov (United States)

Hyde, J S; Essex, M J; Clark, R; Klein, M H

2001-09-01

This research investigated the relationship between the length of women's maternity leave and marital incompatibility, in the context of other variables including the woman's employment, her dissatisfaction with the division of household labor, and her sense of role overload. Length of leave, work hours, and family salience were associated with several forms of dissatisfaction, which in turn predicted role overload. Role overload predicted increased marital incompatibility for experienced mothers but did not for first-time mothers, for whom discrepancies between preferred and actual child care were more important. Length of maternity leave showed significant interactions with other variables, supporting the hypothesis that a short leave is a risk factor that, when combined with another risk factor, contributes to personal and marital distress.

Sick Leave within 5 Years of Whiplash Trauma Predicts Recovery: A Prospective Cohort and Register-Based Study.

Science.gov (United States)

Carstensen, Tina Birgitte Wisbech; Fink, Per; Oernboel, Eva; Kasch, Helge; Jensen, Troels Staehelin; Frostholm, Lisbeth

2015-01-01

10-22% of individuals sustaining whiplash trauma develop persistent symptoms resulting in reduced working ability and decreased quality of life, but it is poorly understood why some people do not recover. Various collision and post-collision risk factors have been studied, but little is known about pre-collision risk factors. In particular, the impact of sickness and socioeconomic factors before the collision on recovery is sparsely explored. The aim of this study was to examine if welfare payments received within five years pre-collision predict neck pain and negative change in provisional situation one year post-collision. 719 individuals with acute whiplash trauma consecutively recruited from emergency departments or primary care after car accidents in Denmark completed questionnaires on socio-demographic and health factors immediately after the collision. After 12 months, a visual analogue scale on neck pain intensity was completed. 3595 matched controls in the general population were sampled, and national public register data on social benefits and any other welfare payments were obtained for participants with acute whiplash trauma and controls from five years pre-collision to 15 months after. Participants with acute whiplash trauma who had received sickness benefit for more than 12 weeks pre-collision had increased odds for negative change in future provisional situation (Odds Ratio (OR) (95% Confidence Interval (CI) = 3.8 (2.1;7.1)) and future neck pain (OR (95%CI) = 3.3 (1.8;6.3)), controlling for other known risk factors. Participants with acute whiplash trauma had weaker attachment to labour market (more weeks of sick leave (χ2(2) = 36.7, p whiplash trauma raised the odds for future negative change in provisional situation (OR (95%CI) = 3.1 (2.3;4.4)) compared with controls. Sick leave before the collision strongly predicted prolonged recovery following whiplash trauma. Participants with acute whiplash trauma had weaker attachment to labour market pre

Reducing the time until psychotherapy initiation reduces sick leave duration in participants diagnosed with anxiety and mood disorders.

Science.gov (United States)

Alonso, Sandra; Marco, José H; Andani, Joaquín

2018-01-01

Sick leave in patients with a mental disorder is characterized by having a long duration. Studies suggest that the time until a patient on sick leave for a common mental health disorder initiates evaluation and treatment by a healthcare professional is an important factor in the duration of the sick leave. However, in these studies, the intervention was not performed by a mental health specialist. The aim of this study was to find out whether the length of sick leave was associated with the time before initiating psychotherapy, age, time until returning to work after psychotherapy ends, and duration of psychotherapy. In a further analysis, we examined whether the model composed of age, duration of psychotherapy, and time before initiating psychotherapy predicted the length of sick leave. The sample consisted of 2,423 participants, 64.1% (n = 1,554) women and 35.9% (n = 869) men, who were on sick leave for anxiety disorders or depressive disorder. The total duration of the sick leave of participants diagnosed with depression and anxiety was positively associated with the time before beginning psychotherapy. Time before beginning psychotherapy predicted the length of sick leave when the variables age and duration of psychotherapy were controlled. It is necessary to reduce the time until beginning psychotherapy in people on sick leave for common mental disorders. Copyright © 2017 John Wiley & Sons, Ltd.

Learning time-dependent noise to reduce logical errors: real time error rate estimation in quantum error correction

Science.gov (United States)

Huo, Ming-Xia; Li, Ying

2017-12-01

Quantum error correction is important to quantum information processing, which allows us to reliably process information encoded in quantum error correction codes. Efficient quantum error correction benefits from the knowledge of error rates. We propose a protocol for monitoring error rates in real time without interrupting the quantum error correction. Any adaptation of the quantum error correction code or its implementation circuit is not required. The protocol can be directly applied to the most advanced quantum error correction techniques, e.g. surface code. A Gaussian processes algorithm is used to estimate and predict error rates based on error correction data in the past. We find that using these estimated error rates, the probability of error correction failures can be significantly reduced by a factor increasing with the code distance.

Prediction of Monte Carlo errors by a theory generalized to treat track-length estimators

International Nuclear Information System (INIS)

Booth, T.E.; Amster, H.J.

1978-01-01

Present theories for predicting expected Monte Carlo errors in neutron transport calculations apply to estimates of flux-weighted integrals sampled directly by scoring individual collisions. To treat track-length estimators, the recent theory of Amster and Djomehri is generalized to allow the score distribution functions to depend on the coordinates of two successive collisions. It has long been known that the expected track length in a region of phase space equals the expected flux integrated over that region, but that the expected statistical error of the Monte Carlo estimate of the track length is different from that of the flux integral obtained by sampling the sum of the reciprocals of the cross sections for all collisions in the region. These conclusions are shown to be implied by the generalized theory, which provides explicit equations for the expected values and errors of both types of estimators. Sampling expected contributions to the track-length estimator is also treated. Other general properties of the errors for both estimators are derived from the equations and physically interpreted. The actual values of these errors are then obtained and interpreted for a simple specific example

Detecting and correcting partial errors: Evidence for efficient control without conscious access.

Science.gov (United States)

Rochet, N; Spieser, L; Casini, L; Hasbroucq, T; Burle, B

2014-09-01

Appropriate reactions to erroneous actions are essential to keeping behavior adaptive. Erring, however, is not an all-or-none process: electromyographic (EMG) recordings of the responding muscles have revealed that covert incorrect response activations (termed "partial errors") occur on a proportion of overtly correct trials. The occurrence of such "partial errors" shows that incorrect response activations could be corrected online, before turning into overt errors. In the present study, we showed that, unlike overt errors, such "partial errors" are poorly consciously detected by participants, who could report only one third of their partial errors. Two parameters of the partial errors were found to predict detection: the surface of the incorrect EMG burst (larger for detected) and the correction time (between the incorrect and correct EMG onsets; longer for detected). These two parameters provided independent information. The correct(ive) responses associated with detected partial errors were larger than the "pure-correct" ones, and this increase was likely a consequence, rather than a cause, of the detection. The respective impacts of the two parameters predicting detection (incorrect surface and correction time), along with the underlying physiological processes subtending partial-error detection, are discussed.

The cerebellum does more than sensory prediction error-based learning in sensorimotor adaptation tasks.

Science.gov (United States)

Butcher, Peter A; Ivry, Richard B; Kuo, Sheng-Han; Rydz, David; Krakauer, John W; Taylor, Jordan A

2017-09-01

Individuals with damage to the cerebellum perform poorly in sensorimotor adaptation paradigms. This deficit has been attributed to impairment in sensory prediction error-based updating of an internal forward model, a form of implicit learning. These individuals can, however, successfully counter a perturbation when instructed with an explicit aiming strategy. This successful use of an instructed aiming strategy presents a paradox: In adaptation tasks, why do individuals with cerebellar damage not come up with an aiming solution on their own to compensate for their implicit learning deficit? To explore this question, we employed a variant of a visuomotor rotation task in which, before executing a movement on each trial, the participants verbally reported their intended aiming location. Compared with healthy control participants, participants with spinocerebellar ataxia displayed impairments in both implicit learning and aiming. This was observed when the visuomotor rotation was introduced abruptly ( experiment 1 ) or gradually ( experiment 2 ). This dual deficit does not appear to be related to the increased movement variance associated with ataxia: Healthy undergraduates showed little change in implicit learning or aiming when their movement feedback was artificially manipulated to produce similar levels of variability ( experiment 3 ). Taken together the results indicate that a consequence of cerebellar dysfunction is not only impaired sensory prediction error-based learning but also a difficulty in developing and/or maintaining an aiming solution in response to a visuomotor perturbation. We suggest that this dual deficit can be explained by the cerebellum forming part of a network that learns and maintains action-outcome associations across trials. NEW & NOTEWORTHY Individuals with cerebellar pathology are impaired in sensorimotor adaptation. This deficit has been attributed to an impairment in error-based learning, specifically, from a deficit in using sensory

EFFECT OF MEASUREMENT ERRORS ON PREDICTED COSMOLOGICAL CONSTRAINTS FROM SHEAR PEAK STATISTICS WITH LARGE SYNOPTIC SURVEY TELESCOPE

Energy Technology Data Exchange (ETDEWEB)

Bard, D.; Chang, C.; Kahn, S. M.; Gilmore, K.; Marshall, S. [KIPAC, Stanford University, 452 Lomita Mall, Stanford, CA 94309 (United States); Kratochvil, J. M.; Huffenberger, K. M. [Department of Physics, University of Miami, Coral Gables, FL 33124 (United States); May, M. [Physics Department, Brookhaven National Laboratory, Upton, NY 11973 (United States); AlSayyad, Y.; Connolly, A.; Gibson, R. R.; Jones, L.; Krughoff, S. [Department of Astronomy, University of Washington, Seattle, WA 98195 (United States); Ahmad, Z.; Bankert, J.; Grace, E.; Hannel, M.; Lorenz, S. [Department of Physics, Purdue University, West Lafayette, IN 47907 (United States); Haiman, Z.; Jernigan, J. G., E-mail: djbard@slac.stanford.edu [Department of Astronomy and Astrophysics, Columbia University, New York, NY 10027 (United States); and others

2013-09-01

We study the effect of galaxy shape measurement errors on predicted cosmological constraints from the statistics of shear peak counts with the Large Synoptic Survey Telescope (LSST). We use the LSST Image Simulator in combination with cosmological N-body simulations to model realistic shear maps for different cosmological models. We include both galaxy shape noise and, for the first time, measurement errors on galaxy shapes. We find that the measurement errors considered have relatively little impact on the constraining power of shear peak counts for LSST.

Breaking the polar-nonpolar division in solvation free energy prediction.

Science.gov (United States)

Wang, Bao; Wang, Chengzhang; Wu, Kedi; Wei, Guo-Wei

2018-02-05

Implicit solvent models divide solvation free energies into polar and nonpolar additive contributions, whereas polar and nonpolar interactions are inseparable and nonadditive. We present a feature functional theory (FFT) framework to break this ad hoc division. The essential ideas of FFT are as follows: (i) representability assumption: there exists a microscopic feature vector that can uniquely characterize and distinguish one molecule from another; (ii) feature-function relationship assumption: the macroscopic features, including solvation free energy, of a molecule is a functional of microscopic feature vectors; and (iii) similarity assumption: molecules with similar microscopic features have similar macroscopic properties, such as solvation free energies. Based on these assumptions, solvation free energy prediction is carried out in the following protocol. First, we construct a molecular microscopic feature vector that is efficient in characterizing the solvation process using quantum mechanics and Poisson-Boltzmann theory. Microscopic feature vectors are combined with macroscopic features, that is, physical observable, to form extended feature vectors. Additionally, we partition a solvation dataset into queries according to molecular compositions. Moreover, for each target molecule, we adopt a machine learning algorithm for its nearest neighbor search, based on the selected microscopic feature vectors. Finally, from the extended feature vectors of obtained nearest neighbors, we construct a functional of solvation free energy, which is employed to predict the solvation free energy of the target molecule. The proposed FFT model has been extensively validated via a large dataset of 668 molecules. The leave-one-out test gives an optimal root-mean-square error (RMSE) of 1.05 kcal/mol. FFT predictions of SAMPL0, SAMPL1, SAMPL2, SAMPL3, and SAMPL4 challenge sets deliver the RMSEs of 0.61, 1.86, 1.64, 0.86, and 1.14 kcal/mol, respectively. Using a test set of 94

Nonparametric predictive inference for reliability of a k-out-of-m:G system with multiple component types

International Nuclear Information System (INIS)

Aboalkhair, Ahmad M.; Coolen, Frank P.A.; MacPhee, Iain M.

2014-01-01

Nonparametric predictive inference for system reliability has recently been presented, with specific focus on k-out-of-m:G systems. The reliability of systems is quantified by lower and upper probabilities of system functioning, given binary test results on components, taking uncertainty about component functioning and indeterminacy due to limited test information explicitly into account. Thus far, systems considered were series configurations of subsystems, with each subsystem i a k i -out-of-m i :G system which consisted of only one type of components. Key results are briefly summarized in this paper, and as an important generalization new results are presented for a single k-out-of-m:G system consisting of components of multiple types. The important aspects of redundancy and diversity for such systems are discussed. - Highlights: • New results on nonparametric predictive inference for system reliability. • Prediction of system reliability based on test data for components. • New insights on system redundancy optimization and diversity. • Components that appear inferior in tests may be included to enhance redundancy

Quality prediction modeling for sintered ores based on mechanism models of sintering and extreme learning machine based error compensation

Science.gov (United States)

Tiebin, Wu; Yunlian, Liu; Xinjun, Li; Yi, Yu; Bin, Zhang

2018-06-01

Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.

Generalized Gaussian Error Calculus

CERN Document Server

Grabe, Michael

2010-01-01

For the first time in 200 years Generalized Gaussian Error Calculus addresses a rigorous, complete and self-consistent revision of the Gaussian error calculus. Since experimentalists realized that measurements in general are burdened by unknown systematic errors, the classical, widespread used evaluation procedures scrutinizing the consequences of random errors alone turned out to be obsolete. As a matter of course, the error calculus to-be, treating random and unknown systematic errors side by side, should ensure the consistency and traceability of physical units, physical constants and physical quantities at large. The generalized Gaussian error calculus considers unknown systematic errors to spawn biased estimators. Beyond, random errors are asked to conform to the idea of what the author calls well-defined measuring conditions. The approach features the properties of a building kit: any overall uncertainty turns out to be the sum of a contribution due to random errors, to be taken from a confidence inter...

Negative leave balances

CERN Multimedia

Human Resources Department

2005-01-01

Members of the personnel entitled to annual leave and, where appropriate, saved leave and/or compensatory leave are requested to take note of the new arrangements described below, which were recommended by the Standing Concertation Committee (SCC) at its meeting on 1Â September 2005 and subsequently approved by the Director-General. The changes do not apply to members of the personnel participating in the Progressive Retirement Programme (PRP) or the Part-time Work as a pre-retirement measure, for whom the specific provisions communicated at the time of joining will continue to apply. Â Negative balances in annual leave, saved leave and/or compensatory leave accounts at the end of the leave year (30th September) and on the date on which bonuses are credited to the saved leave account (31st December): Where members of the personnel have a leave account with a negative balance on 30Â September and/or 31Â December, leave will automatically be transferred from one account to another on the relevant dates i...

Negative leave balances

CERN Multimedia

Human Resources Department

2005-01-01

Members of the personnel entitled to annual leave and, where appropriate, saved leave and/or compensatory leave are requested to take note of the new arrangements described below, which were recommended by the Standing Concertation Committee (SCC) at its meeting on 1 September 2005 and subsequently approved by the Director-General. The changes do not apply to members of the personnel participating in the Progressive Retirement Programme (PRP) or the Part-time Work as a pre-retirement measure, for whom the specific provisions communicated at the time of joining will continue to apply.  Negative balances in annual leave, saved leave and/or compensatory leave accounts at the end of the leave year (30th September) and on the date on which bonuses are credited to the saved leave account (31st December): Where members of the personnel have a leave account with a negative balance on 30 September and/or 31 December, leave will automatically be transferred from one account to another on the relevant dates in or...

Toward a better understanding on the role of prediction error on memory processes: From bench to clinic.

Science.gov (United States)

Krawczyk, María C; Fernández, Rodrigo S; Pedreira, María E; Boccia, Mariano M

2017-07-01

Experimental psychology defines Prediction Error (PE) as a mismatch between expected and current events. It represents a unifier concept within the memory field, as it is the driving force of memory acquisition and updating. Prediction error induces updating of consolidated memories in strength or content by memory reconsolidation. This process has two different neurobiological phases, which involves the destabilization (labilization) of a consolidated memory followed by its restabilization. The aim of this work is to emphasize the functional role of PE on the neurobiology of learning and memory, integrating and discussing different research areas: behavioral, neurobiological, computational and clinical psychiatry. Copyright © 2016 Elsevier Inc. All rights reserved.

Magnetic field errors tolerances of Nuclotron booster

Science.gov (United States)

Butenko, Andrey; Kazinova, Olha; Kostromin, Sergey; Mikhaylov, Vladimir; Tuzikov, Alexey; Khodzhibagiyan, Hamlet

2018-04-01

Generation of magnetic field in units of booster synchrotron for the NICA project is one of the most important conditions for getting the required parameters and qualitative accelerator operation. Research of linear and nonlinear dynamics of ion beam 197Au31+ in the booster have carried out with MADX program. Analytical estimation of magnetic field errors tolerance and numerical computation of dynamic aperture of booster DFO-magnetic lattice are presented. Closed orbit distortion with random errors of magnetic fields and errors in layout of booster units was evaluated.

Enhanced outage prediction modeling for strong extratropical storms and hurricanes in the Northeastern United States

Science.gov (United States)

Cerrai, D.; Anagnostou, E. N.; Wanik, D. W.; Bhuiyan, M. A. E.; Zhang, X.; Yang, J.; Astitha, M.; Frediani, M. E.; Schwartz, C. S.; Pardakhti, M.

2016-12-01

The overwhelming majority of human activities need reliable electric power. Severe weather events can cause power outages, resulting in substantial economic losses and a temporary worsening of living conditions. Accurate prediction of these events and the communication of forecasted impacts to the affected utilities is necessary for efficient emergency preparedness and mitigation. The University of Connecticut Outage Prediction Model (OPM) uses regression tree models, high-resolution weather reanalysis and real-time weather forecasts (WRF and NCAR ensemble), airport station data, vegetation and electric grid characteristics and historical outage data to forecast the number and spatial distribution of outages in the power distribution grid located within dense vegetation. Recent OPM improvements consist of improved storm classification and addition of new predictive weather-related variables and are demonstrated using a leave-one-storm-out cross-validation based on 130 severe extratropical storms and two hurricanes (Sandy and Irene) in the Northeast US. We show that it is possible to predict the number of trouble spots causing outages in the electric grid with a median absolute percentage error as low as 27% for some storm types, and at most around 40%, in a scale that varies between four orders of magnitude, from few outages to tens of thousands. This outage information can be communicated to the electric utility to manage allocation of crews and equipment and minimize the recovery time for an upcoming storm hazard.

Difference in Perseverative Errors during a Visual Attention Task with Auditory Distractors in Alpha-9 Nicotinic Receptor Subunit Wild Type and Knock-Out Mice.

Science.gov (United States)

Jorratt, Pascal; Delano, Paul H; Delgado, Carolina; Dagnino-Subiabre, Alexies; Terreros, Gonzalo

2017-01-01

The auditory efferent system is a neural network that originates in the auditory cortex and projects to the cochlear receptor through olivocochlear (OC) neurons. Medial OC neurons make cholinergic synapses with outer hair cells (OHCs) through nicotinic receptors constituted by α9 and α10 subunits. One of the physiological functions of the α9 nicotinic receptor subunit (α9-nAChR) is the suppression of auditory distractors during selective attention to visual stimuli. In a recent study we demonstrated that the behavioral performance of alpha-9 nicotinic receptor knock-out (KO) mice is altered during selective attention to visual stimuli with auditory distractors since they made less correct responses and more omissions than wild type (WT) mice. As the inhibition of the behavioral responses to irrelevant stimuli is an important mechanism of the selective attention processes, behavioral errors are relevant measures that can reflect altered inhibitory control. Errors produced during a cued attention task can be classified as premature, target and perseverative errors. Perseverative responses can be considered as an inability to inhibit the repetition of an action already planned, while premature responses can be considered as an index of the ability to wait or retain an action. Here, we studied premature, target and perseverative errors during a visual attention task with auditory distractors in WT and KO mice. We found that α9-KO mice make fewer perseverative errors with longer latencies than WT mice in the presence of auditory distractors. In addition, although we found no significant difference in the number of target error between genotypes, KO mice made more short-latency target errors than WT mice during the presentation of auditory distractors. The fewer perseverative error made by α9-KO mice could be explained by a reduced motivation for reward and an increased impulsivity during decision making with auditory distraction in KO mice.

A novel registration-based methodology for prediction of trabecular bone fabric from clinical QCT: A comprehensive analysis.

Directory of Open Access Journals (Sweden)

Vimal Chandran

Full Text Available Osteoporosis leads to hip fractures in aging populations and is diagnosed by modern medical imaging techniques such as quantitative computed tomography (QCT. Hip fracture sites involve trabecular bone, whose strength is determined by volume fraction and orientation, known as fabric. However, bone fabric cannot be reliably assessed in clinical QCT images of proximal femur. Accordingly, we propose a novel registration-based estimation of bone fabric designed to preserve tensor properties of bone fabric and to map bone fabric by a global and local decomposition of the gradient of a non-rigid image registration transformation. Furthermore, no comprehensive analysis on the critical components of this methodology has been previously conducted. Hence, the aim of this work was to identify the best registration-based strategy to assign bone fabric to the QCT image of a patient's proximal femur. The normalized correlation coefficient and curvature-based regularization were used for image-based registration and the Frobenius norm of the stretch tensor of the local gradient was selected to quantify the distance among the proximal femora in the population. Based on this distance, closest, farthest and mean femora with a distinction of sex were chosen as alternative atlases to evaluate their influence on bone fabric prediction. Second, we analyzed different tensor mapping schemes for bone fabric prediction: identity, rotation-only, rotation and stretch tensor. Third, we investigated the use of a population average fabric atlas. A leave one out (LOO evaluation study was performed with a dual QCT and HR-pQCT database of 36 pairs of human femora. The quality of the fabric prediction was assessed with three metrics, the tensor norm (TN error, the degree of anisotropy (DA error and the angular deviation of the principal tensor direction (PTD. The closest femur atlas (CTP with a full rotation (CR for fabric mapping delivered the best results with a TN error of 7

Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features

Energy Technology Data Exchange (ETDEWEB)

Grimm, Lars J., E-mail: Lars.grimm@duke.edu; Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie [Department of Radiology, Duke University Medical Center, Box 3808, Durham, North Carolina 27710 (United States); Kuzmiak, Cherie M. [Department of Radiology, University of North Carolina School of Medicine, 2006 Old Clinic, CB No. 7510, Chapel Hill, North Carolina 27599 (United States); Mazurowski, Maciej A. [Duke University Medical Center, Box 2731 Medical Center, Durham, North Carolina 27710 (United States)

2014-03-15

Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features.

Science.gov (United States)

Grimm, Lars J; Ghate, Sujata V; Yoon, Sora C; Kuzmiak, Cherie M; Kim, Connie; Mazurowski, Maciej A

2014-03-01

The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502-0.739, 95% Confidence Interval: 0.543-0.680,p errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features

International Nuclear Information System (INIS)

Grimm, Lars J.; Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie; Kuzmiak, Cherie M.; Mazurowski, Maciej A.

2014-01-01

Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees

Changes in multidimensional pain inventory profile after a pain rehabilitation programme indicate the risk of receiving sick leave benefits one year later

DEFF Research Database (Denmark)

Nyberg, Vanja E; Novo, Mehmed; Sjölund, Bengt H.

2014-01-01

,784 patients (709 men and 2,075 women) collected from the Swedish Quality Register for Pain Rehabilitation (SQRP) before and at the end of rehabilitation and compared with independent sick leave data for 1 year later. RESULTS: After rehabilitation there was a significantly decreased share of Dysfunctional...... profiles (DYS) among both men (44% before, 31% after) and women (39% before, 26% after), but an increased share of Adaptive Coper profiles (men 15% before, 24% after, women 14% before, 24% after). The number of patients on full-time sick leave decreased significantly among men (from 57% to 46%) and women......OBJECTIVES: To determine whether coping profile changes after rehabilitation, assessed with the Multidimensional Pain Inventory (MPI), can predict which persons disabled by chronic musculoskeletal pain will be in receipt of sick leave benefits in the long term. METHODS: Study of MPI data from 2...

Derivation of the extended elastic stiffness formula of the holddown spring assembly comprised of several leaves

International Nuclear Information System (INIS)

Song, Kee Nam; Kang, H. S.; Yoon, K. H.

1999-01-01

Based on the Euler beam theory and the elastic strain energy method, the elastic stiffness formula of the holddown spring assembly consisting of several leaves was previously derived. Even though the previous formula was known to be useful to estimate the elastic stiffness of the holddown spring assembly, recently it was reported that the elastic stiffness from the previous formula deviated greatly from the test results as the number of leaves was increased. The objective of this study is to extend the previous formula in order to resolve such an increasing deviation when increasing the number of leaves. Additionally, considering the friction forces acting on the interfaces between the leaves, we obtained an extended elastic stiffness formula. The characteristic test and the elastic stiffness analysis on the various kinds of specimens of the holddown spring assembly have been carried out; the validity of the extended formula has been verified by the comparison of their results. As a result of comparisons, it is found that the extended formula is able to evaluate the elastic stiffness of the holddown spring assembly within the maximum error range of +12%, irrespective of the number of the leaves. (author). 9 refs., 5 figs., 1 tab

Protein-protein interaction site predictions with minimum covariance determinant and Mahalanobis distance.

Science.gov (United States)

Qiu, Zhijun; Zhou, Bo; Yuan, Jiangfeng

2017-11-21

Protein-protein interaction site (PPIS) prediction must deal with the diversity of interaction sites that limits their prediction accuracy. Use of proteins with unknown or unidentified interactions can also lead to missing interfaces. Such data errors are often brought into the training dataset. In response to these two problems, we used the minimum covariance determinant (MCD) method to refine the training data to build a predictor with better performance, utilizing its ability of removing outliers. In order to predict test data in practice, a method based on Mahalanobis distance was devised to select proper test data as input for the predictor. With leave-one-validation and independent test, after the Mahalanobis distance screening, our method achieved higher performance according to Matthews correlation coefficient (MCC), although only a part of test data could be predicted. These results indicate that data refinement is an efficient approach to improve protein-protein interaction site prediction. By further optimizing our method, it is hopeful to develop predictors of better performance and wide range of application. Copyright © 2017 Elsevier Ltd. All rights reserved.

Paid Maternity Leave in the United States: Associations with Maternal and Infant Health.

Science.gov (United States)

Jou, Judy; Kozhimannil, Katy B; Abraham, Jean M; Blewett, Lynn A; McGovern, Patricia M

2018-02-01

Objectives The United States is one of only three countries worldwide with no national policy guaranteeing paid leave to employed women who give birth. While maternity leave has been linked to improved maternal and child outcomes in international contexts, up-to-date research evidence in the U.S. context is needed to inform current policy debates on paid family leave. Methods Using data from Listening to Mothers III, a national survey of women ages 18-45 who gave birth in 2011-2012, we conducted multivariate logistic regression to predict the likelihood of outcomes related to infant health, maternal physical and mental health, and maternal health behaviors by the use and duration of paid maternity leave. Results Use of paid and unpaid leave varied significantly by race/ethnicity and household income. Women who took paid maternity leave experienced a 47% decrease in the odds of re-hospitalizing their infants (95% CI 0.3, 1.0) and a 51% decrease in the odds of being re-hospitalized themselves (95% CI 0.3, 0.9) at 21 months postpartum, compared to women taking unpaid or no leave. They also had 1.8 times the odds of doing well with exercise (95% CI 1.1, 3.0) and stress management (95% CI 1.1, 2.8), compared to women taking only unpaid leave. Conclusions for Practice Paid maternity leave significantly predicts lower odds of maternal and infant re-hospitalization and higher odds of doing well with exercise and stress management. Policies aimed at expanding access to paid maternity and family leave may contribute toward reducing socio-demographic disparities in paid leave use and its associated health benefits.

Processing of action- but not stimulus-related prediction errors differs between active and observational feedback learning.

Science.gov (United States)

Kobza, Stefan; Bellebaum, Christian

2015-01-01

Learning of stimulus-response-outcome associations is driven by outcome prediction errors (PEs). Previous studies have shown larger PE-dependent activity in the striatum for learning from own as compared to observed actions and the following outcomes despite comparable learning rates. We hypothesised that this finding relates primarily to a stronger integration of action and outcome information in active learners. Using functional magnetic resonance imaging, we investigated brain activations related to action-dependent PEs, reflecting the deviation between action values and obtained outcomes, and action-independent PEs, reflecting the deviation between subjective values of response-preceding cues and obtained outcomes. To this end, 16 active and 15 observational learners engaged in a probabilistic learning card-guessing paradigm. On each trial, active learners saw one out of five cues and pressed either a left or right response button to receive feedback (monetary win or loss). Each observational learner observed exactly those cues, responses and outcomes of one active learner. Learning performance was assessed in active test trials without feedback and did not differ between groups. For both types of PEs, activations were found in the globus pallidus, putamen, cerebellum, and insula in active learners. However, only for action-dependent PEs, activations in these structures and the anterior cingulate were increased in active relative to observational learners. Thus, PE-related activity in the reward system is not generally enhanced in active relative to observational learning but only for action-dependent PEs. For the cerebellum, additional activations were found across groups for cue-related uncertainty, thereby emphasising the cerebellum's role in stimulus-outcome learning. Copyright © 2014 Elsevier Ltd. All rights reserved.

On superactivation of one-shot quantum zero-error capacity and the related property of quantum measurements

DEFF Research Database (Denmark)

Shirokov, M. E.; Shulman, Tatiana

2014-01-01

We give a detailed description of a low-dimensional quantum channel (input dimension 4, Choi rank 3) demonstrating the symmetric form of superactivation of one-shot quantum zero-error capacity. This property means appearance of a noiseless (perfectly reversible) subchannel in the tensor square...... of a channel having no noiseless subchannels. Then we describe a quantum channel with an arbitrary given level of symmetric superactivation (including the infinite value). We also show that superactivation of one-shot quantum zero-error capacity of a channel can be reformulated in terms of quantum measurement...

Non-invasive prediction of hematocrit levels by portable visible and near-infrared spectrophotometer.

Science.gov (United States)

Sakudo, Akikazu; Kato, Yukiko Hakariya; Kuratsune, Hirohiko; Ikuta, Kazuyoshi

2009-10-01

After blood donation, in some individuals having polycythemia, dehydration causes anemia. Although the hematocrit (Ht) level is closely related to anemia, the current method of measuring Ht is performed after blood drawing. Furthermore, the monitoring of Ht levels contributes to a healthy life. Therefore, a non-invasive test for Ht is warranted for the safe donation of blood and good quality of life. A non-invasive procedure for the prediction of hematocrit levels was developed on the basis of a chemometric analysis of visible and near-infrared (Vis-NIR) spectra of the thumbs using portable spectrophotometer. Transmittance spectra in the 600- to 1100-nm region from thumbs of Japanese volunteers were subjected to a partial least squares regression (PLSR) analysis and leave-out cross-validation to develop chemometric models for predicting Ht levels. Ht levels of masked samples predicted by this model from Vis-NIR spectra provided a coefficient of determination in prediction of 0.6349 with a standard error of prediction of 3.704% and a detection limit in prediction of 17.14%, indicating that the model is applicable for normal and abnormal value in Ht level. These results suggest portable Vis-NIR spectrophotometer to have potential for the non-invasive measurement of Ht levels with a combination of PLSR analysis.

Operator errors

International Nuclear Information System (INIS)

Knuefer; Lindauer

1980-01-01

Besides that at spectacular events a combination of component failure and human error is often found. Especially the Rasmussen-Report and the German Risk Assessment Study show for pressurised water reactors that human error must not be underestimated. Although operator errors as a form of human error can never be eliminated entirely, they can be minimized and their effects kept within acceptable limits if a thorough training of personnel is combined with an adequate design of the plant against accidents. Contrary to the investigation of engineering errors, the investigation of human errors has so far been carried out with relatively small budgets. Intensified investigations in this field appear to be a worthwhile effort. (orig.)

Applying the Innov8 approach for reviewing national health programmes to leave no one behind: lessons learnt from Indonesia

Science.gov (United States)

Saint, Victoria; Floranita, Rustini; Koemara Sakti, Gita Maya; Pambudi, Imran; Hermawan, Lukas; Villar, Eugenio; Magar, Veronica

2018-01-01

ABSTRACT The World Health Organization’s Innov8 Approach for Reviewing National Health Programmes to Leave No One Behind is an eight-step process that supports the operationalization of the Sustainable Development Goals’ commitment to ‘leave no one behind’. In 2014–2015, Innov8 was adapted and applied in Indonesia to review how the national neonatal and maternal health action plans could become more equity-oriented, rights-based and gender-responsive, and better address critical social determinants of health. The process was led by the Indonesian Ministry of Health, with the support of WHO. It involved a wide range of actors and aligned with/fed into the drafting of the maternal newborn health action plan and the implementation planning of the newborn action plan. Key activities included a sensitization meeting, diagnostic checklist, review workshop and in-country work by the review teams. This ‘methods forum’ article describes this adaptation and application process, the outcomes and lessons learnt. In conjunction with other sources, Innov8 findings and recommendations informed national and sub-national maternal and neonatal action plans and programming to strengthen a ‘leave no one behind’ approach. As follow-up during 2015–2017, components of the Innov8 methodology were integrated into district-level planning processes for maternal and newborn health, and Innov8 helped generate demand for health inequality monitoring and its use in planning. In Indonesia, Innov8 enhanced national capacity for equity-oriented, rights-based and gender-responsive approaches and addressing critical social determinants of health. Adaptation for the national planning context (e.g. decentralized structure) and linking with health inequality monitoring capacity building were important lessons learnt. The pilot of Innov8 in Indonesia suggests that this approach can help operationalize the SDGs’ commitment to leave no one behind, in particular in relation to

Conformation radiotherapy with eccentric multi-leaves, (1)

International Nuclear Information System (INIS)

Obata, Yasunori; Sakuma, Sadayuki.

1986-01-01

In order to extend the application of the conformation radiotherapy, the eccentric multi-leaves are equipped with the linear accelerator. The information of the position of the collimators and the dose distribution of the eccentric conformation radiotherapy are calculated by the improved algorism of the treatment planning system. In simple cases, the dose distributions for the distant region from the rotational center are measured and compared with the calculated values. Both distributions are well coincided with the error of about 5 % in the high dose region and 10 % in the low dose region. In eccentric conformation radiotherapy, it is difficult to deliver the planned dose to the lesion. The dose increases with the distance of the target area from the rotational center. And the measured value and the calculated value are well coincided with 1 % error. So after getting the dose ratio of the rotational center to the target area, the calculated dose can be delivered to the rotational center. The advantages of the eccentric conformation radiotherapy are a good coincidence of target area and treated area, a partial shielding and a hollow out technique without absorber. The limitation of the movement of the collimator from center is 5 cm at 1 m SCD. (author)

Predictive error dependencies when using pilot points and singular value decomposition in groundwater model calibration

DEFF Research Database (Denmark)

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...

A method for predicting errors when interacting with finite state systems. How implicit learning shapes the user's knowledge of a system

International Nuclear Information System (INIS)

Javaux, Denis

2002-01-01

This paper describes a method for predicting the errors that may appear when human operators or users interact with systems behaving as finite state systems. The method is a generalization of a method used for predicting errors when interacting with autopilot modes on modern, highly computerized airliners [Proc 17th Digital Avionics Sys Conf (DASC) (1998); Proc 10th Int Symp Aviat Psychol (1999)]. A cognitive model based on spreading activation networks is used for predicting the user's model of the system and its impact on the production of errors. The model strongly posits the importance of implicit learning in user-system interaction and its possible detrimental influence on users' knowledge of the system. An experiment conducted with Airbus Industrie and a major European airline on pilots' knowledge of autopilot behavior on the A340-200/300 confirms the model predictions, and in particular the impact of the frequencies with which specific state transitions and contexts are experienced

Predicting the outcomes of performance error indicators on accreditation status in the nuclear power industry

International Nuclear Information System (INIS)

Wilson, P.A.

1986-01-01

The null hypothesis for this study suggested that there was no significant difference in the types of performance error indicators between accredited and non-accredited programs on the following types of indicators: (1) number of significant event reports per unit, (2) number of forced outages per unit, (3) number of unplanned automatic scrams per unit, and (4) amount of equivalent availability per unit. A sample of 90 nuclear power plants was selected for this study. Data were summarized from two data bases maintained by the Institute of Nuclear Power Operations. Results of this study did not support the research hypothesis. There was no significant difference between the accredited and non-accredited programs on any of the four performance error indicators. The primary conclusions of this include the following: (1) The four selected performance error indicators cannot be used individually or collectively to predict accreditation status in the nuclear power industry. (2) Annual performance error indicator ratings cannot be used to determine the effects of performance-based training on plant performance. (3) The four selected performance error indicators cannot be used to measure the effect of operator job performance on plant effectiveness

The prevalence of sick leave

DEFF Research Database (Denmark)

Backhausen, Mette; Damm, Peter; Bendix, Jane

2018-01-01

of long-term sick leave. Method Data from 508 employed pregnant women seeking antenatal care was collected by questionnaires from August 2015 to March 2016. The questionnaires, which were filled in at 20 and 32 weeks of gestation, provided information on maternal characteristics, the number of days spent...... on sick leave and the associated reasons. Descriptive statistics and logistic regression analysis were applied. Results The prevalence of sick leave was 56% of employed pregnant women in the first 32 weeks of gestation and more than one in four reported long-term sick leave (>20 days, continuous...... was a negative predictor. Conclusions The prevalence of sick leave was 56% in the first 32 weeks of gestation and more than one in four women reported long-term sick leave. The majority of reasons for sick leave were pregnancy-related and low back pain was the most frequently given reason....

Predicting Heats of Explosion of Nitroaromatic Compounds through NBO Charges and 15N NMR Chemical Shifts of Nitro Groups

Directory of Open Access Journals (Sweden)

Ricardo Infante-Castillo

2012-01-01

Full Text Available This work presents a new quantitative model to predict the heat of explosion of nitroaromatic compounds using the natural bond orbital (NBO charge and 15N NMR chemical shifts of the nitro groups (15NNitro as structural parameters. The values of the heat of explosion predicted for 21 nitroaromatic compounds using the model described here were compared with experimental data. The prediction ability of the model was assessed by the leave-one-out cross-validation method. The cross-validation results show that the model is significant and stable and that the predicted accuracy is within 0.146 MJ kg−1, with an overall root mean squared error of prediction (RMSEP below 0.183 MJ kg−1. Strong correlations were observed between the heat of explosion and the charges (R2 = 0.9533 and 15N NMR chemical shifts (R2 = 0.9531 of the studied compounds. In addition, the dependence of the heat of explosion on the presence of activating or deactivating groups of nitroaromatic explosives was analyzed. All calculations, including optimizations, NBO charges, and 15NNitro NMR chemical shifts analyses, were performed using density functional theory (DFT and a 6-311+G(2d,p basis set. Based on these results, this practical quantitative model can be used as a tool in the design and development of highly energetic materials (HEM based on nitroaromatic compounds.

Dorsal Anterior Cingulate Cortices Differentially Lateralize Prediction Errors and Outcome Valence in a Decision-Making Task

Directory of Open Access Journals (Sweden)

Alexander R. Weiss

2018-05-01

Full Text Available The dorsal anterior cingulate cortex (dACC is proposed to facilitate learning by signaling mismatches between the expected outcome of decisions and the actual outcomes in the form of prediction errors. The dACC is also proposed to discriminate outcome valence—whether a result has positive (either expected or desirable or negative (either unexpected or undesirable value. However, direct electrophysiological recordings from human dACC to validate these separate, but integrated, dimensions have not been previously performed. We hypothesized that local field potentials (LFPs would reveal changes in the dACC related to prediction error and valence and used the unique opportunity offered by deep brain stimulation (DBS surgery in the dACC of three human subjects to test this hypothesis. We used a cognitive task that involved the presentation of object pairs, a motor response, and audiovisual feedback to guide future object selection choices. The dACC displayed distinctly lateralized theta frequency (3–8 Hz event-related potential responses—the left hemisphere dACC signaled outcome valence and prediction errors while the right hemisphere dACC was involved in prediction formation. Multivariate analyses provided evidence that the human dACC response to decision outcomes reflects two spatiotemporally distinct early and late systems that are consistent with both our lateralized electrophysiological results and the involvement of the theta frequency oscillatory activity in dACC cognitive processing. Further findings suggested that dACC does not respond to other phases of action-outcome-feedback tasks such as the motor response which supports the notion that dACC primarily signals information that is crucial for behavioral monitoring and not for motor control.

Tackling Complex Inequalities and Ecuador's Buen Vivir: Leaving No-one Behind and equality in diversity

OpenAIRE

Radcliffe, Sarah Anne

2017-01-01

Ecuador's postneoliberal policy of Buen Vivir seeks to reduce social inequality and tackle complex disadvantages associated with gender, location, race-ethnicity and other social differences. The paper analyses governmental Buen Vivir policy thinking and institutional arrangements to explore how Buen Vivir frameworks approach the constitutional commitment to equality in diversity, in light of the global Sustainable Development Goal of "Leaving No-one Behind" (LNOB). In many respects Ecuador ...

Personality predicts drop-out from therapist-guided internet-based cognitive behavioural therapy for eating disorders. Results from a randomized controlled trial

Directory of Open Access Journals (Sweden)

Louise Högdahl

2016-09-01

Full Text Available Internet-based guided self-help cognitive behavioural therapy (ICBT seems a promising way of delivering eating disorder treatment. However, treatment drop-out is a common problem and little is known about the correlates, especially in clinical settings. The study aimed to explore prediction of drop-out in the context of a randomized controlled trial within specialized eating disorder care in terms of eating disorder symptomatology, personality traits, comorbidity, and demographic characteristics. 109 outpatients diagnosed with bulimia nervosa or similar eating disorder were randomized to two types of ICBT. Participants were assessed with several clinical- and self-ratings. The average drop-out rate was 36%. Drop-out was predicted by lower scores in the personality traits Dutifulness and Assertiveness as measured by the NEO Personality Inventory Revised, and by higher scores in Self-affirm as measured by the Structural Analysis of Social Behaviour. Drop-out was also predicted by therapist factors: one therapist had significantly more drop-outs (82% than the other three (M = 30%. Theoretical and clinical implications of the impact of the predictors are discussed.

The application of SHERPA (Systematic Human Error Reduction and Prediction Approach) in the development of compensatory cognitive rehabilitation strategies for stroke patients with left and right brain damage.

Science.gov (United States)

Hughes, Charmayne M L; Baber, Chris; Bienkiewicz, Marta; Worthington, Andrew; Hazell, Alexa; Hermsdörfer, Joachim

2015-01-01

Approximately 33% of stroke patients have difficulty performing activities of daily living, often committing errors during the planning and execution of such activities. The objective of this study was to evaluate the ability of the human error identification (HEI) technique SHERPA (Systematic Human Error Reduction and Prediction Approach) to predict errors during the performance of daily activities in stroke patients with left and right hemisphere lesions. Using SHERPA we successfully predicted 36 of the 38 observed errors, with analysis indicating that the proportion of predicted and observed errors was similar for all sub-tasks and severity levels. HEI results were used to develop compensatory cognitive strategies that clinicians could employ to reduce or prevent errors from occurring. This study provides evidence for the reliability and validity of SHERPA in the design of cognitive rehabilitation strategies in stroke populations.

Sick Leave within 5 Years of Whiplash Trauma Predicts Recovery: A Prospective Cohort and Register-Based Study

Science.gov (United States)

Carstensen, Tina Birgitte Wisbech; Fink, Per; Oernboel, Eva; Kasch, Helge; Jensen, Troels Staehelin; Frostholm, Lisbeth

2015-01-01

Background 10–22% of individuals sustaining whiplash trauma develop persistent symptoms resulting in reduced working ability and decreased quality of life, but it is poorly understood why some people do not recover. Various collision and post-collision risk factors have been studied, but little is known about pre-collision risk factors. In particular, the impact of sickness and socioeconomic factors before the collision on recovery is sparsely explored. The aim of this study was to examine if welfare payments received within five years pre-collision predict neck pain and negative change in provisional situation one year post-collision. Methods and Findings 719 individuals with acute whiplash trauma consecutively recruited from emergency departments or primary care after car accidents in Denmark completed questionnaires on socio-demographic and health factors immediately after the collision. After 12 months, a visual analogue scale on neck pain intensity was completed. 3595 matched controls in the general population were sampled, and national public register data on social benefits and any other welfare payments were obtained for participants with acute whiplash trauma and controls from five years pre-collision to 15 months after. Participants with acute whiplash trauma who had received sickness benefit for more than 12 weeks pre-collision had increased odds for negative change in future provisional situation (Odds Ratio (OR) (95% Confidence Interval (CI) = 3.8 (2.1;7.1)) and future neck pain (OR (95%CI) = 3.3 (1.8;6.3)), controlling for other known risk factors. Participants with acute whiplash trauma had weaker attachment to labour market (more weeks of sick leave (χ2(2) = 36.7, p whiplash trauma raised the odds for future negative change in provisional situation (OR (95%CI) = 3.1 (2.3;4.4)) compared with controls. Conclusions Sick leave before the collision strongly predicted prolonged recovery following whiplash trauma. Participants with acute whiplash trauma

Sick Leave within 5 Years of Whiplash Trauma Predicts Recovery: A Prospective Cohort and Register-Based Study.

Directory of Open Access Journals (Sweden)

Tina Birgitte Wisbech Carstensen

Full Text Available 10-22% of individuals sustaining whiplash trauma develop persistent symptoms resulting in reduced working ability and decreased quality of life, but it is poorly understood why some people do not recover. Various collision and post-collision risk factors have been studied, but little is known about pre-collision risk factors. In particular, the impact of sickness and socioeconomic factors before the collision on recovery is sparsely explored. The aim of this study was to examine if welfare payments received within five years pre-collision predict neck pain and negative change in provisional situation one year post-collision.719 individuals with acute whiplash trauma consecutively recruited from emergency departments or primary care after car accidents in Denmark completed questionnaires on socio-demographic and health factors immediately after the collision. After 12 months, a visual analogue scale on neck pain intensity was completed. 3595 matched controls in the general population were sampled, and national public register data on social benefits and any other welfare payments were obtained for participants with acute whiplash trauma and controls from five years pre-collision to 15 months after. Participants with acute whiplash trauma who had received sickness benefit for more than 12 weeks pre-collision had increased odds for negative change in future provisional situation (Odds Ratio (OR (95% Confidence Interval (CI = 3.8 (2.1;7.1 and future neck pain (OR (95%CI = 3.3 (1.8;6.3, controlling for other known risk factors. Participants with acute whiplash trauma had weaker attachment to labour market (more weeks of sick leave (χ2(2 = 36.7, p < 0.001 and unemployment (χ2(2 = 12.5, p = 0.002 pre-collision compared with controls. Experiencing a whiplash trauma raised the odds for future negative change in provisional situation (OR (95%CI = 3.1 (2.3;4.4 compared with controls.Sick leave before the collision strongly predicted prolonged recovery

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning.

Science.gov (United States)

Sun, Yu; Reynolds, Hayley M; Wraith, Darren; Williams, Scott; Finnegan, Mary E; Mitchell, Catherine; Murphy, Declan; Haworth, Annette

2018-04-26

There are currently no methods to estimate cell density in the prostate. This study aimed to develop predictive models to estimate prostate cell density from multiparametric magnetic resonance imaging (mpMRI) data at a voxel level using machine learning techniques. In vivo mpMRI data were collected from 30 patients before radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Ground truth cell density maps were computed from histology and co-registered with mpMRI. Feature extraction and selection were performed on mpMRI data. Final models were fitted using three regression algorithms including multivariate adaptive regression spline (MARS), polynomial regression (PR) and generalised additive model (GAM). Model parameters were optimised using leave-one-out cross-validation on the training data and model performance was evaluated on test data using root mean square error (RMSE) measurements. Predictive models to estimate voxel-wise prostate cell density were successfully trained and tested using the three algorithms. The best model (GAM) achieved a RMSE of 1.06 (± 0.06) × 10 3 cells/mm 2 and a relative deviation of 13.3 ± 0.8%. Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.

The psychological background about human error and safety in NPP

International Nuclear Information System (INIS)

Zhang Li

1992-01-01

A human error is one of the factors which cause an accident in NPP. The in-situ psychological background plays an important role in inducing it. The author analyzes the structure of one's psychological background when one is at work, and gives a few examples of typical psychological background resulting in human errors. Finally it points out that the fundamental way to eliminate the unfavourable psychological background of safety production is to establish the safety culture in NPP along with its characteristics

Scalable data-driven short-term traffic prediction

NARCIS (Netherlands)

Friso, K.; Wismans, L. J.J.; Tijink, M. B.

2017-01-01

Short-term traffic prediction has a lot of potential for traffic management. However, most research has traditionally focused on either traffic models-which do not scale very well to large networks, computationally-or on data-driven methods for freeways, leaving out urban arterials completely. Urban

Error analysis of isotope dilution mass spectrometry method with internal standard

International Nuclear Information System (INIS)

Rizhinskii, M.W.; Vitinskii, M.Y.

1989-02-01

The computation algorithms of the normalized isotopic ratios and element concentration by isotope dilution mass spectrometry with internal standard are presented. A procedure based on the Monte-Carlo calculation is proposed for predicting the magnitude of the errors to be expected. The estimation of systematic and random errors is carried out in the case of the certification of uranium and plutonium reference materials as well as for the use of those reference materials in the analysis of irradiated nuclear fuels. 4 refs, 11 figs, 2 tabs

Hierarchical prediction errors in midbrain and septum during social learning.

Science.gov (United States)

Diaconescu, Andreea O; Mathys, Christoph; Weber, Lilian A E; Kasper, Lars; Mauer, Jan; Stephan, Klaas E

2017-04-01

Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling and genetics to address this question in two separate samples (N = 35, N = 47). Participants played a game requiring inference on an adviser's intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser's trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support 'theory of mind', but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs ('expected uncertainty') about the adviser's fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others. © The Author (2017). Published by Oxford University Press.

Metabolic profiling and predicting the free radical scavenging activity of guava (Psidium guajava L.) leaves according to harvest time by 1H-nuclear magnetic resonance spectroscopy.

Science.gov (United States)

Kim, So-Hyun; Cho, Somi K; Hyun, Sun-Hee; Park, Hae-Eun; Kim, Young-Suk; Choi, Hyung-Kyoon

2011-01-01

Guava leaves were classified and the free radical scavenging activity (FRSA) evaluated according to different harvest times by using the (1)H-NMR-based metabolomic technique. A principal component analysis (PCA) of (1)H-NMR data from the guava leaves provided clear clusters according to the harvesting time. A partial least squares (PLS) analysis indicated a correlation between the metabolic profile and FRSA. FRSA levels of the guava leaves harvested during May and August were high, and those leaves contained higher amounts of 3-hydroxybutyric acid, acetic acid, glutamic acid, asparagine, citric acid, malonic acid, trans-aconitic acid, ascorbic acid, maleic acid, cis-aconitic acid, epicatechin, protocatechuic acid, and xanthine than the leaves harvested during October and December. Epicatechin and protocatechuic acid among those compounds seem to have enhanced FRSA of the guava leaf samples harvested in May and August. A PLS regression model was established to predict guava leaf FRSA at different harvesting times by using a (1)H-NMR data set. The predictability of the PLS model was then tested by internal and external validation. The results of this study indicate that (1)H-NMR-based metabolomic data could usefully characterize guava leaves according to their time of harvesting.

Estimates of error introduced when one-dimensional inverse heat transfer techniques are applied to multi-dimensional problems

International Nuclear Information System (INIS)

Lopez, C.; Koski, J.A.; Razani, A.

2000-01-01

A study of the errors introduced when one-dimensional inverse heat conduction techniques are applied to problems involving two-dimensional heat transfer effects was performed. The geometry used for the study was a cylinder with similar dimensions as a typical container used for the transportation of radioactive materials. The finite element analysis code MSC P/Thermal was used to generate synthetic test data that was then used as input for an inverse heat conduction code. Four different problems were considered including one with uniform flux around the outer surface of the cylinder and three with non-uniform flux applied over 360 deg C, 180 deg C, and 90 deg C sections of the outer surface of the cylinder. The Sandia One-Dimensional Direct and Inverse Thermal (SODDIT) code was used to estimate the surface heat flux of all four cases. The error analysis was performed by comparing the results from SODDIT and the heat flux calculated based on the temperature results obtained from P/Thermal. Results showed an increase in error of the surface heat flux estimates as the applied heat became more localized. For the uniform case, SODDIT provided heat flux estimates with a maximum error of 0.5% whereas for the non-uniform cases, the maximum errors were found to be about 3%, 7%, and 18% for the 360 deg C, 180 deg C, and 90 deg C cases, respectively

Color measurement of tea leaves at different drying periods using hyperspectral imaging technique.

Science.gov (United States)

Xie, Chuanqi; Li, Xiaoli; Shao, Yongni; He, Yong

2014-01-01

This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods.

Color measurement of tea leaves at different drying periods using hyperspectral imaging technique.

Directory of Open Access Journals (Sweden)

Chuanqi Xie

Full Text Available This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb* and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR models. Competitive adaptive reweighted sampling (CARS and successive projections algorithm (SPA were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR] were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods.

DELAMINATION PREDICTION IN DRILLING OF CFRP COMPOSITES USING ARTIFICIAL NEURAL NETWORK

Directory of Open Access Journals (Sweden)

K. PALANIKUMAR

2011-04-01

Full Text Available Carbon fibre reinforced plastic (CFRP materials play a major role in the applications of aeronautic, aerospace, sporting and transportation industries. Machining is indispensible and hence drilling of CFRP materials is considered in this present study with respect to spindle speed in rpm, drill size in mm and feed in mm/min. Delamination is one of the major defects to be dealt with. The experiments are carried out using computer numerical control machine and the results are applied to an artificial neural network (ANN for the prediction of delamination factor at the exit plane of the CFRP material. It is found that ANN model predicts the delamination for any given set of machining parameters with a maximum error of 0.81% and a minimum error of 0.03%. Thus an ANN model is highly suitable for the prediction of delamination in CFRP materials.

Understanding the increase in the number of childbirth-related leave beneficiaries in Serbia

Directory of Open Access Journals (Sweden)

Stanić Katarina

2017-01-01

Full Text Available Over the past number of years, the public expenditures for childbirth-related leave benefits have more than doubled – in 2015 amounted to 0.7% GDP in relation to 0.3% GDP in 2002. This increase can mainly be attributed to the increased number of beneficiaries that grew consistently from 24 thousand in 2002 up to 40 thousand in 2015, despite the fact that the annual number of live births has been almost continually decreasing and the registered employment has dropped by almost 20 per cent in the observed period. One of the clear reasons explaining part of this increase is the extension of 3+ order of birth leaves in 2006, from one to two years, which can explain the increase of around 3.5 thousand of beneficiaries. Another reason is high number of beneficiaries using special child-care leave meant for parents with children with disabilities, but which, in reality, is very often used simply as the extension of parental leave. The average number of special child-care leave beneficiaries in the second half of 2015 amounted to 2.8 thousand. When these two effects are taken into account, we still notice significant increase of beneficiaries of around 10 thousand in the observed period. Fictitious employment during the pregnancy can explain this increase to some extent. Available data unambiguously show that a number of women formally employing during the second and third trimester of pregnancy has increased from 800 in 2002 to almost 3.5 thousand monthly average in the second half of 2015. There are two flaws of the childbirth-related leave programme in Serbia, which together lead to the constant increase of the number of beneficiaries. First is the lack of flexibility of the programme, both in terms of eligibility for acquiring the right as well as in terms of flexibility in use. Maternity/parental leave benefit may acquire only those in „standard employment” i.e. employed under employment contract (and entrepreneurs while other type of

Prediction-error in the context of real social relationships modulates reward system activity

Directory of Open Access Journals (Sweden)

Joshua ePoore

2012-08-01

Full Text Available The human reward system is sensitive to both social (e.g., validation and non-social rewards (e.g., money and is likely integral for relationship development and reputation building. However, data is sparse on the question of whether implicit social reward processing meaningfully contributes to explicit social representations such as trust and attachment security in pre-existing relationships. This event-related fMRI experiment examined reward system prediction-error activity in response to a potent social reward—social validation—and this activity’s relation to both attachment security and trust in the context of real romantic relationships. During the experiment, participants’ expectations for their romantic partners’ positive regard of them were confirmed (validated or violated, in either positive or negative directions. Primary analyses were conducted using predefined regions of interest, the locations of which were taken from previously published research. Results indicate that activity for mid-brain and striatal reward system regions of interest was modulated by social reward expectation violation in ways consistent with prior research on reward prediction-error. Additionally, activity in the striatum during viewing of disconfirmatory information was associated with both increases in post-scan reports of attachment anxiety and decreases in post-scan trust, a finding that follows directly from representational models of attachment and trust.

Prediction-error in the context of real social relationships modulates reward system activity.

Science.gov (United States)

Poore, Joshua C; Pfeifer, Jennifer H; Berkman, Elliot T; Inagaki, Tristen K; Welborn, Benjamin L; Lieberman, Matthew D

2012-01-01

The human reward system is sensitive to both social (e.g., validation) and non-social rewards (e.g., money) and is likely integral for relationship development and reputation building. However, data is sparse on the question of whether implicit social reward processing meaningfully contributes to explicit social representations such as trust and attachment security in pre-existing relationships. This event-related fMRI experiment examined reward system prediction-error activity in response to a potent social reward-social validation-and this activity's relation to both attachment security and trust in the context of real romantic relationships. During the experiment, participants' expectations for their romantic partners' positive regard of them were confirmed (validated) or violated, in either positive or negative directions. Primary analyses were conducted using predefined regions of interest, the locations of which were taken from previously published research. Results indicate that activity for mid-brain and striatal reward system regions of interest was modulated by social reward expectation violation in ways consistent with prior research on reward prediction-error. Additionally, activity in the striatum during viewing of disconfirmatory information was associated with both increases in post-scan reports of attachment anxiety and decreases in post-scan trust, a finding that follows directly from representational models of attachment and trust.

[Medication errors in a neonatal unit: One of the main adverse events].

Science.gov (United States)

Esqué Ruiz, M T; Moretones Suñol, M G; Rodríguez Miguélez, J M; Sánchez Ortiz, E; Izco Urroz, M; de Lamo Camino, M; Figueras Aloy, J

2016-04-01

Neonatal units are one of the hospital areas most exposed to the committing of treatment errors. A medication error (ME) is defined as the avoidable incident secondary to drug misuse that causes or may cause harm to the patient. The aim of this paper is to present the incidence of ME (including feeding) reported in our neonatal unit and its characteristics and possible causal factors. A list of the strategies implemented for prevention is presented. An analysis was performed on the ME declared in a neonatal unit. A total of 511 MEs have been reported over a period of seven years in the neonatal unit. The incidence in the critical care unit was 32.2 per 1000 hospital days or 20 per 100 patients, of which 0.22 per 1000 days had serious repercussions. The ME reported were, 39.5% prescribing errors, 68.1% administration errors, 0.6% were adverse drug reactions. Around two-thirds (65.4%) were produced by drugs, with 17% being intercepted. The large majority (89.4%) had no impact on the patient, but 0.6% caused permanent damage or death. Nurses reported 65.4% of MEs. The most commonly implicated causal factor was distraction (59%). Simple corrective action (alerts), and intermediate (protocols, clinical sessions and courses) and complex actions (causal analysis, monograph) were performed. It is essential to determine the current state of ME, in order to establish preventive measures and, together with teamwork and good practices, promote a climate of safety. Copyright © 2015 Asociación Española de Pediatría. Published by Elsevier España, S.L.U. All rights reserved.

Human Error Assessmentin Minefield Cleaning Operation Using Human Event Analysis

Directory of Open Access Journals (Sweden)

Mohammad Hajiakbari

2015-12-01

Full Text Available Background & objective: Human error is one of the main causes of accidents. Due to the unreliability of the human element and the high-risk nature of demining operations, this study aimed to assess and manage human errors likely to occur in such operations. Methods: This study was performed at a demining site in war zones located in the West of Iran. After acquiring an initial familiarity with the operations, methods, and tools of clearing minefields, job task related to clearing landmines were specified. Next, these tasks were studied using HTA and related possible errors were assessed using ATHEANA. Results: de-mining task was composed of four main operations, including primary detection, technical identification, investigation, and neutralization. There were found four main reasons for accidents occurring in such operations; walking on the mines, leaving mines with no action, error in neutralizing operation and environmental explosion. The possibility of human error in mine clearance operations was calculated as 0.010. Conclusion: The main causes of human error in de-mining operations can be attributed to various factors such as poor weather and operating conditions like outdoor work, inappropriate personal protective equipment, personality characteristics, insufficient accuracy in the work, and insufficient time available. To reduce the probability of human error in de-mining operations, the aforementioned factors should be managed properly.

Can we throw information out of visual working memory and does this leave informational residue in long-term memory?

Directory of Open Access Journals (Sweden)

Ashleigh Monette Maxcey

2014-04-01

Full Text Available Can we entirely erase a temporary memory representation from mind? This question has been addressed in several recent studies that tested the specific hypothesis that a representation can be erased from visual working memory based on a cue that indicated that the representation was no longer necessary for the task. In addition to behavioral results that are consistent with the idea that we can throw information out of visual working memory, recent neurophysiological recordings support this proposal. However, given the infinite capacity of long-term memory, it is unclear whether throwing a representation out of visual working memory really removes its effects on memory entirely. In this paper we advocate for an approach that examines our ability to erase memory representations from working memory, as well as possible traces that those erased representations leave in long-term memory.

The speed of memory errors shows the influence of misleading information: Testing the diffusion model and discrete-state models.

Science.gov (United States)

Starns, Jeffrey J; Dubé, Chad; Frelinger, Matthew E

2018-05-01

In this report, we evaluate single-item and forced-choice recognition memory for the same items and use the resulting accuracy and reaction time data to test the predictions of discrete-state and continuous models. For the single-item trials, participants saw a word and indicated whether or not it was studied on a previous list. The forced-choice trials had one studied and one non-studied word that both appeared in the earlier single-item trials and both received the same response. Thus, forced-choice trials always had one word with a previous correct response and one with a previous error. Participants were asked to select the studied word regardless of whether they previously called both words "studied" or "not studied." The diffusion model predicts that forced-choice accuracy should be lower when the word with a previous error had a fast versus a slow single-item RT, because fast errors are associated with more compelling misleading memory retrieval. The two-high-threshold (2HT) model does not share this prediction because all errors are guesses, so error RT is not related to memory strength. A low-threshold version of the discrete state approach predicts an effect similar to the diffusion model, because errors are a mixture of responses based on misleading retrieval and guesses, and the guesses should tend to be slower. Results showed that faster single-trial errors were associated with lower forced-choice accuracy, as predicted by the diffusion and low-threshold models. Copyright © 2018 Elsevier Inc. All rights reserved.

Hierarchical prediction errors in midbrain and basal forebrain during sensory learning.

Science.gov (United States)

Iglesias, Sandra; Mathys, Christoph; Brodersen, Kay H; Kasper, Lars; Piccirelli, Marco; den Ouden, Hanneke E M; Stephan, Klaas E

2013-10-16

In Bayesian brain theories, hierarchically related prediction errors (PEs) play a central role for predicting sensory inputs and inferring their underlying causes, e.g., the probabilistic structure of the environment and its volatility. Notably, PEs at different hierarchical levels may be encoded by different neuromodulatory transmitters. Here, we tested this possibility in computational fMRI studies of audio-visual learning. Using a hierarchical Bayesian model, we found that low-level PEs about visual stimulus outcome were reflected by widespread activity in visual and supramodal areas but also in the midbrain. In contrast, high-level PEs about stimulus probabilities were encoded by the basal forebrain. These findings were replicated in two groups of healthy volunteers. While our fMRI measures do not reveal the exact neuron types activated in midbrain and basal forebrain, they suggest a dichotomy between neuromodulatory systems, linking dopamine to low-level PEs about stimulus outcome and acetylcholine to more abstract PEs about stimulus probabilities. Copyright © 2013 Elsevier Inc. All rights reserved.

Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error

OpenAIRE

Shipitsin, M; Small, C; Choudhury, S; Giladi, E; Friedlander, S; Nardone, J; Hussain, S; Hurley, A D; Ernst, C; Huang, Y E; Chang, H; Nifong, T P; Rimm, D L; Dunyak, J; Loda, M

2014-01-01

Background: Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation. Methods: Prostatectom...

Examining the Experiences of Young People Transitioning from Out-of-Home Care in Rural Victoria

Science.gov (United States)

Mendes, Philip

2012-01-01

Young people leaving state out-of-home care are arguably one of the most vulnerable and disadvantaged groups in society. Many have been found to experience significant health, social and educational deficits. In recent years, most Australian States and Territories have introduced specialist leaving care and after care programs and supports, but…

New statement of leave format

CERN Multimedia

HR Department

2009-01-01

Following the communication of the Standing Concertation Committee published in Weekly Bulletin No. 18-19 of 27 April 2009, the current statement of leave on monthly pay slips has been replaced with the EDH Leave Transactions report that displays the up-to-date situation of individual leave balances at all times. The report is available on EDH. Additionally, the layout of the pay slip has been modernised. The new version of the pay slip will be send out from September 2009 onwards. Finance and Purchasing Department, Personnel Accounting Human Resources Department, Organisation and Procedures General Infrastructure Services Department, Administrative Information Services

Difference in Perseverative Errors during a Visual Attention Task with Auditory Distractors in Alpha-9 Nicotinic Receptor Subunit Wild Type and Knock-Out Mice

Directory of Open Access Journals (Sweden)

Pascal Jorratt

2017-11-01

Full Text Available The auditory efferent system is a neural network that originates in the auditory cortex and projects to the cochlear receptor through olivocochlear (OC neurons. Medial OC neurons make cholinergic synapses with outer hair cells (OHCs through nicotinic receptors constituted by α9 and α10 subunits. One of the physiological functions of the α9 nicotinic receptor subunit (α9-nAChR is the suppression of auditory distractors during selective attention to visual stimuli. In a recent study we demonstrated that the behavioral performance of alpha-9 nicotinic receptor knock-out (KO mice is altered during selective attention to visual stimuli with auditory distractors since they made less correct responses and more omissions than wild type (WT mice. As the inhibition of the behavioral responses to irrelevant stimuli is an important mechanism of the selective attention processes, behavioral errors are relevant measures that can reflect altered inhibitory control. Errors produced during a cued attention task can be classified as premature, target and perseverative errors. Perseverative responses can be considered as an inability to inhibit the repetition of an action already planned, while premature responses can be considered as an index of the ability to wait or retain an action. Here, we studied premature, target and perseverative errors during a visual attention task with auditory distractors in WT and KO mice. We found that α9-KO mice make fewer perseverative errors with longer latencies than WT mice in the presence of auditory distractors. In addition, although we found no significant difference in the number of target error between genotypes, KO mice made more short-latency target errors than WT mice during the presentation of auditory distractors. The fewer perseverative error made by α9-KO mice could be explained by a reduced motivation for reward and an increased impulsivity during decision making with auditory distraction in KO mice.

[Medication errors in Spanish intensive care units].

Science.gov (United States)

Merino, P; Martín, M C; Alonso, A; Gutiérrez, I; Alvarez, J; Becerril, F

2013-01-01

To estimate the incidence of medication errors in Spanish intensive care units. Post hoc study of the SYREC trial. A longitudinal observational study carried out during 24 hours in patients admitted to the ICU. Spanish intensive care units. Patients admitted to the intensive care unit participating in the SYREC during the period of study. Risk, individual risk, and rate of medication errors. The final study sample consisted of 1017 patients from 79 intensive care units; 591 (58%) were affected by one or more incidents. Of these, 253 (43%) had at least one medication-related incident. The total number of incidents reported was 1424, of which 350 (25%) were medication errors. The risk of suffering at least one incident was 22% (IQR: 8-50%) while the individual risk was 21% (IQR: 8-42%). The medication error rate was 1.13 medication errors per 100 patient-days of stay. Most incidents occurred in the prescription (34%) and administration (28%) phases, 16% resulted in patient harm, and 82% were considered "totally avoidable". Medication errors are among the most frequent types of incidents in critically ill patients, and are more common in the prescription and administration stages. Although most such incidents have no clinical consequences, a significant percentage prove harmful for the patient, and a large proportion are avoidable. Copyright © 2012 Elsevier España, S.L. and SEMICYUC. All rights reserved.

Episodic Memory Encoding Interferes with Reward Learning and Decreases Striatal Prediction Errors

Science.gov (United States)

Braun, Erin Kendall; Daw, Nathaniel D.

2014-01-01

Learning is essential for adaptive decision making. The striatum and its dopaminergic inputs are known to support incremental reward-based learning, while the hippocampus is known to support encoding of single events (episodic memory). Although traditionally studied separately, in even simple experiences, these two types of learning are likely to co-occur and may interact. Here we sought to understand the nature of this interaction by examining how incremental reward learning is related to concurrent episodic memory encoding. During the experiment, human participants made choices between two options (colored squares), each associated with a drifting probability of reward, with the goal of earning as much money as possible. Incidental, trial-unique object pictures, unrelated to the choice, were overlaid on each option. The next day, participants were given a surprise memory test for these pictures. We found that better episodic memory was related to a decreased influence of recent reward experience on choice, both within and across participants. fMRI analyses further revealed that during learning the canonical striatal reward prediction error signal was significantly weaker when episodic memory was stronger. This decrease in reward prediction error signals in the striatum was associated with enhanced functional connectivity between the hippocampus and striatum at the time of choice. Our results suggest a mechanism by which memory encoding may compete for striatal processing and provide insight into how interactions between different forms of learning guide reward-based decision making. PMID:25378157

Leaves of Absence. School Law Summary.

Science.gov (United States)

National Education Association, Washington, DC. Research Div.

This report contains State-by-State statutory summaries on three types of leaves of absence relating to teachers -- sick leave, maternity leave, and sabbatical leave. Only State laws that have specific reference to one of these three types of leaves of absence are included. Not included are those statutes granting boards of education the general…

Reducing sick leave of Dutch vocational school students: adaptation of a sick leave protocol using the intervention mapping process.

Science.gov (United States)

de Kroon, Marlou L A; Bulthuis, Jozien; Mulder, Wico; Schaafsma, Frederieke G; Anema, Johannes R

2016-12-01

Since the extent of sick leave and the problems of vocational school students are relatively large, we aimed to tailor a sick leave protocol at Dutch lower secondary education schools to the particular context of vocational schools. Four steps of the iterative process of Intervention Mapping (IM) to adapt this protocol were carried out: (1) performing a needs assessment and defining a program objective, (2) determining the performance and change objectives, (3) identifying theory-based methods and practical strategies and (4) developing a program plan. Interviews with students using structured questionnaires, in-depth interviews with relevant stakeholders, a literature research and, finally, a pilot implementation were carried out. A sick leave protocol was developed that was feasible and acceptable for all stakeholders. The main barriers for widespread implementation are time constraints in both monitoring and acting upon sick leave by school and youth health care. The iterative process of IM has shown its merits in the adaptation of the manual 'A quick return to school is much better' to a sick leave protocol for vocational school students.

Genomic prediction in a breeding program of perennial ryegrass

DEFF Research Database (Denmark)

Fé, Dario; Ashraf, Bilal; Greve-Pedersen, Morten

2015-01-01

We present a genomic selection study performed on 1918 rye grass families (Lolium perenne L.), which were derived from a commercial breeding program at DLF-Trifolium, Denmark. Phenotypes were recorded on standard plots, across 13 years and in 6 different countries. Variants were identified...... this set. Estimated Breeding Value and prediction accuracies were calculated trough two different cross-validation schemes: (i) k-fold (k=10); (ii) leaving out one parent combination at the time, in order to test for accuracy of predicting new families. Accuracies ranged between 0.56 and 0.97 for scheme (i....... A larger set of 1791 F2s were used as training set to predict EBVs of 127 synthetic families (originated from poly-crosses between 5-11 single plants) for heading date and crown rust resistance. Prediction accuracies were 0.93 and 0.57 respectively. Results clearly demonstrate considerable potential...

A Hybrid Approach for Improving Image Segmentation: Application to Phenotyping of Wheat Leaves.

Directory of Open Access Journals (Sweden)

Joshua Chopin

Full Text Available In this article we propose a novel tool that takes an initial segmented image and returns a more accurate segmentation that accurately captures sharp features such as leaf tips, twists and axils. Our algorithm utilizes basic a-priori information about the shape of plant leaves and local image orientations to fit active contour models to important plant features that have been missed during the initial segmentation. We compare the performance of our approach with three state-of-the-art segmentation techniques, using three error metrics. The results show that leaf tips are detected with roughly one half of the original error, segmentation accuracy is almost always improved and more than half of the leaf breakages are corrected.

A Hold-out method to correct PCA variance inflation

DEFF Research Database (Denmark)

Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Hansen, Lars Kai

2012-01-01

In this paper we analyze the problem of variance inflation experienced by the PCA algorithm when working in an ill-posed scenario where the dimensionality of the training set is larger than its sample size. In an earlier article a correction method based on a Leave-One-Out (LOO) procedure...

Evaluation of reversible contraceptive potential of Cordia dichotoma leaves extract

Directory of Open Access Journals (Sweden)

Plaban Bhattacharya

2013-04-01

Full Text Available Considering the safety-risk ratio of steroidal contraceptives, the present work was carried out to evaluate ethno-contraceptive use of Cordia dichotoma G. Forst., Boraginaceae, leaves (LCD. Preliminary pharmacological screening was performed on post-coital female albino rats. The leaves extract (LD50 5.50 g/kg bw showed 100% anti-implantation activity (n=10 at 800 mg/kg dose level. (2-hydroxypropyl-β-cyclodextrin (BCD was used as bioavailability enhancer to form LCD-BCD complex, characterized by DLS, SEM and XRD analyses. The LCD-BCD complex (1:1, w/w exhibited 100% pregnancy interception (n=20 at the dose level of 250 mg/kg and also showed strong estrogenic potential with a luteal phase defect. Qualitative and quantitative phytochemical analyses were carried out. The LCD extract was standardized by a validated HPTLC method and two contraceptive phytoconstituents, apigenin and luteolin were isolated. A detailed pharmacological analyses followed by chronic toxicity study were performed to predict the reversible nature of the developed phytopharmaceutical. The histological and biochemical estimations detected the reversible contraceptive potential after withdrawal. The observations suggested that the developed phyto-pharmaceutical has potential antifertility activity with safety aspects.

Evaluation of reversible contraceptive potential of Cordia dichotoma leaves extract

Directory of Open Access Journals (Sweden)

Plaban Bhattacharya

2013-03-01

Full Text Available Considering the safety-risk ratio of steroidal contraceptives, the present work was carried out to evaluate ethno-contraceptive use of Cordia dichotoma G. Forst., Boraginaceae, leaves (LCD. Preliminary pharmacological screening was performed on post-coital female albino rats. The leaves extract (LD50 5.50 g/kg bw showed 100% anti-implantation activity (n=10 at 800 mg/kg dose level. (2-hydroxypropyl-β-cyclodextrin (BCD was used as bioavailability enhancer to form LCD-BCD complex, characterized by DLS, SEM and XRD analyses. The LCD-BCD complex (1:1, w/w exhibited 100% pregnancy interception (n=20 at the dose level of 250 mg/kg and also showed strong estrogenic potential with a luteal phase defect. Qualitative and quantitative phytochemical analyses were carried out. The LCD extract was standardized by a validated HPTLC method and two contraceptive phytoconstituents, apigenin and luteolin were isolated. A detailed pharmacological analyses followed by chronic toxicity study were performed to predict the reversible nature of the developed phytopharmaceutical. The histological and biochemical estimations detected the reversible contraceptive potential after withdrawal. The observations suggested that the developed phyto-pharmaceutical has potential antifertility activity with safety aspects.

Forecast Combination under Heavy-Tailed Errors

Directory of Open Access Journals (Sweden)

Gang Cheng

2015-11-01

Full Text Available Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our knowledge, little has been done to obtain reliable forecast combinations for such situations. The familiar forecast combination methods, such as simple average, least squares regression or those based on the variance-covariance of the forecasts, may perform very poorly due to the fact that outliers tend to occur, and they make these methods have unstable weights, leading to un-robust forecasts. To address this problem, in this paper, we propose two nonparametric forecast combination methods. One is specially proposed for the situations in which the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student’s t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to a shortage of data and/or an evolving data-generating process. Adaptive risk bounds of both methods are developed. They show that the resulting combined forecasts yield near optimal mean forecast errors relative to the candidate forecasts. Simulations and a real example demonstrate their superior performance in that they indeed tend to have significantly smaller prediction errors than the previous combination methods in the presence of forecast outliers.

Influence of yellow rust infextion on 32P transport in detached barley leaves

International Nuclear Information System (INIS)

Schubert, J.

1982-01-01

Several barley cultivars (Hordeum vulgare L.) differing in their resistance to yellow rust (Puccinia striiformis West.) were tested for relationships between changes of 32 P transport in detached leaves and resistance to yellow rust disease. Investigation carried out with detached second leaves from plants infected at their first leaf revealed a matter transport in these leaves changed by the infection. Transport was also influenced by inoculation with yellow rust uredospores. In that case rust infection influenced the basipetal transport less strongly in resistent plants than in susceptible ones. Connected with the findings the influence of fungal substances on transport processes is discussed in general. (author)

Strictly local one-dimensional topological quantum error correction with symmetry-constrained cellular automata

Directory of Open Access Journals (Sweden)

Nicolai Lang, Hans Peter Büchler

2018-01-01

Full Text Available Active quantum error correction on topological codes is one of the most promising routes to long-term qubit storage. In view of future applications, the scalability of the used decoding algorithms in physical implementations is crucial. In this work, we focus on the one-dimensional Majorana chain and construct a strictly local decoder based on a self-dual cellular automaton. We study numerically and analytically its performance and exploit these results to contrive a scalable decoder with exponentially growing decoherence times in the presence of noise. Our results pave the way for scalable and modular designs of actively corrected one-dimensional topological quantum memories.

Rectifying calibration error of Goldmann applanation tonometer is easy!

Directory of Open Access Journals (Sweden)

Nikhil S Choudhari

2014-01-01

Full Text Available Purpose: Goldmann applanation tonometer (GAT is the current Gold standard tonometer. However, its calibration error is common and can go unnoticed in clinics. Its company repair has limitations. The purpose of this report is to describe a self-taught technique of rectifying calibration error of GAT. Materials and Methods: Twenty-nine slit-lamp-mounted Haag-Streit Goldmann tonometers (Model AT 900 C/M; Haag-Streit, Switzerland were included in this cross-sectional interventional pilot study. The technique of rectification of calibration error of the tonometer involved cleaning and lubrication of the instrument followed by alignment of weights when lubrication alone didn′t suffice. We followed the South East Asia Glaucoma Interest Group′s definition of calibration error tolerance (acceptable GAT calibration error within ±2, ±3 and ±4 mm Hg at the 0, 20 and 60-mm Hg testing levels, respectively. Results: Twelve out of 29 (41.3% GATs were out of calibration. The range of positive and negative calibration error at the clinically most important 20-mm Hg testing level was 0.5 to 20 mm Hg and -0.5 to -18 mm Hg, respectively. Cleaning and lubrication alone sufficed to rectify calibration error of 11 (91.6% faulty instruments. Only one (8.3% faulty GAT required alignment of the counter-weight. Conclusions: Rectification of calibration error of GAT is possible in-house. Cleaning and lubrication of GAT can be carried out even by eye care professionals and may suffice to rectify calibration error in the majority of faulty instruments. Such an exercise may drastically reduce the downtime of the Gold standard tonometer.

A model for predicting lung cancer response to therapy

International Nuclear Information System (INIS)

Seibert, Rebecca M.; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.

2007-01-01

Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during

Influence of chemical treatment on dimensional stability of narrow-leaved ash - part one: Tangential swelling

Directory of Open Access Journals (Sweden)

Popović Jasmina

2012-01-01

Full Text Available Dimensional change in wood occurs with the change in hygroscopic moisture content, as a consequence of available hydroxyl groups in wood constituents, allowing for the hydrogen bonding with water molecules. Various pretreatments of wood material are being frequently applied in the wood processing industry. One of the main effects of such processes is the hydrolysis of hemicelluloses, which is the main carrier of the free hydroxyl groups in wood material. Hence, the influence of water treatment and the acetic acid treatment on dimensional stability of narrow-leaved ash (Fraxinus angustifolia Vahl. ssp. Pannonica Soó & Simon were examined in this paper. Duration of treatments was 1h, 2h, 3h and 4h for both solvents. In addition the acetic acid was separately used in concentrations of 3% and 6%. Dimensional stability of the control (referent and treated sample groups were tested on oven dried samples which were consequently submerged in the distilled water during 32 days. The increase of dimensional stability of narrow-leaved ash was achieved with all of the three treatments (one treatment with water and the two with acetic acid solutions. Simultaneously, it was noticed that the results of water uptake and tangential swelling were not significantly affected by the duration (length of the treatments. [Projekat Ministarstva nauke Republike Srbije, br. TP-031041

Distribution of the Determinant of the Sample Correlation Matrix: Monte Carlo Type One Error Rates.

Science.gov (United States)

Reddon, John R.; And Others

1985-01-01

Computer sampling from a multivariate normal spherical population was used to evaluate the type one error rates for a test of sphericity based on the distribution of the determinant of the sample correlation matrix. (Author/LMO)

Patient identification errors: the detective in the laboratory.

Science.gov (United States)

Salinas, Maria; López-Garrigós, Maite; Lillo, Rosa; Gutiérrez, Mercedes; Lugo, Javier; Leiva-Salinas, Carlos

2013-11-01

The eradication of errors regarding patients' identification is one of the main goals for safety improvement. As clinical laboratory intervenes in 70% of clinical decisions, laboratory safety is crucial in patient safety. We studied the number of Laboratory Information System (LIS) demographic data errors registered in our laboratory during one year. The laboratory attends a variety of inpatients and outpatients. The demographic data of outpatients is registered in the LIS, when they present to the laboratory front desk. The requests from the primary care centers (PCC) are made electronically by the general practitioner. A manual step is always done at the PCC to conciliate the patient identification number in the electronic request with the one in the LIS. Manual registration is done through hospital information system demographic data capture when patient's medical record number is registered in LIS. Laboratory report is always sent out electronically to the patient's electronic medical record. Daily, every demographic data in LIS is manually compared to the request form to detect potential errors. Fewer errors were committed when electronic order was used. There was great error variability between PCC when using the electronic order. LIS demographic data manual registration errors depended on patient origin and test requesting method. Even when using the electronic approach, errors were detected. There was a great variability between PCC even when using this electronic modality; this suggests that the number of errors is still dependent on the personnel in charge of the technology. © 2013.

IPMP Global Fit - A one-step direct data analysis tool for predictive microbiology.

Science.gov (United States)

Huang, Lihan

2017-12-04

The objective of this work is to develop and validate a unified optimization algorithm for performing one-step global regression analysis of isothermal growth and survival curves for determination of kinetic parameters in predictive microbiology. The algorithm is incorporated with user-friendly graphical interfaces (GUIs) to develop a data analysis tool, the USDA IPMP-Global Fit. The GUIs are designed to guide the users to easily navigate through the data analysis process and properly select the initial parameters for different combinations of mathematical models. The software is developed for one-step kinetic analysis to directly construct tertiary models by minimizing the global error between the experimental observations and mathematical models. The current version of the software is specifically designed for constructing tertiary models with time and temperature as the independent model parameters in the package. The software is tested with a total of 9 different combinations of primary and secondary models for growth and survival of various microorganisms. The results of data analysis show that this software provides accurate estimates of kinetic parameters. In addition, it can be used to improve the experimental design and data collection for more accurate estimation of kinetic parameters. IPMP-Global Fit can be used in combination with the regular USDA-IPMP for solving the inverse problems and developing tertiary models in predictive microbiology. Published by Elsevier B.V.

Error Analysis for Arithmetic Word Problems--A Case Study of Primary Three Students in One Singapore School

Science.gov (United States)

Cheng, Lu Pien

2015-01-01

In this study, ways in which 9-year old students from one Singapore school solved 1-step and 2-step word problems based on the three semantic structures were examined. The students' work and diagrams provided insights into the range of errors in word problem solving for 1- step and 2-step word problems. In particular, the errors provided some…

The Temporary Leave Dilemma -

DEFF Research Database (Denmark)

Amilon, Anna

2010-01-01

Lone mothers have to take care of a sick child with little or no help from the child’s other parent and have to carry all costs connected to leave-taking. This paper empirically tests whether lone mothers take more temporary parental leave to care for sick children than partnered mothers...... and whether parental leave is associated with a signaling cost. The results from this study of Swedish mothers show that lone mothers use more temporary parental leave than partnered mothers. Further, within the group of lone mothers, those with higher socioeconomic status take less temporary parental leave...... than those with lower socioeconomic status, whereas no such differences are found within the group of partnered mothers. One possible interpretation is that signaling costs negatively influence the utilization of temporary parental leave for lone mothers....

Capability of the "Ball-Berry" model for predicting stomatal conductance and water use efficiency of potato leaves under different irrigation regimes

DEFF Research Database (Denmark)

Liu, Fulai; Andersen, Mathias N.; Jensen, Christian Richardt

2009-01-01

was used for model parameterization, where measurements of midday leaf gas exchange of potted potatoes were done during progressive soil drying for 2 weeks at tuber initiation and earlier bulking stages. The measured photosynthetic rate (An) was used as an input for the model. To account for the effects......The capability of the ‘Ball-Berry' model (BB-model) in predicting stomatal conductance (gs) and water use efficiency (WUE) of potato (Solanum tuberosum L.) leaves under different irrigation regimes was tested using data from two independent pot experiments in 2004 and 2007. Data obtained from 2004...... of soil water deficits on gs, a simple equation modifying the slope (m) based on the mean soil water potential (Ψs) in the soil columns was incorporated into the original BB-model. Compared with the original BB-model, the modified BB-model showed better predictability for both gs and WUE of potato leaves...

RECRUITMENT FINANCED BY SAVED LEAVE (RSL PROGRAMME)

CERN Multimedia

Division du Personnel; Tel. 73903

1999-01-01

Transfer to the saved leave account and saved leave bonusStaff members participating in the RSL programme may opt to transfer up to 10 days of unused annual leave or unused compensatory leave into their saved leave account, at the end of the leave year, i.e. 30 September (as set out in the implementation procedure dated 27 August 1997).A leave transfer request form, which you should complete, sign and return, if you wish to use this possibility, has been addressed you. To allow the necessary time for the processing of your request, you should return it without delay.As foreseen in the implementation procedure, an additional day of saved leave will be granted for each full period of 20 days remaining in the saved leave account on 31 December 1999, for any staff member participating in the RSL programme until that date.For part-time staff members participating in the RSL programme, the above-mentioned days of leave (annual, compensatory and saved) are adjusted proportionally to their contractual working week as...

Why are a quarter of faculty considering leaving academic medicine? A study of their perceptions of institutional culture and intentions to leave at 26 representative U.S. medical schools.

Science.gov (United States)

Pololi, Linda H; Krupat, Edward; Civian, Janet T; Ash, Arlene S; Brennan, Robert T

2012-07-01

Vital, productive faculty are critical to academic medicine, yet studies indicate high dissatisfaction and attrition. The authors sought to identify key personal and cultural factors associated with intentions to leave one's institution and/or academic medicine. From 2007 through early 2009, the authors surveyed a stratified random sample of 4,578 full-time faculty from 26 representative U.S. medical schools. The survey asked about advancement, engagement, relationships, diversity and equity, leadership, institutional values and practices, and work-life integration. A two-level, multinomial logit model was used to predict leaving intentions. A total of 2,381 faculty responded (52%); 1,994 provided complete data for analysis. Of these, 1,062 (53%) were female and 475 (24%) were underrepresented minorities in medicine. Faculty valued their work, but 273 (14%) had seriously considered leaving their own institution during the prior year and 421 (21%) had considered leaving academic medicine altogether because of dissatisfaction; an additional 109 (5%) cited personal/family issues and 49 (2%) retirement as reasons to leave. Negative perceptions of the culture-unrelatedness, feeling moral distress at work, and lack of engagement-were associated with leaving for dissatisfaction. Other significant predictors were perceptions of values incongruence, low institutional support, and low self-efficacy. Institutional characteristics and personal variables (e.g., gender) were not predictive. Findings suggest that academic medicine does not support relatedness and a moral culture for many faculty. If these issues are not addressed, academic health centers may find themselves with dissatisfied faculty looking to go elsewhere.

EEG-based emergency braking intention prediction for brain-controlled driving considering one electrode falling-off.

Science.gov (United States)

Huikang Wang; Luzheng Bi; Teng Teng

2017-07-01

This paper proposes a novel method of electroencephalography (EEG)-based driver emergency braking intention detection system for brain-controlled driving considering one electrode falling-off. First, whether one electrode falls off is discriminated based on EEG potentials. Then, the missing signals are estimated by using the signals collected from other channels based on multivariate linear regression. Finally, a linear decoder is applied to classify driver intentions. Experimental results show that the falling-off discrimination accuracy is 99.63% on average and the correlation coefficient and root mean squared error (RMSE) between the estimated and experimental data are 0.90 and 11.43 μV, respectively, on average. Given one electrode falls off, the system accuracy of the proposed intention prediction method is significantly higher than that of the original method (95.12% VS 79.11%) and is close to that (95.95%) of the original system under normal situations (i. e., no electrode falling-off).

Episodic memory encoding interferes with reward learning and decreases striatal prediction errors.

Science.gov (United States)

Wimmer, G Elliott; Braun, Erin Kendall; Daw, Nathaniel D; Shohamy, Daphna

2014-11-05

Learning is essential for adaptive decision making. The striatum and its dopaminergic inputs are known to support incremental reward-based learning, while the hippocampus is known to support encoding of single events (episodic memory). Although traditionally studied separately, in even simple experiences, these two types of learning are likely to co-occur and may interact. Here we sought to understand the nature of this interaction by examining how incremental reward learning is related to concurrent episodic memory encoding. During the experiment, human participants made choices between two options (colored squares), each associated with a drifting probability of reward, with the goal of earning as much money as possible. Incidental, trial-unique object pictures, unrelated to the choice, were overlaid on each option. The next day, participants were given a surprise memory test for these pictures. We found that better episodic memory was related to a decreased influence of recent reward experience on choice, both within and across participants. fMRI analyses further revealed that during learning the canonical striatal reward prediction error signal was significantly weaker when episodic memory was stronger. This decrease in reward prediction error signals in the striatum was associated with enhanced functional connectivity between the hippocampus and striatum at the time of choice. Our results suggest a mechanism by which memory encoding may compete for striatal processing and provide insight into how interactions between different forms of learning guide reward-based decision making. Copyright © 2014 the authors 0270-6474/14/3414901-12$15.00/0.

Short-read reading-frame predictors are not created equal: sequence error causes loss of signal

Directory of Open Access Journals (Sweden)

Trimble William L

2012-07-01

Full Text Available Abstract Background Gene prediction algorithms (or gene callers are an essential tool for analyzing shotgun nucleic acid sequence data. Gene prediction is a ubiquitous step in sequence analysis pipelines; it reduces the volume of data by identifying the most likely reading frame for a fragment, permitting the out-of-frame translations to be ignored. In this study we evaluate five widely used ab initio gene-calling algorithms—FragGeneScan, MetaGeneAnnotator, MetaGeneMark, Orphelia, and Prodigal—for accuracy on short (75–1000 bp fragments containing sequence error from previously published artificial data and “real” metagenomic datasets. Results While gene prediction tools have similar accuracies predicting genes on error-free fragments, in the presence of sequencing errors considerable differences between tools become evident. For error-containing short reads, FragGeneScan finds more prokaryotic coding regions than does MetaGeneAnnotator, MetaGeneMark, Orphelia, or Prodigal. This improved detection of genes in error-containing fragments, however, comes at the cost of much lower (50% specificity and overprediction of genes in noncoding regions. Conclusions Ab initio gene callers offer a significant reduction in the computational burden of annotating individual nucleic acid reads and are used in many metagenomic annotation systems. For predicting reading frames on raw reads, we find the hidden Markov model approach in FragGeneScan is more sensitive than other gene prediction tools, while Prodigal, MGA, and MGM are better suited for higher-quality sequences such as assembled contigs.

Learning Similar Actions by Reinforcement or Sensory-Prediction Errors Rely on Distinct Physiological Mechanisms.

Science.gov (United States)

Uehara, Shintaro; Mawase, Firas; Celnik, Pablo

2017-09-14

Humans can acquire knowledge of new motor behavior via different forms of learning. The two forms most commonly studied have been the development of internal models based on sensory-prediction errors (error-based learning) and success-based feedback (reinforcement learning). Human behavioral studies suggest these are distinct learning processes, though the neurophysiological mechanisms that are involved have not been characterized. Here, we evaluated physiological markers from the cerebellum and the primary motor cortex (M1) using noninvasive brain stimulations while healthy participants trained finger-reaching tasks. We manipulated the extent to which subjects rely on error-based or reinforcement by providing either vector or binary feedback about task performance. Our results demonstrated a double dissociation where learning the task mainly via error-based mechanisms leads to cerebellar plasticity modifications but not long-term potentiation (LTP)-like plasticity changes in M1; while learning a similar action via reinforcement mechanisms elicited M1 LTP-like plasticity but not cerebellar plasticity changes. Our findings indicate that learning complex motor behavior is mediated by the interplay of different forms of learning, weighing distinct neural mechanisms in M1 and the cerebellum. Our study provides insights for designing effective interventions to enhance human motor learning. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Action errors, error management, and learning in organizations.

Science.gov (United States)

Frese, Michael; Keith, Nina

2015-01-03

Every organization is confronted with errors. Most errors are corrected easily, but some may lead to negative consequences. Organizations often focus on error prevention as a single strategy for dealing with errors. Our review suggests that error prevention needs to be supplemented by error management--an approach directed at effectively dealing with errors after they have occurred, with the goal of minimizing negative and maximizing positive error consequences (examples of the latter are learning and innovations). After defining errors and related concepts, we review research on error-related processes affected by error management (error detection, damage control). Empirical evidence on positive effects of error management in individuals and organizations is then discussed, along with emotional, motivational, cognitive, and behavioral pathways of these effects. Learning from errors is central, but like other positive consequences, learning occurs under certain circumstances--one being the development of a mind-set of acceptance of human error.

A calibration and data assimilation method using the Bayesian MARS emulator

International Nuclear Information System (INIS)

Stripling, H.F.; McClarren, R.G.; Kuranz, C.C.; Grosskopf, M.J.; Rutter, E.; Torralva, B.R.

2013-01-01

Highlights: ► We outline a transparent, flexible method for the calibration of uncertain inputs to computer models. ► We account for model, data, emulator, and measurement uncertainties. ► The method produces improved predictive results, which are validated using leave one-out experiments. ► Our implementation leverages the Bayesian MARS emulator, but any emulator may be substituted. -- Abstract: We present a method for calibrating the uncertain inputs to a computer model using available experimental data. The goal of the procedure is to estimate the posterior distribution of the uncertain inputs such that when samples from the posterior are used as inputs to future model runs, the model is more likely to replicate (or predict) the experimental response. The calibration is performed by sampling the space of the uncertain inputs, using the computer model (or, more likely, an emulator for the computer model) to assign weights to the samples, and applying the weights to produce the posterior distributions and generate predictions of new experiments with confidence bounds. The method is similar to Metropolis–Hastings calibration methods with independently sampled updates, except that we generate samples beforehand and replace the candidate acceptance routine with a weighting scheme. We apply our method to the calibration of a Hyades 2D model of laser energy deposition in beryllium. We employ a Bayesian Multivariate Adaptive Regression Splines (BMARS) emulator as a surrogate for Hyades 2D. We treat a range of uncertainties in our application, including uncertainties in the experimental inputs, experimental measurement error, and systematic experimental timing errors. The resulting posterior distributions agree with our existing intuition, and we validate the results by performing a series of leave-one-out predictions. We find that the calibrated predictions are considerably more accurate and less uncertain than blind sampling of the forward model alone.

SU-F-J-207: Non-Small Cell Lung Cancer Patient Survival Prediction with Quantitative Tumor Textures Analysis in Baseline CT

Energy Technology Data Exchange (ETDEWEB)

Wu, Y; Zou, J; Murillo, P; Nosher, J; Amorosa, J; Bramwit, M; Yue, N; Jabbour, S; Foran, D [Rutgers University, New Brunswick, NJ (United States)

2016-06-15

Purpose: Chemo-radiation therapy (CRT) is widely used in treating patients with locally advanced non-small cell lung cancer (NSCLC). Determination of the likelihood of patient response to treatment and optimization of treatment regime is of clinical significance. Up to date, no imaging biomarker has reliably correlated to NSCLC patient survival rate. This pilot study is to extract CT texture information from tumor regions for patient survival prediction. Methods: Thirteen patients with stage II-III NSCLC were treated using CRT with a median dose of 6210 cGy. Non-contrast-enhanced CT images were acquired for treatment planning and retrospectively collected for this study. Texture analysis was applied in segmented tumor regions using the Local Binary Pattern method (LBP). By comparing its HU with neighboring voxels, the LBPs of a voxel were measured in multiple scales with different group radiuses and numbers of neighbors. The LBP histograms formed a multi-dimensional texture vector for each patient, which was then used to establish and test a Support Vector Machine (SVM) model to predict patients’ one year survival. The leave-one-out cross validation strategy was used recursively to enlarge the training set and derive a reliable predictor. The predictions were compared with the true clinical outcomes. Results: A 10-dimensional LBP histogram was extracted from 3D segmented tumor region for each of the 13 patients. Using the SVM model with the leave-one-out strategy, only 1 out of 13 patients was misclassified. The experiments showed an accuracy of 93%, sensitivity of 100%, and specificity of 86%. Conclusion: Within the framework of a Support Vector Machine based model, the Local Binary Pattern method is able to extract a quantitative imaging biomarker in the prediction of NSCLC patient survival. More patients are to be included in the study.

SU-F-J-207: Non-Small Cell Lung Cancer Patient Survival Prediction with Quantitative Tumor Textures Analysis in Baseline CT

International Nuclear Information System (INIS)

Wu, Y; Zou, J; Murillo, P; Nosher, J; Amorosa, J; Bramwit, M; Yue, N; Jabbour, S; Foran, D

2016-01-01

Purpose: Chemo-radiation therapy (CRT) is widely used in treating patients with locally advanced non-small cell lung cancer (NSCLC). Determination of the likelihood of patient response to treatment and optimization of treatment regime is of clinical significance. Up to date, no imaging biomarker has reliably correlated to NSCLC patient survival rate. This pilot study is to extract CT texture information from tumor regions for patient survival prediction. Methods: Thirteen patients with stage II-III NSCLC were treated using CRT with a median dose of 6210 cGy. Non-contrast-enhanced CT images were acquired for treatment planning and retrospectively collected for this study. Texture analysis was applied in segmented tumor regions using the Local Binary Pattern method (LBP). By comparing its HU with neighboring voxels, the LBPs of a voxel were measured in multiple scales with different group radiuses and numbers of neighbors. The LBP histograms formed a multi-dimensional texture vector for each patient, which was then used to establish and test a Support Vector Machine (SVM) model to predict patients’ one year survival. The leave-one-out cross validation strategy was used recursively to enlarge the training set and derive a reliable predictor. The predictions were compared with the true clinical outcomes. Results: A 10-dimensional LBP histogram was extracted from 3D segmented tumor region for each of the 13 patients. Using the SVM model with the leave-one-out strategy, only 1 out of 13 patients was misclassified. The experiments showed an accuracy of 93%, sensitivity of 100%, and specificity of 86%. Conclusion: Within the framework of a Support Vector Machine based model, the Local Binary Pattern method is able to extract a quantitative imaging biomarker in the prediction of NSCLC patient survival. More patients are to be included in the study.

Recursive prediction error methods for online estimation in nonlinear state-space models

Directory of Open Access Journals (Sweden)

Dag Ljungquist

1994-04-01

Full Text Available Several recursive algorithms for online, combined state and parameter estimation in nonlinear state-space models are discussed in this paper. Well-known algorithms such as the extended Kalman filter and alternative formulations of the recursive prediction error method are included, as well as a new method based on a line-search strategy. A comparison of the algorithms illustrates that they are very similar although the differences can be important for the online tracking capabilities and robustness. Simulation experiments on a simple nonlinear process show that the performance under certain conditions can be improved by including a line-search strategy.

Influence of yellow rust infection on /sup 32/P transport in detached barley leaves

Energy Technology Data Exchange (ETDEWEB)

Schubert, J. (Akademie der Landwirtschaftswissenschaften der DDR, Aschersleben. Inst. fuer Phytopathologie)

1982-01-01

Several barley cultivars (Hordeum vulgare L.) differing in their resistance to yellow rust (Puccinia striiformis West.) were tested for relationships between changes of /sup 32/P transport in detached leaves and resistance to yellow rust disease. Investigation carried out with detached second leaves from plants infected at their first leaf revealed a matter transport in these leaves changed by the infection. Transport was also influenced by inoculation with yellow rust uredospores. In that case rust infection influenced the basipetal transport less strongly in resistent plants than in susceptible ones. Connected with the findings the influence of fungal substances on transport processes is discussed in general.

The work ability index and single-item question: associations with sick leave, symptoms, and health--a prospective study of women on long-term sick leave.

Science.gov (United States)

Ahlstrom, Linda; Grimby-Ekman, Anna; Hagberg, Mats; Dellve, Lotta

2010-09-01

This study investigated the association between the work ability index (WAI) and the single-item question on work ability among women working in human service organizations (HSO) currently on long-term sick leave. It also examined the association between the WAI and the single-item question in relation to sick leave, symptoms, and health. Predictive values of the WAI, the changed WAI, the single-item question and the changed single-item question were investigated for degree of sick leave, symptoms, and health. This cohort study comprised 324 HSO female workers on long-term (>60 days) sick leave, with follow-ups at 6 and 12 months. Participants responded to questionnaires. Data on work ability, sick leave, health, and symptoms were analyzed with regard to associations and predictability. Spearman correlation and mixed-model analysis were performed for repeated measurements over time. The study showed a very strong association between the WAI and the single-item question among all participants. Both the WAI and the single-item question showed similar patterns of associations with sick leave, health, and symptoms. The predictive value for the degree of sick leave and health-related quality of life (HRQoL) was strong for both the WAI and the single-item question, and slightly less strong for vitality, neck pain, both self-rated general and mental health, and behavioral and current stress. This study suggests that the single-item question on work ability could be used as a simple indicator for assessing the status and progress of work ability among women on long-term sick leave.

Refractive Errors in Primary School Children in Nigeria | Faderin ...

African Journals Online (AJOL)

The study was carried out to determine the prevalence of refractive errors in primary school children in the Nigerian Army children school. Bonny Camp, Lagos, Nigeria. A total of 919 pupils from two primary schools (one private school and one public school) were screened. The schools and classes were selected using ...

Impossibility of Classically Simulating One-Clean-Qubit Model with Multiplicative Error

Science.gov (United States)

Fujii, Keisuke; Kobayashi, Hirotada; Morimae, Tomoyuki; Nishimura, Harumichi; Tamate, Shuhei; Tani, Seiichiro

2018-05-01

The one-clean-qubit model (or the deterministic quantum computation with one quantum bit model) is a restricted model of quantum computing where all but a single input qubits are maximally mixed. It is known that the probability distribution of measurement results on three output qubits of the one-clean-qubit model cannot be classically efficiently sampled within a constant multiplicative error unless the polynomial-time hierarchy collapses to the third level [T. Morimae, K. Fujii, and J. F. Fitzsimons, Phys. Rev. Lett. 112, 130502 (2014), 10.1103/PhysRevLett.112.130502]. It was open whether we can keep the no-go result while reducing the number of output qubits from three to one. Here, we solve the open problem affirmatively. We also show that the third-level collapse of the polynomial-time hierarchy can be strengthened to the second-level one. The strengthening of the collapse level from the third to the second also holds for other subuniversal models such as the instantaneous quantum polynomial model [M. Bremner, R. Jozsa, and D. J. Shepherd, Proc. R. Soc. A 467, 459 (2011), 10.1098/rspa.2010.0301] and the boson sampling model [S. Aaronson and A. Arkhipov, STOC 2011, p. 333]. We additionally study the classical simulatability of the one-clean-qubit model with further restrictions on the circuit depth or the gate types.

Mismatch Negativity Encoding of Prediction Errors Predicts S-ketamine-Induced Cognitive Impairments

Science.gov (United States)

Schmidt, André; Bachmann, Rosilla; Kometer, Michael; Csomor, Philipp A; Stephan, Klaas E; Seifritz, Erich; Vollenweider, Franz X

2012-01-01

Psychotomimetics like the N-methyl--aspartate receptor (NMDAR) antagonist ketamine and the 5-hydroxytryptamine2A receptor (5-HT2AR) agonist psilocybin induce psychotic symptoms in healthy volunteers that resemble those of schizophrenia. Recent theories of psychosis posit that aberrant encoding of prediction errors (PE) may underlie the expression of psychotic symptoms. This study used a roving mismatch negativity (MMN) paradigm to investigate whether the encoding of PE is affected by pharmacological manipulation of NMDAR or 5-HT2AR, and whether the encoding of PE under placebo can be used to predict drug-induced symptoms. Using a double-blind within-subject placebo-controlled design, S-ketamine and psilocybin, respectively, were administrated to two groups of healthy subjects. Psychological alterations were assessed using a revised version of the Altered States of Consciousness (ASC-R) questionnaire. As an index of PE, we computed changes in MMN amplitudes as a function of the number of preceding standards (MMN memory trace effect) during a roving paradigm. S-ketamine, but not psilocybin, disrupted PE processing as expressed by a frontally disrupted MMN memory trace effect. Although both drugs produced positive-like symptoms, the extent of PE processing under placebo only correlated significantly with the severity of cognitive impairments induced by S-ketamine. Our results suggest that the NMDAR, but not the 5-HT2AR system, is implicated in PE processing during the MMN paradigm, and that aberrant PE signaling may contribute to the formation of cognitive impairments. The assessment of the MMN memory trace in schizophrenia may allow detecting early phases of the illness and might also serve to assess the efficacy of novel pharmacological treatments, in particular of cognitive impairments. PMID:22030715

Uncertainty in predictions of forest carbon dynamics: separating driver error from model error.

Science.gov (United States)

Spadavecchia, L; Williams, M; Law, B E

2011-07-01

We present an analysis of the relative magnitude and contribution of parameter and driver uncertainty to the confidence intervals on estimates of net carbon fluxes. Model parameters may be difficult or impractical to measure, while driver fields are rarely complete, with data gaps due to sensor failure and sparse observational networks. Parameters are generally derived through some optimization method, while driver fields may be interpolated from available data sources. For this study, we used data from a young ponderosa pine stand at Metolius, Central Oregon, and a simple daily model of coupled carbon and water fluxes (DALEC). An ensemble of acceptable parameterizations was generated using an ensemble Kalman filter and eddy covariance measurements of net C exchange. Geostatistical simulations generated an ensemble of meteorological driving variables for the site, consistent with the spatiotemporal autocorrelations inherent in the observational data from 13 local weather stations. Simulated meteorological data were propagated through the model to derive the uncertainty on the CO2 flux resultant from driver uncertainty typical of spatially extensive modeling studies. Furthermore, the model uncertainty was partitioned between temperature and precipitation. With at least one meteorological station within 25 km of the study site, driver uncertainty was relatively small ( 10% of the total net flux), while parameterization uncertainty was larger, 50% of the total net flux. The largest source of driver uncertainty was due to temperature (8% of the total flux). The combined effect of parameter and driver uncertainty was 57% of the total net flux. However, when the nearest meteorological station was > 100 km from the study site, uncertainty in net ecosystem exchange (NEE) predictions introduced by meteorological drivers increased by 88%. Precipitation estimates were a larger source of bias in NEE estimates than were temperature estimates, although the biases partly

Utilizing measure-based feedback in control-mastery theory: A clinical error.

Science.gov (United States)

Snyder, John; Aafjes-van Doorn, Katie

2016-09-01

Clinical errors and ruptures are an inevitable part of clinical practice. Often times, therapists are unaware that a clinical error or rupture has occurred, leaving no space for repair, and potentially leading to patient dropout and/or less effective treatment. One way to overcome our blind spots is by frequently and systematically collecting measure-based feedback from the patient. Patient feedback measures that focus on the process of psychotherapy such as the Patient's Experience of Attunement and Responsiveness scale (PEAR) can be used in conjunction with treatment outcome measures such as the Outcome Questionnaire 45.2 (OQ-45.2) to monitor the patient's therapeutic experience and progress. The regular use of these types of measures can aid clinicians in the identification of clinical errors and the associated patient deterioration that might otherwise go unnoticed and unaddressed. The current case study describes an instance of clinical error that occurred during the 2-year treatment of a highly traumatized young woman. The clinical error was identified using measure-based feedback and subsequently understood and addressed from the theoretical standpoint of the control-mastery theory of psychotherapy. An alternative hypothetical response is also presented and explained using control-mastery theory. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors

Science.gov (United States)

Pernot, Pascal; Savin, Andreas

2018-06-01

Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulative distribution function of unsigned errors, namely, (1) the probability for a new calculation to have an absolute error below a chosen threshold and (2) the maximal amplitude of errors one can expect with a chosen high confidence level. Those statistics are also shown to be well suited for benchmarking and ranking studies. Moreover, the standard error on all benchmarking statistics depends on the size of the reference dataset. Systematic publication of these standard errors would be very helpful to assess the statistical reliability of benchmarking conclusions.

Two heads are better than one: the association between condom decision-making and condom use errors and problems.

Science.gov (United States)

Crosby, R; Milhausen, R; Sanders, S A; Graham, C A; Yarber, W L

2008-06-01

This exploratory study compared the frequency of condom use errors and problems between men reporting that condom use for penile-vaginal sex was a mutual decision compared with men making the decision unilaterally. Nearly 2000 people completed a web-based questionnaire. A sub-sample of 660 men reporting that they last used a condom for penile-vaginal sex (within the past three months) was analysed. Nine condom use errors/problems were assessed. Multivariate analyses controlled for men's age, marital status, and level of experience using condoms. Men's unilateral decision-making was associated with increased odds of removing condoms before sex ended (adjusted odds ratio (AOR) 2.51, p = 0.002), breakage (AOR 3.90, p = 0.037), and slippage during withdrawal (AOR 2.04, p = 0.019). Men's self-reported level of experience using condoms was significantly associated with seven out of nine errors/problems, with those indicating less experience consistently reporting more errors/problems. Findings suggest that female involvement in the decision to use condoms for penile-vaginal sex may be partly protective against some condom errors/problems. Men's self-reported level of experience using condoms may be a useful indicator of the need for education designed to promote the correct use of condoms. Education programmes may benefit men by urging them to involve their female partner in condom use decisions.

The use of source memory to identify one's own episodic confusion errors.

Science.gov (United States)

Smith, S M; Tindell, D R; Pierce, B H; Gilliland, T R; Gerkens, D R

2001-03-01

In 4 category cued recall experiments, participants falsely recalled nonlist common members, a semantic confusion error. Errors were more likely if critical nonlist words were presented on an incidental task, causing source memory failures called episodic confusion errors. Participants could better identify the source of falsely recalled words if they had deeply processed the words on the incidental task. For deep but not shallow processing, participants could reliably include or exclude incidentally shown category members in recall. The illusion that critical items actually appeared on categorized lists was diminished but not eradicated when participants identified episodic confusion errors post hoc among their own recalled responses; participants often believed that critical items had been on both the incidental task and the study list. Improved source monitoring can potentially mitigate episodic (but not semantic) confusion errors.

Employer Provisions for Parental Leave.

Science.gov (United States)

Meisenheimer, Joseph R., II

1989-01-01

Slightly more than one-third of full-time employees in medium and large firms in private industry were covered by maternity- or paternity-leave policies; days off were usually leave without pay. (Author)

Evaluation of Data with Systematic Errors

International Nuclear Information System (INIS)

Froehner, F. H.

2003-01-01

Application-oriented evaluated nuclear data libraries such as ENDF and JEFF contain not only recommended values but also uncertainty information in the form of 'covariance' or 'error files'. These can neither be constructed nor utilized properly without a thorough understanding of uncertainties and correlations. It is shown how incomplete information about errors is described by multivariate probability distributions or, more summarily, by covariance matrices, and how correlations are caused by incompletely known common errors. Parameter estimation for the practically most important case of the Gaussian distribution with common errors is developed in close analogy to the more familiar case without. The formalism shows that, contrary to widespread belief, common ('systematic') and uncorrelated ('random' or 'statistical') errors are to be added in quadrature. It also shows explicitly that repetition of a measurement reduces mainly the statistical uncertainties but not the systematic ones. While statistical uncertainties are readily estimated from the scatter of repeatedly measured data, systematic uncertainties can only be inferred from prior information about common errors and their propagation. The optimal way to handle error-affected auxiliary quantities ('nuisance parameters') in data fitting and parameter estimation is to adjust them on the same footing as the parameters of interest and to integrate (marginalize) them out of the joint posterior distribution afterward

Factors Predicting Ethiopian Anesthetists' Intention to Leave Their Job

NARCIS (Netherlands)

Kols, Adrienne; Kibwana, Sharon; Molla, Yohannes; Ayalew, Firew; Teshome, Mihereteab; van Roosmalen, Jos; Stekelenburg, Jelle

BACKGROUND: Ethiopia has rapidly expanded training programs for associate clinician anesthetists in order to address shortages of anesthesia providers. However, retaining them in the public health sector has proven challenging. This study aimed to determine anesthetists' intentions to leave their

Laboratory errors and patient safety.

Science.gov (United States)

Miligy, Dawlat A

2015-01-01

Laboratory data are extensively used in medical practice; consequently, laboratory errors have a tremendous impact on patient safety. Therefore, programs designed to identify and reduce laboratory errors, as well as, setting specific strategies are required to minimize these errors and improve patient safety. The purpose of this paper is to identify part of the commonly encountered laboratory errors throughout our practice in laboratory work, their hazards on patient health care and some measures and recommendations to minimize or to eliminate these errors. Recording the encountered laboratory errors during May 2008 and their statistical evaluation (using simple percent distribution) have been done in the department of laboratory of one of the private hospitals in Egypt. Errors have been classified according to the laboratory phases and according to their implication on patient health. Data obtained out of 1,600 testing procedure revealed that the total number of encountered errors is 14 tests (0.87 percent of total testing procedures). Most of the encountered errors lay in the pre- and post-analytic phases of testing cycle (representing 35.7 and 50 percent, respectively, of total errors). While the number of test errors encountered in the analytic phase represented only 14.3 percent of total errors. About 85.7 percent of total errors were of non-significant implication on patients health being detected before test reports have been submitted to the patients. On the other hand, the number of test errors that have been already submitted to patients and reach the physician represented 14.3 percent of total errors. Only 7.1 percent of the errors could have an impact on patient diagnosis. The findings of this study were concomitant with those published from the USA and other countries. This proves that laboratory problems are universal and need general standardization and bench marking measures. Original being the first data published from Arabic countries that

Synchronous front-face fluorescence spectroscopy for authentication of the adulteration of edible vegetable oil with refined used frying oil.

Science.gov (United States)

Tan, Jin; Li, Rong; Jiang, Zi-Tao; Tang, Shu-Hua; Wang, Ying; Shi, Meng; Xiao, Yi-Qian; Jia, Bin; Lu, Tian-Xiang; Wang, Hao

2017-02-15

Synchronous front-face fluorescence spectroscopy has been developed for the discrimination of used frying oil (UFO) from edible vegetable oil (EVO), the estimation of the using time of UFO, and the determination of the adulteration of EVO with UFO. Both the heating time of laboratory prepared UFO and the adulteration of EVO with UFO could be determined by partial least squares regression (PLSR). To simulate the EVO adulteration with UFO, for each kind of oil, fifty adulterated samples at the adulterant amounts range of 1-50% were prepared. PLSR was then adopted to build the model and both full (leave-one-out) cross-validation and external validation were performed to evaluate the predictive ability. Under the optimum condition, the plots of observed versus predicted values exhibited high linearity (R(2)>0.96). The root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) were both lower than 3%. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures.

Science.gov (United States)

Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent

2016-04-01

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Characterization of Melanogenesis Inhibitory Constituents of Morus alba Leaves and Optimization of Extraction Conditions Using Response Surface Methodology.

Science.gov (United States)

Jeong, Ji Yeon; Liu, Qing; Kim, Seon Beom; Jo, Yang Hee; Mo, Eun Jin; Yang, Hyo Hee; Song, Dae Hye; Hwang, Bang Yeon; Lee, Mi Kyeong

2015-05-14

Melanin is a natural pigment that plays an important role in the protection of skin, however, hyperpigmentation cause by excessive levels of melatonin is associated with several problems. Therefore, melanogenesis inhibitory natural products have been developed by the cosmetic industry as skin medications. The leaves of Morus alba (Moraceae) have been reported to inhibit melanogenesis, therefore, characterization of the melanogenesis inhibitory constituents of M. alba leaves was attempted in this study. Twenty compounds including eight benzofurans, 10 flavonoids, one stilbenoid and one chalcone were isolated from M. alba leaves and these phenolic constituents were shown to significantly inhibit tyrosinase activity and melanin content in B6F10 melanoma cells. To maximize the melanogenesis inhibitory activity and active phenolic contents, optimized M. alba leave extraction conditions were predicted using response surface methodology as a methanol concentration of 85.2%; an extraction temperature of 53.2 °C and an extraction time of 2 h. The tyrosinase inhibition and total phenolic content under optimal conditions were found to be 74.8% inhibition and 24.8 μg GAE/mg extract, which were well-matched with the predicted values of 75.0% inhibition and 23.8 μg GAE/mg extract. These results shall provide useful information about melanogenesis inhibitory constituents and optimized extracts from M. alba leaves as cosmetic therapeutics to reduce skin hyperpigmentation.

Characterization of Melanogenesis Inhibitory Constituents of Morus alba Leaves and Optimization of Extraction Conditions Using Response Surface Methodology

Directory of Open Access Journals (Sweden)

Ji Yeon Jeong

2015-05-01

Full Text Available Melanin is a natural pigment that plays an important role in the protection of skin, however, hyperpigmentation cause by excessive levels of melatonin is associated with several problems. Therefore, melanogenesis inhibitory natural products have been developed by the cosmetic industry as skin medications. The leaves of Morus alba (Moraceae have been reported to inhibit melanogenesis, therefore, characterization of the melanogenesis inhibitory constituents of M. alba leaves was attempted in this study. Twenty compounds including eight benzofurans, 10 flavonoids, one stilbenoid and one chalcone were isolated from M. alba leaves and these phenolic constituents were shown to significantly inhibit tyrosinase activity and melanin content in B6F10 melanoma cells. To maximize the melanogenesis inhibitory activity and active phenolic contents, optimized M. alba leave extraction conditions were predicted using response surface methodology as a methanol concentration of 85.2%; an extraction temperature of 53.2 °C and an extraction time of 2 h. The tyrosinase inhibition and total phenolic content under optimal conditions were found to be 74.8% inhibition and 24.8 μg GAE/mg extract, which were well-matched with the predicted values of 75.0% inhibition and 23.8 μg GAE/mg extract. These results shall provide useful information about melanogenesis inhibitory constituents and optimized extracts from M. alba leaves as cosmetic therapeutics to reduce skin hyperpigmentation.

Pharmacognostic Investigation of the Leaves and Stems of ...

African Journals Online (AJOL)

Purpose: Some pharmacognostical investigations were carried out on the leaves and stems of Viburnum erubescens Wall.ex DC to record parameters for identifying and differentiating various species of Viburnum. Methods: The research specimens were authenticated and preserved both in fresh and dry forms. The leaves ...

High energy hadron-induced errors in memory chips

Energy Technology Data Exchange (ETDEWEB)

Peterson, R.J. [University of Colorado, Boulder, CO (United States)

2001-09-01

We have measured probabilities for proton, neutron and pion beams from accelerators to induce temporary or soft errors in a wide range of modern 16 Mb and 64 Mb dRAM memory chips, typical of those used in aircraft electronics. Relations among the cross sections for these particles are deduced, and failure rates for aircraft avionics due to cosmic rays are evaluated. Measurement of alpha pha particle yields from pions on aluminum, as a surrogate for silicon, indicate that these reaction products are the proximate cause of the charge deposition resulting in errors. Heavy ions can cause damage to solar panels and other components in satellites above the atmosphere, by the heavy ionization trails they leave. However, at the earth's surface or at aircraft altitude it is known that cosmic rays, other than heavy ions, can cause soft errors in memory circuit components. Soft errors are those confusions between ones and zeroes that cause wrong contents to be stored in the memory, but without causing permanent damage to the circuit. As modern aircraft rely increasingly upon computerized and automated systems, these soft errors are important threats to safety. Protons, neutrons and pions resulting from high energy cosmic ray bombardment of the atmosphere pervade our environment. These particles do not induce damage directly by their ionization loss, but rather by reactions in the materials of the microcircuits. We have measured many cross sections for soft error upsets (SEU) in a broad range of commercial 16 Mb and 64 Mb dRAMs with accelerator beams. Here we define {sigma} SEU = induced errors/number of sample bits x particles/cm{sup 2}. We compare {sigma} SEU to find relations among results for these beams, and relations to reaction cross sections in order to systematize effects. We have modelled cosmic ray effects upon the components we have studied. (Author)

High energy hadron-induced errors in memory chips

International Nuclear Information System (INIS)

Peterson, R.J.

2001-01-01

We have measured probabilities for proton, neutron and pion beams from accelerators to induce temporary or soft errors in a wide range of modern 16 Mb and 64 Mb dRAM memory chips, typical of those used in aircraft electronics. Relations among the cross sections for these particles are deduced, and failure rates for aircraft avionics due to cosmic rays are evaluated. Measurement of alpha pha particle yields from pions on aluminum, as a surrogate for silicon, indicate that these reaction products are the proximate cause of the charge deposition resulting in errors. Heavy ions can cause damage to solar panels and other components in satellites above the atmosphere, by the heavy ionization trails they leave. However, at the earth's surface or at aircraft altitude it is known that cosmic rays, other than heavy ions, can cause soft errors in memory circuit components. Soft errors are those confusions between ones and zeroes that cause wrong contents to be stored in the memory, but without causing permanent damage to the circuit. As modern aircraft rely increasingly upon computerized and automated systems, these soft errors are important threats to safety. Protons, neutrons and pions resulting from high energy cosmic ray bombardment of the atmosphere pervade our environment. These particles do not induce damage directly by their ionization loss, but rather by reactions in the materials of the microcircuits. We have measured many cross sections for soft error upsets (SEU) in a broad range of commercial 16 Mb and 64 Mb dRAMs with accelerator beams. Here we define σ SEU = induced errors/number of sample bits x particles/cm 2 . We compare σ SEU to find relations among results for these beams, and relations to reaction cross sections in order to systematize effects. We have modelled cosmic ray effects upon the components we have studied. (Author)

Consistency errors in p-values reported in Spanish psychology journals.

Science.gov (United States)

Caperos, José Manuel; Pardo, Antonio

2013-01-01

Recent reviews have drawn attention to frequent consistency errors when reporting statistical results. We have reviewed the statistical results reported in 186 articles published in four Spanish psychology journals. Of these articles, 102 contained at least one of the statistics selected for our study: Fisher-F , Student-t and Pearson-c 2 . Out of the 1,212 complete statistics reviewed, 12.2% presented a consistency error, meaning that the reported p-value did not correspond to the reported value of the statistic and its degrees of freedom. In 2.3% of the cases, the correct calculation would have led to a different conclusion than the reported one. In terms of articles, 48% included at least one consistency error, and 17.6% would have to change at least one conclusion. In meta-analytical terms, with a focus on effect size, consistency errors can be considered substantial in 9.5% of the cases. These results imply a need to improve the quality and precision with which statistical results are reported in Spanish psychology journals.

ANALGETIC ACTIVITY OF CEP-CEPAN (Saurauia cauliflora DC. LEAVES EXTRACT

Directory of Open Access Journals (Sweden)

Emil Salim

2017-03-01

Full Text Available Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue damage. The people who live in Karo use several types of plants to relieve pain, one of which is cep-cepan (Saurauia cauliflora DC. The leaves of this plant traditionally used to treat gastrointestinal disorders. There is no scientific evidence about analgetic effect of the leaves. Thus, this study aimed to determine the potential effect of the ethanolic extract of cep-cepan leaves as an analgesic. Fresh Cep-cepan leaves were dried in a drying cabinet at 40°C. Furthermore, the water content of the powdered dried leaves was determined using azeotropic distillation method. Phytochemical screening was carried out to determine chemical groups contained in the dried leaves. Plant extraction was done by maceration method using ethanol 96%. Analgesic effect of the extract was evaluated by observing respon time of mices to infrared as pain inducer. Mices were grouped into six categories, they were: vehicle, antalgin 65 mg/kgBW, and extracts at the dose of 500 mg/kgBW, 250 mg/kgBW, 125 mg/kgBW, 62,5 mg/kgBW, all were administered orally. The data were analayzed using ANOVA followed by LSD. Results showed that the dried leaves contained flavonoids, alkaloids, tannins, anthraquinone glycosides and steroids/triterpenoids. The water contain of the dried leaves was 5,3%. The analgesic test results showed the extract at the dose of 250 mg/kgBW had strong analgesic effect similar to that of 500 mg/kgBW and antalgin 65 mg/kgBW.

Errors and Correction of Precipitation Measurements in China

Institute of Scientific and Technical Information of China (English)

REN Zhihua; LI Mingqin

2007-01-01

In order to discover the range of various errors in Chinese precipitation measurements and seek a correction method, 30 precipitation evaluation stations were set up countrywide before 1993. All the stations are reference stations in China. To seek a correction method for wind-induced error, a precipitation correction instrument called the "horizontal precipitation gauge" was devised beforehand. Field intercomparison observations regarding 29,000 precipitation events have been conducted using one pit gauge, two elevated operational gauges and one horizontal gauge at the above 30 stations. The range of precipitation measurement errors in China is obtained by analysis of intercomparison measurement results. The distribution of random errors and systematic errors in precipitation measurements are studied in this paper.A correction method, especially for wind-induced errors, is developed. The results prove that a correlation of power function exists between the precipitation amount caught by the horizontal gauge and the absolute difference of observations implemented by the operational gauge and pit gauge. The correlation coefficient is 0.99. For operational observations, precipitation correction can be carried out only by parallel observation with a horizontal precipitation gauge. The precipitation accuracy after correction approaches that of the pit gauge. The correction method developed is simple and feasible.

Predictors of Errors of Novice Java Programmers

Science.gov (United States)

Bringula, Rex P.; Manabat, Geecee Maybelline A.; Tolentino, Miguel Angelo A.; Torres, Edmon L.

2012-01-01

This descriptive study determined which of the sources of errors would predict the errors committed by novice Java programmers. Descriptive statistics revealed that the respondents perceived that they committed the identified eighteen errors infrequently. Thought error was perceived to be the main source of error during the laboratory programming…

Dependence of fluence errors in dynamic IMRT on leaf-positional errors varying with time and leaf number

International Nuclear Information System (INIS)

Zygmanski, Piotr; Kung, Jong H.; Jiang, Steve B.; Chin, Lee

2003-01-01

In d-MLC based IMRT, leaves move along a trajectory that lies within a user-defined tolerance (TOL) about the ideal trajectory specified in a d-MLC sequence file. The MLC controller measures leaf positions multiple times per second and corrects them if they deviate from ideal positions by a value greater than TOL. The magnitude of leaf-positional errors resulting from finite mechanical precision depends on the performance of the MLC motors executing leaf motions and is generally larger if leaves are forced to move at higher speeds. The maximum value of leaf-positional errors can be limited by decreasing TOL. However, due to the inherent time delay in the MLC controller, this may not happen at all times. Furthermore, decreasing the leaf tolerance results in a larger number of beam hold-offs, which, in turn leads, to a longer delivery time and, paradoxically, to higher chances of leaf-positional errors (≤TOL). On the other end, the magnitude of leaf-positional errors depends on the complexity of the fluence map to be delivered. Recently, it has been shown that it is possible to determine the actual distribution of leaf-positional errors either by the imaging of moving MLC apertures with a digital imager or by analysis of a MLC log file saved by a MLC controller. This leads next to an important question: What is the relation between the distribution of leaf-positional errors and fluence errors. In this work, we introduce an analytical method to determine this relation in dynamic IMRT delivery. We model MLC errors as Random-Leaf Positional (RLP) errors described by a truncated normal distribution defined by two characteristic parameters: a standard deviation σ and a cut-off value Δx 0 (Δx 0 ∼TOL). We quantify fluence errors for two cases: (i) Δx 0 >>σ (unrestricted normal distribution) and (ii) Δx 0 0 --limited normal distribution). We show that an average fluence error of an IMRT field is proportional to (i) σ/ALPO and (ii) Δx 0 /ALPO, respectively, where

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

International Nuclear Information System (INIS)

Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

2017-01-01

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well

Prognostic Factors of Returning to Work after Sick Leave due to Work-Related Common Mental Disorders: A One- and Three-Year Follow-Up Study.

Science.gov (United States)

Netterstrøm, Bo; Eller, Nanna Hurwitz; Borritz, Marianne

2015-01-01

The aim of this paper was to assess the prognostic factors of return to work (RTW) after one and three years among people on sick leave due to occupational stress. Methods. The study population comprised 223 completers on sick leave, who participated in a stress treatment program. Self-reported psychosocial work environment, life events during the past year, severity of the condition, occupational position, employment sector, marital status, and medication were assessed at baseline. RTW was assessed with data from a national compensation database (DREAM). Results. Self-reported high demands, low decision authority, low reward, low support from leaders and colleagues, bullying, high global symptom index, length of sick leave at baseline, and stressful negative life events during the year before baseline were associated with no RTW after one year. Low work ability and full-time sick leave at inclusion were predictors after three years too. Being single was associated with no RTW after three years. The type of treatment, occupational position, gender, age, and degree of depression were not associated with RTW after one or three years. Conclusion. The impact of the psychosocial work environment as predictor for RTW disappeared over time and only the severity of the condition was a predictor for RTW in the long run.

Analysis of Errors in a Special Perturbations Satellite Orbit Propagator

Energy Technology Data Exchange (ETDEWEB)

Beckerman, M.; Jones, J.P.

1999-02-01

We performed an analysis of error densities for the Special Perturbations orbit propagator using data for 29 satellites in orbits of interest to Space Shuttle and International Space Station collision avoidance. We find that the along-track errors predominate. These errors increase monotonically over each 36-hour prediction interval. The predicted positions in the along-track direction progressively either leap ahead of or lag behind the actual positions. Unlike the along-track errors the radial and cross-track errors oscillate about their nearly zero mean values. As the number of observations per fit interval decline the along-track prediction errors, and amplitudes of the radial and cross-track errors, increase.

Online visual feedback during error-free channel trials leads to active unlearning of movement dynamics: evidence for adaptation to trajectory prediction errors.

Directory of Open Access Journals (Sweden)

Angel Lago-Rodriguez

2016-09-01

Full Text Available Prolonged exposure to movement perturbations leads to creation of motor memories which decay towards previous states when the perturbations are removed. However, it remains unclear whether this decay is due only to a spontaneous and passive recovery of the previous state. It has recently been reported that activation of reinforcement-based learning mechanisms delays the onset of the decay. This raises the question whether other motor learning mechanisms may also contribute to the retention and/or decay of the motor memory. Therefore, we aimed to test whether mechanisms of error-based motor adaptation are active during the decay of the motor memory. Forty-five right-handed participants performed point-to-point reaching movements under an external dynamic perturbation. We measured the expression of the motor memory through error-clamped (EC trials, in which lateral forces constrained movements to a straight line towards the target. We found greater and faster decay of the motor memory for participants who had access to full online visual feedback during these EC trials (Cursor group, when compared with participants who had no EC feedback regarding movement trajectory (Arc group. Importantly, we did not find between-group differences in adaptation to the external perturbation. In addition, we found greater decay of the motor memory when we artificially increased feedback errors through the manipulation of visual feedback (Augmented-Error group. Our results then support the notion of an active decay of the motor memory, suggesting that adaptive mechanisms are involved in correcting for the mismatch between predicted movement trajectories and actual sensory feedback, which leads to greater and faster decay of the motor memory.