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Sample records for conditional prediction intervals

  1. Sensor drift and predicted calibration intervals of handheld temperature and relative humidity meters under residential field-use conditions.

    PubMed

    Johnston James D; Magnusson, Brianna M; Eggett, Dennis; Mumford, Kyle; Collingwood, Scott C; Bernhardt, Scott A

    2014-10-01

    Handheld temperature and relative humidity (T/RH) meters are commonly used in residential indoor air surveys. Although popular, T/RH meters are prone to sensor drift and consequent loss of accuracy, and thus instrument manufacturers often recommend annual calibration and adjustment. Field-use conditions, however, have been shown to accelerate electronic sensor drift in outdoor applications, resulting in out-of-tolerance measurements in less than one year. In the study described in this article, sensor drift was evaluated under residential field use for 30 handheld T/RH meters to predict needed calibration intervals based on hierarchical linear modeling. Instruments were used in 43 home visits over a 93-day period and were calibrated (without adjustment) 49 times over the study period with a laboratory standard. Analysis of covariance showed significant drift among temperature sensors for all three instrument types (p < .0001) and among humidity sensors in two instruments. The authors' study suggests calibration frequency should be based on instrument performance under specific sampling conditions rather than on predetermined time intervals.

  2. Evaluating long-term relationship of protein sequence by use of D-interval conditional probability and its impact on protein structural class prediction.

    PubMed

    Gu, Fei; Chen, Hang

    2009-01-01

    To fix the large and expanding gap between sequence known proteins and structure known proteins, it is important to study on protein structural class prediction (PSCP) for its foundation and usefulness in protein structure analysis. In this paper, the d-interval conditional probability index was proposed to reflect the long-term correlation between amino acids. Based on this index, the impact of residues' long-term relationship on PSCP was analyzed. Two new information theory based algorithms were proposed and were used combining with the long-term information between residues to predict protein structural class (PSC). The dataset 5714 was tested for its low sequence similarity and high reliability. The result showed that the new index was 3-6% higher than traditional index by use of the same algorithms, and the PSCP accuracy was 4-10% improved using the new algorithms. The presented index, algorithms and the long-term relationship of residues on PSCP can be extensively applied in other sequence based protein structure analysis.

  3. Reliable prediction intervals with regression neural networks.

    PubMed

    Papadopoulos, Harris; Haralambous, Haris

    2011-10-01

    This paper proposes an extension to conventional regression neural networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, called Conformal Prediction (CP), for assigning reliable confidence measures to predictions without assuming anything more than that the data are independent and identically distributed (i.i.d.). We evaluate the proposed method on four benchmark datasets and on the problem of predicting Total Electron Content (TEC), which is an important parameter in trans-ionospheric links; for the latter we use a dataset of more than 60000 TEC measurements collected over a period of 11 years. Our experimental results show that the prediction intervals produced by our method are both well calibrated and tight enough to be useful in practice. Copyright © 2011 Elsevier Ltd. All rights reserved.

  4. Inflation of Conditional Predictions

    ERIC Educational Resources Information Center

    Koriat, Asher; Fiedler, Klaus; Bjork, Robert A.

    2006-01-01

    The authors report 7 experiments indicating that conditional predictions--the assessed probability that a certain outcome will occur given a certain condition--tend to be markedly inflated. The results suggest that this inflation derives in part from backward activation in which the target outcome highlights aspects of the condition that are…

  5. Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

    NASA Technical Reports Server (NTRS)

    Kumar, Sricharan; Srivistava, Ashok N.

    2012-01-01

    Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

  6. Empirical Bayes interval estimates that are conditionally equal to unadjusted confidence intervals or to default prior credibility intervals.

    PubMed

    Bickel, David R

    2012-02-21

    Problems involving thousands of null hypotheses have been addressed by estimating the local false discovery rate (LFDR). A previous LFDR approach to reporting point and interval estimates of an effect-size parameter uses an estimate of the prior distribution of the parameter conditional on the alternative hypothesis. That estimated prior is often unreliable, and yet strongly influences the posterior intervals and point estimates, causing the posterior intervals to differ from fixed-parameter confidence intervals, even for arbitrarily small estimates of the LFDR. That influence of the estimated prior manifests the failure of the conditional posterior intervals, given the truth of the alternative hypothesis, to match the confidence intervals. Those problems are overcome by changing the posterior distribution conditional on the alternative hypothesis from a Bayesian posterior to a confidence posterior. Unlike the Bayesian posterior, the confidence posterior equates the posterior probability that the parameter lies in a fixed interval with the coverage rate of the coinciding confidence interval. The resulting confidence-Bayes hybrid posterior supplies interval and point estimates that shrink toward the null hypothesis value. The confidence intervals tend to be much shorter than their fixed-parameter counterparts, as illustrated with gene expression data. Simulations nonetheless confirm that the shrunken confidence intervals cover the parameter more frequently than stated. Generally applicable sufficient conditions for correct coverage are given. In addition to having those frequentist properties, the hybrid posterior can also be motivated from an objective Bayesian perspective by requiring coherence with some default prior conditional on the alternative hypothesis. That requirement generates a new class of approximate posteriors that supplement Bayes factors modified for improper priors and that dampen the influence of proper priors on the credibility intervals. While

  7. Plea for routinely presenting prediction intervals in meta-analysis.

    PubMed

    IntHout, Joanna; Ioannidis, John P A; Rovers, Maroeska M; Goeman, Jelle J

    2016-07-12

    Evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like τ(2) or I(2), but their clinical interpretation is not straightforward. A prediction interval is less complicated: it presents the expected range of true effects in similar studies. We aimed to show the advantages of having the prediction interval routinely reported in meta-analyses. We show how the prediction interval can help understand the uncertainty about whether an intervention works or not. To evaluate the implications of using this interval to interpret the results, we selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009-2013 with a dichotomous (n=2009) or continuous (n=1254) outcome, and generated 95% prediction intervals for them. In 72.4% of 479 statistically significant (random-effects p<0.05) meta-analyses in the Cochrane Database 2009-2013 with heterogeneity (I(2)>0), the 95% prediction interval suggested that the intervention effect could be null or even be in the opposite direction. In 20.3% of those 479 meta-analyses, the prediction interval showed that the effect could be completely opposite to the point estimate of the meta-analysis. We demonstrate also how the prediction interval can be used to calculate the probability that a new trial will show a negative effect and to improve the calculations of the power of a new trial. The prediction interval reflects the variation in treatment effects over different settings, including what effect is to be expected in future patients, such as the patients that a clinician is interested to treat. Prediction intervals should be routinely reported to allow more informative inferences in meta-analyses. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

  8. Measurement of Phonated Intervals during Four Fluency-Inducing Conditions

    ERIC Educational Resources Information Center

    Davidow, Jason H.; Bothe, Anne K.; Andreatta, Richard D.; Ye, Jun

    2009-01-01

    Purpose: Previous investigations of persons who stutter have demonstrated changes in vocalization variables during fluency-inducing conditions (FICs). A series of studies has also shown that a reduction in short intervals of phonation, those from 30 to 200 ms, is associated with decreased stuttering. The purpose of this study, therefore, was to…

  9. Prediction of the confidence interval of quantitative trait Loci location.

    PubMed

    Visscher, Peter M; Goddard, Mike E

    2004-07-01

    In 1997, Darvasi and Soller presented empirical predictions of the confidence interval of quantitative trait loci (QTL) location for dense marker maps in experimental crosses. They showed from simulation results for backcross and F2 populations from inbred lines that the 95% confidence interval was a simple function of sample size and the effect of the QTL. In this study, we derive by theory simple equations that can be used to predict any confidence interval and show that for the 95% confidence interval, they are in good agreement with the empirical results given by Darvasi and Soller. A general form of the confidence interval is given that also applies to other population structures (e.g., collections of sib pairs). Furthermore, the expected shape of the likelihood-ratio-test around the true QTL location is derived, which is shown to be extremely leptokurtic. It is shown that this shape explains why confidence intervals from the Log of Odds (LOD) drop-off method and bootstrap results frequently differ for real data sets.

  10. Hourly Wind Speed Interval Prediction in Arid Regions

    NASA Astrophysics Data System (ADS)

    Chaouch, M.; Ouarda, T.

    2013-12-01

    context, probabilistic forecasts might be more relevant than point forecasts for the planner to build scenarios In this paper, we are interested in estimating predictive intervals of the hourly wind speed measures in few cities in United Arab emirates (UAE). More precisely, given a wind speed time series, our target is to forecast the wind speed at any specific hour during the day and provide in addition an interval with the coverage probability 0interval we need to estimate the lower band (resp. upper band) which corresponds to the (1-p)/2-th (resp. (1+p)/2-th) conditional quantile. In this paper, a kernel-smoothed estimator of the conditional quantiles is introduced. The proposed non-parametric approach has many advantages since it is flexible because it does not need a specification of the model to work with (such as normal distribution or a linear relation). Here, we use a covariable that is correlated to the wind speed. In practice, many possible choices of the covariate are available. In fact, in addition to its historical data, the wind speed is highly correlated to temperature, humidity and wind direction. In this paper a comparison, in terms of Mean Absolute Prediction Errors and Interquartile Range, between those choices will be provided to show which covariates are more suitable to forecast wind speed.

  11. FORECASTING WITH PREDICTION INTERVALS FOR PERIODIC ARMA MODELS

    PubMed Central

    Anderson, Paul L.; Meerschaert, Mark M.; Zhang, Kai

    2012-01-01

    Periodic autoregressive moving average (PARMA) models are indicated for time series whose mean, variance, and covariance function vary with the season. In this paper, we develop and implement forecasting procedures for PARMA models. Forecasts are developed using the innovations algorithm, along with an idea of Ansley. A formula for the asymptotic error variance is provided, so that Gaussian prediction intervals can be computed. Finally, an application to monthly river flow forecasting is given, to illustrate the method. PMID:23956476

  12. How to Avoid Errors in Error Propagation: Prediction Intervals and Confidence Intervals in Forest Biomass

    NASA Astrophysics Data System (ADS)

    Lilly, P.; Yanai, R. D.; Buckley, H. L.; Case, B. S.; Woollons, R. C.; Holdaway, R. J.; Johnson, J.

    2016-12-01

    Calculations of forest biomass and elemental content require many measurements and models, each contributing uncertainty to the final estimates. While sampling error is commonly reported, based on replicate plots, error due to uncertainty in the regression used to estimate biomass from tree diameter is usually not quantified. Some published estimates of uncertainty due to the regression models have used the uncertainty in the prediction of individuals, ignoring uncertainty in the mean, while others have propagated uncertainty in the mean while ignoring individual variation. Using the simple case of the calcium concentration of sugar maple leaves, we compare the variation among individuals (the standard deviation) to the uncertainty in the mean (the standard error) and illustrate the declining importance in the prediction of individual concentrations as the number of individuals increases. For allometric models, the analogous statistics are the prediction interval (or the residual variation in the model fit) and the confidence interval (describing the uncertainty in the best fit model). The effect of propagating these two sources of error is illustrated using the mass of sugar maple foliage. The uncertainty in individual tree predictions was large for plots with few trees; for plots with 30 trees or more, the uncertainty in individuals was less important than the uncertainty in the mean. Authors of previously published analyses have reanalyzed their data to show the magnitude of these two sources of uncertainty in scales ranging from experimental plots to entire countries. The most correct analysis will take both sources of uncertainty into account, but for practical purposes, country-level reports of uncertainty in carbon stocks, as required by the IPCC, can ignore the uncertainty in individuals. Ignoring the uncertainty in the mean will lead to exaggerated estimates of confidence in estimates of forest biomass and carbon and nutrient contents.

  13. Ongoing behavior predicts perceptual report of interval duration

    PubMed Central

    Gouvêa, Thiago S.; Monteiro, Tiago; Soares, Sofia; Atallah, Bassam V.; Paton, Joseph J.

    2014-01-01

    The ability to estimate the passage of time is essential for adaptive behavior in complex environments. Yet, it is not known how the brain encodes time over the durations necessary to explain animal behavior. Under temporally structured reinforcement schedules, animals tend to develop temporally structured behavior, and interval timing has been suggested to be accomplished by learning sequences of behavioral states. If this is true, trial to trial fluctuations in behavioral sequences should be predictive of fluctuations in time estimation. We trained rodents in an duration categorization task while continuously monitoring their behavior with a high speed camera. Animals developed highly reproducible behavioral sequences during the interval being timed. Moreover, those sequences were often predictive of perceptual report from early in the trial, providing support to the idea that animals may use learned behavioral patterns to estimate the duration of time intervals. To better resolve the issue, we propose that continuous and simultaneous behavioral and neural monitoring will enable identification of neural activity related to time perception that is not explained by ongoing behavior. PMID:24672473

  14. Mapping of Estimations and Prediction Intervals Using Extreme Learning Machines

    NASA Astrophysics Data System (ADS)

    Leuenberger, Michael; Kanevski, Mikhail

    2015-04-01

    Due to the large amount and complexity of data available nowadays in environmental sciences, we face the need to apply more robust methodology allowing analyses and understanding of the phenomena under study. One particular but very important aspect of this understanding is the reliability of generated prediction models. From the data collection to the prediction map, several sources of error can occur and affect the final result. Theses sources are mainly identified as uncertainty in data (data noise), and uncertainty in the model. Their combination leads to the so-called prediction interval. Quantifying these two categories of uncertainty allows a finer understanding of phenomena under study and a better assessment of the prediction accuracy. The present research deals with a methodology combining a machine learning algorithm (ELM - Extreme Learning Machine) with a bootstrap-based procedure. Developed by G.-B. Huang et al. (2006), ELM is an artificial neural network following the structure of a multilayer perceptron (MLP) with one single hidden layer. Compared to classical MLP, ELM has the ability to learn faster without loss of accuracy, and need only one hyper-parameter to be fitted (that is the number of nodes in the hidden layer). The key steps of the proposed method are as following: sample from the original data a variety of subsets using bootstrapping; from these subsets, train and validate ELM models; and compute residuals. Then, the same procedure is performed a second time with only the squared training residuals. Finally, taking into account the two modeling levels allows developing the mean prediction map, the model uncertainty variance, and the data noise variance. The proposed approach is illustrated using geospatial data. References Efron B., and Tibshirani R. 1986, Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical accuracy, Statistical Science, vol. 1: 54-75. Huang G.-B., Zhu Q.-Y., and Siew C.-K. 2006

  15. Evaluation of prediction intervals for expressing uncertainties in groundwater flow model predictions

    USGS Publications Warehouse

    Christensen, S.; Cooley, R.L.

    1999-01-01

    We tested the accuracy of 95% individual prediction intervals for hydraulic heads, streamflow gains, and effective transmissivities computed by groundwater models of two Danish aquifers. To compute the intervals, we assumed that each predicted value can be written as the sum of a computed dependent variable and a random error. Testing was accomplished by using a cross-validation method and by using new field measurements of hydraulic heads and transmissivities that were not used to develop or calibrate the models. The tested null hypotheses are that the coverage probability of the prediction intervals is not significantly smaller than the assumed probability (95%) and that each tail probability is not significantly different from the assumed probability (2.5%). In all cases tested, these hypotheses were accepted at the 5% level of significance. We therefore conclude that for the groundwater models of two real aquifers the individual prediction intervals appear to be accurate.We tested the accuracy of 95% individual prediction intervals for hydraulic heads, streamflow gains, and effective transmissivities computed by groundwater models of two Danish aquifers. To compute the intervals, we assumed that each predicted value can be written as the sum of a computed dependent variable and a random error. Testing was accomplished by using a cross-validation method and by using new field measurements of hydraulic heads and transmissivities that were not used to develop or calibrate the models. The tested null hypotheses are that the coverage probability of the prediction intervals is not significantly smaller than the assumed probability (95%) and that each tail probability is not significantly different from the assumed probability (2.5%). In all cases tested, these hypotheses were accepted at the 5% level of significance. We therefore conclude that for the groundwater models of two real aquifers the individual prediction intervals appear to be accurate.

  16. Trial order and retention interval in human predictive judgment.

    PubMed

    Stout, Steven C; Amundson, Jeffrey C; Miller, Ralph R

    2005-12-01

    The influences of order of trial type and retention interval on human predictive judgments were assessed for a cue that was reinforced on half of its training presentations. Subjects observed 10 cue-outcome presentations (i.e., reinforced trials) and 10 cue-alone presentations (i.e., nonreinforced trials) in one of three different orders: all nonreinforced trials followed by all reinforced trials(latent inhibition), reinforced and nonreinforced trials interspersed (partial reinforcement), or al lreinforced trials followed by all nonreinforced trials (extinction). Ratings were based mainly on the most recent event type (i.e., a recency effect) when the test occurred immediately after training but were based mainly on initial event types (i.e., a primacy effect) when the test occurred after a 48-h delay. The subjects tested both immediately and with a long retention interval did not exhibit this shift to primacy (i.e., the recency effect persisted). These results demonstrate noncatastrophic forgetting and the flexible use of trial order information in predictive judgments.

  17. Fear potentiated startle at short intervals following conditioned stimulus onset during delay but not trace conditioning.

    PubMed

    Asli, Ole; Kulvedrøsten, Silje; Solbakken, Line E; Flaten, Magne Arve

    2009-07-01

    The latency of conditioned fear after delay and trace conditioning was investigated. Some argue that delay conditioning is not dependent on awareness. In contrast, trace conditioning, where there is a gap between the conditioned stimulus (CS) and the unconditioned stimulus (US), is assumed to be dependent on awareness. In the present study, a tone CS signaled a noise US presented 1000 ms after CS onset in the delay conditioning group. In the trace conditioning group, a 200-ms tone CS was followed by an 800-ms gap prior to US presentation. Fear-potentiated startle should be seen at shorter intervals after delay conditioning compared to trace conditioning. Analyses showed increased startle at 30, 50, 100, and 150 ms after CS onset following delay conditioning compared to trace conditioning. This implies that fear-relevant stimuli elicit physiological reactions before extended processing of the stimuli occur, following delay, but not trace conditioning.

  18. Predicting Driver Behavior during the Yellow Interval Using Video Surveillance.

    PubMed

    Li, Juan; Jia, Xudong; Shao, Chunfu

    2016-12-06

    At a signalized intersection, drivers must make a stop/go decision at the onset of the yellow signal. Incorrect decisions would lead to red light running (RLR) violations or crashes. This study aims to predict drivers' stop/go decisions and RLR violations during yellow intervals. Traffic data such as vehicle approaching speed, acceleration, distance to the intersection, and occurrence of RLR violations are gathered by a Vehicle Data Collection System (VDCS). An enhanced Gaussian Mixture Model (GMM) is used to extract moving vehicles from target lanes, and the Kalman Filter (KF) algorithm is utilized to acquire vehicle trajectories. The data collected from the VDCS are further analyzed by a sequential logit model, and the relationship between drivers' stop/go decisions and RLR violations is identified. The results indicate that the distance of vehicles to the stop line at the onset of the yellow signal is an important predictor for both drivers' stop/go decisions and RLR violations. In addition, vehicle approaching speed is a contributing factor for stop/go decisions. Furthermore, the accelerations of vehicles after the onset of the yellow signal are positively related to RLR violations. The findings of this study can be used to predict the probability of drivers' RLR violations and improve traffic safety at signalized intersections.

  19. Predicting Driver Behavior during the Yellow Interval Using Video Surveillance

    PubMed Central

    Li, Juan; Jia, Xudong; Shao, Chunfu

    2016-01-01

    At a signalized intersection, drivers must make a stop/go decision at the onset of the yellow signal. Incorrect decisions would lead to red light running (RLR) violations or crashes. This study aims to predict drivers’ stop/go decisions and RLR violations during yellow intervals. Traffic data such as vehicle approaching speed, acceleration, distance to the intersection, and occurrence of RLR violations are gathered by a Vehicle Data Collection System (VDCS). An enhanced Gaussian Mixture Model (GMM) is used to extract moving vehicles from target lanes, and the Kalman Filter (KF) algorithm is utilized to acquire vehicle trajectories. The data collected from the VDCS are further analyzed by a sequential logit model, and the relationship between drivers’ stop/go decisions and RLR violations is identified. The results indicate that the distance of vehicles to the stop line at the onset of the yellow signal is an important predictor for both drivers’ stop/go decisions and RLR violations. In addition, vehicle approaching speed is a contributing factor for stop/go decisions. Furthermore, the accelerations of vehicles after the onset of the yellow signal are positively related to RLR violations. The findings of this study can be used to predict the probability of drivers’ RLR violations and improve traffic safety at signalized intersections. PMID:27929447

  20. Predicting fecal coliform using the interval-to-interval approach and SWAT in the Miyun watershed, China.

    PubMed

    Bai, Jianwen; Shen, Zhenyao; Yan, Tiezhu; Qiu, Jiali; Li, Yangyang

    2017-06-01

    Pathogens in manure can cause waterborne-disease outbreaks, serious illness, and even death in humans. Therefore, information about the transformation and transport of bacteria is crucial for determining their source. In this study, the Soil and Water Assessment Tool (SWAT) was applied to simulate fecal coliform bacteria load in the Miyun Reservoir watershed, China. The data for the fecal coliform were obtained at three sampling sites, Chenying (CY), Gubeikou (GBK), and Xiahui (XH). The calibration processes of the fecal coliform were conducted using the CY and GBK sites, and validation was conducted at the XH site. An interval-to-interval approach was designed and incorporated into the processes of fecal coliform calibration and validation. The 95% confidence interval of the predicted values and the 95% confidence interval of measured values were considered during calibration and validation in the interval-to-interval approach. Compared with the traditional point-to-point comparison, this method can improve simulation accuracy. The results indicated that the simulation of fecal coliform using the interval-to-interval approach was reasonable for the watershed. This method could provide a new research direction for future model calibration and validation studies.

  1. Conditional replenishment using motion prediction

    NASA Technical Reports Server (NTRS)

    Hein, D. N.; Jones, H. W., Jr.

    1979-01-01

    Conditional replenishment is an interframe video compression method that uses correlation in time to reduce video transmission rates. This method works by detecting and sending only the changing portions of the image and by having the receiver use the video data from the previous frame for the non-changing portion. The amount of compression that can be achieved through this technique depends to a large extent on the rate of change within the image, and can vary from 10 to 1 to less than 2 to 1. An additional 3 to 1 reduction in rate is obtained by the intraframe coding of data blocks using a 2-dimensional variable rate Hadamard transform coder. A further additional 2 to 1 rate reduction is achieved by using motion prediction. Motion prediction works by measuring the relative displacements of a subpicture from one frame to the next. The subpicture can then be transmitted by sending only the value of the 2-dimensional displacement. Computer simulations have demonstrated that data rates of 2 to 4 Mega-bits/second can be achieved while still retaining good fidelity in the image.

  2. Dorsal hippocampus involvement in trace fear conditioning with long, but not short, trace intervals in mice.

    PubMed

    Chowdhury, Najwa; Quinn, Jennifer J; Fanselow, Michael S

    2005-10-01

    Placing a "trace" interval between a warning signal and an aversive shock makes consolidation of the memory for trace conditioning hippocampus dependent. To determine the trace at which memory consolidation requires the hippocampus, mice were trained with 0-s, 1-s, 3-s, or 20-s trace intervals and tested for freezing to context and tone. Posttraining dorsal hippocampus (DH) lesions decreased context conditioning regardless of trace interval. However, DH lesions attenuated only the 20-s trace tone freezing. Like eyeblink conditioning, the DH is necessary for trace fear conditioning only at long trace intervals, but the time scale for the effective interval in fear conditioning is about 40 times longer. Manipulations that alter trace fear conditioning with short trace intervals probably do not reflect altered DH function. Given this difference in time scale along with the use of posttraining DH lesions, hippocampus dependency of trace conditioning is not related to a bridging function or response timing.

  3. Predicting spontaneous termination of atrial fibrillation based on the RR interval.

    PubMed

    Sun, R R; Wang, Y Y

    2009-08-01

    It is important to characterize conditions under which atrial fibrillation (AF) is likely to terminate spontaneously. A novel method is proposed here. Eleven features are first extracted to characterize RR interval and Poincaré plot from a statistical viewpoint and a geometric viewpoint respectively. Then sequential forward search (SFS) algorithm is utilized for feature selection. Finally, a fuzzy support vector machine (FSVM) with a new fuzzy membership is applied for AF termination prediction. The method is studied with an AF database of electrocardiogram (ECG) recordings provided by PhysioNet for the Cardiology Challenge 2004. It is divided into a training set and two testing sets (A and B). Experiment results show that 100 per cent of testing set A and 100 per cent of testing set B are correctly classified, together with 92.3 per cent of non-terminating and soon-terminating AF correctly classified. It demonstrates that the proposed method can predict spontaneous termination of AF effectively.

  4. One-Year Real-Time Operational Prediction Intervals for Direct Normal Irradiance

    NASA Astrophysics Data System (ADS)

    Chu, Y.; Carreira Pedro, H. T.; Coimbra, C. F.

    2015-12-01

    This work describes an algorithm to generate intra-hour prediction intervals (PIs) for the highly-variable direct normal irradiance, which is the energy source for the concentrated solar power technologies. The prediction intervals are generated using a Multi-layer Stochastic-Learning Model (MSLM), which is developed based on methods such as: sky imaging techniques, support vector machine and artificial neural network. The MSLM is trained using one year of co-located, high-quality irradiance and sky image recording in Folsom, California. In addition to being validated with historical data, the algorithm has been generating operational PI forecasts in real-time for that observatory since July 1st 2014. In the real-time scenario, without re-training or significant maintenance, the hybrid model consistently provides valid PI (PICP > 92%) and outperforms the reference persistence model (PICP ~ 85%) regardless of weather condition. This work has great impact in the field of solar energy to potentially facilitate the level of solar penetration in the grid with significantly reduced integration costs.

  5. Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K. S.; Cibin, R.; Sudheer, K. P.; Chaubey, I.

    2013-08-01

    This paper presents a method of constructing prediction interval for artificial neural network (ANN) rainfall runoff models during calibration with a consideration of generating ensemble predictions. A two stage optimization procedure is envisaged in this study for construction of prediction interval for the ANN output. In Stage 1, ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector. In Stage 2, possible variability of ANN parameters (obtained in Stage 1) is optimized so as to create an ensemble of models with the consideration of minimum residual variance for the ensemble mean, while ensuring a maximum of the measured data to fall within the estimated prediction interval. The width of the prediction interval is also minimized simultaneously. The method is demonstrated using a real world case study of rainfall runoff data for an Indian basin. The method was able to produce ensembles with a prediction interval (average width) of 26.49 m3/s with 97.17% of the total observed data points lying within the interval in validation. One specific advantage of the method is that when ensemble mean value is considered as a forecast, the peak flows are predicted with improved accuracy by this method compared to traditional single point forecasted ANNs.

  6. CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS.

    USGS Publications Warehouse

    Cooley, Richard L.; Vecchia, Aldo V.

    1987-01-01

    A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that inclusion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.

  7. Timing Rhythms: Perceived Duration Increases with a Predictable Temporal Structure of Short Interval Fillers

    PubMed Central

    Horr, Ninja K.; Di Luca, Massimiliano

    2015-01-01

    Variations in the temporal structure of an interval can lead to remarkable differences in perceived duration. For example, it has previously been shown that isochronous intervals, that is, intervals filled with temporally regular stimuli, are perceived to last longer than intervals left empty or filled with randomly timed stimuli. Characterizing the extent of such distortions is crucial to understanding how duration perception works. One account to explain effects of temporal structure is a non-linear accumulator-counter mechanism reset at the beginning of every subinterval. An alternative explanation based on entrainment to regular stimulation posits that the neural response to each filler stimulus in an isochronous sequence is amplified and a higher neural response may lead to an overestimation of duration. If entrainment is the key that generates response amplification and the distortions in perceived duration, then any form of predictability in the temporal structure of interval fillers should lead to the perception of an interval that lasts longer than a randomly filled one. The present experiments confirm that intervals filled with fully predictable rhythmically grouped stimuli lead to longer perceived duration than anisochronous intervals. No general over- or underestimation is registered for rhythmically grouped compared to isochronous intervals. However, we find that the number of stimuli in each group composing the rhythm also influences perceived duration. Implications of these findings for a non-linear clock model as well as a neural response magnitude account of perceived duration are discussed. PMID:26474047

  8. Human trace fear conditioning: right-lateralized cortical activity supports trace-interval processes.

    PubMed

    Haritha, Abhishek T; Wood, Kimberly H; Ver Hoef, Lawrence W; Knight, David C

    2013-06-01

    Pavlovian conditioning requires the convergence and simultaneous activation of neural circuitry that supports conditioned stimulus (CS) and unconditioned stimulus (US) processes. However, in trace conditioning, the CS and US are separated by a period of time called the trace interval, and thus do not overlap. Therefore, determining brain regions that support associative learning by maintaining a CS representation during the trace interval is an important issue for conditioning research. Prior functional magnetic resonance imaging (fMRI) research has identified brain regions that support trace-conditioning processes. However, relatively little is known about whether this activity is specific to the trace CS, the trace interval, or both periods of time. The present study was designed to disentangle the hemodynamic response produced by the trace CS from that associated with the trace interval, in order to identify learning-related activation during these distinct components of a trace-conditioning trial. Trace-conditioned activity was observed within dorsomedial prefrontal cortex (PFC), dorsolateral PFC, insula, inferior parietal lobule (IPL), and posterior cingulate (PCC). Each of these regions showed learning-related activity during the trace CS, while trace-interval activity was only observed within a subset of these areas (i.e., dorsomedial PFC, PCC, right dorsolateral PFC, right IPL, right superior/middle temporal gyrus, and bilateral insula). Trace-interval activity was greater in right than in left dorsolateral PFC, IPL, and superior/middle temporal gyrus. These findings indicate that components of the prefrontal, cingulate, insular, and parietal cortices support trace-interval processes, as well as suggesting that a right-lateralized fronto-parietal circuit may play a unique role in trace conditioning.

  9. Prediction Interval Development for Wind-Tunnel Balance Check-Loading

    NASA Technical Reports Server (NTRS)

    Landman, Drew; Toro, Kenneth G.; Commo, Sean A.; Lynn, Keith C.

    2014-01-01

    Results from the Facility Analysis Verification and Operational Reliability project revealed a critical gap in capability in ground-based aeronautics research applications. Without a standardized process for check-loading the wind-tunnel balance or the model system, the quality of the aerodynamic force data collected varied significantly between facilities. A prediction interval is required in order to confirm a check-loading. The prediction interval provides an expected upper and lower bound on balance load prediction at a given confidence level. A method has been developed which accounts for sources of variability due to calibration and check-load application. The prediction interval method of calculation and a case study demonstrating its use is provided. Validation of the methods is demonstrated for the case study based on the probability of capture of confirmation points.

  10. On the Effective Construction of Compactly Supported Wavelets Satisfying Homogenous Boundary Conditions on the Interval

    NASA Technical Reports Server (NTRS)

    Chiavassa, G.; Liandrat, J.

    1996-01-01

    We construct compactly supported wavelet bases satisfying homogeneous boundary conditions on the interval (0,1). The maximum features of multiresolution analysis on the line are retained, including polynomial approximation and tree algorithms. The case of H(sub 0)(sup 1)(0, 1)is detailed, and numerical values, required for the implementation, are provided for the Neumann and Dirichlet boundary conditions.

  11. Likelihood based observability analysis and confidence intervals for predictions of dynamic models

    PubMed Central

    2012-01-01

    Background Predicting a system’s behavior based on a mathematical model is a primary task in Systems Biology. If the model parameters are estimated from experimental data, the parameter uncertainty has to be translated into confidence intervals for model predictions. For dynamic models of biochemical networks, the nonlinearity in combination with the large number of parameters hampers the calculation of prediction confidence intervals and renders classical approaches as hardly feasible. Results In this article reliable confidence intervals are calculated based on the prediction profile likelihood. Such prediction confidence intervals of the dynamic states can be utilized for a data-based observability analysis. The method is also applicable if there are non-identifiable parameters yielding to some insufficiently specified model predictions that can be interpreted as non-observability. Moreover, a validation profile likelihood is introduced that should be applied when noisy validation experiments are to be interpreted. Conclusions The presented methodology allows the propagation of uncertainty from experimental to model predictions. Although presented in the context of ordinary differential equations, the concept is general and also applicable to other types of models. Matlab code which can be used as a template to implement the method is provided at http://www.fdmold.uni-freiburg.de/∼ckreutz/PPL. PMID:22947028

  12. Likelihood based observability analysis and confidence intervals for predictions of dynamic models.

    PubMed

    Kreutz, Clemens; Raue, Andreas; Timmer, Jens

    2012-09-05

    Predicting a system's behavior based on a mathematical model is a primary task in Systems Biology. If the model parameters are estimated from experimental data, the parameter uncertainty has to be translated into confidence intervals for model predictions. For dynamic models of biochemical networks, the nonlinearity in combination with the large number of parameters hampers the calculation of prediction confidence intervals and renders classical approaches as hardly feasible. In this article reliable confidence intervals are calculated based on the prediction profile likelihood. Such prediction confidence intervals of the dynamic states can be utilized for a data-based observability analysis. The method is also applicable if there are non-identifiable parameters yielding to some insufficiently specified model predictions that can be interpreted as non-observability. Moreover, a validation profile likelihood is introduced that should be applied when noisy validation experiments are to be interpreted. The presented methodology allows the propagation of uncertainty from experimental to model predictions. Although presented in the context of ordinary differential equations, the concept is general and also applicable to other types of models. Matlab code which can be used as a template to implement the method is provided at http://www.fdmold.uni-freiburg.de/∼ckreutz/PPL.

  13. Stability margins for multilinear interval systems by way of phase conditions: A unified approach

    NASA Technical Reports Server (NTRS)

    Keel, L. H.; Bhattacharyya, S. P.

    1992-01-01

    A simple way of checking the stability with respect to an arbitrary stability region of a family of polynomials containing a vector of parameters varying within prescribed intervals is discussed. It is assumed that the parameters appear affine multilinearly in the characteristic polynomial coefficients. The condition proposed is simply to check the phase difference of the vertex polynomials. This test based on the mapping theorem significantly reduces computational complexity. Mathematical proofs are omitted. The results can be used to determine various stability margins of control systems containing interconnected interval subsystems. These include the gain, phase, time-delay, H(sup infinity), and nonlinear sector bounded stability margins of multilinear interval systems.

  14. Statistical prediction intervals for the evaluation of ground-water quality

    SciTech Connect

    Gibbons, R.D.

    1987-07-01

    Factors for a normal distribution are given such that one may be 99% confident that the two-sided prediction interval chi-bar +- rs or the one-sided prediction several chi-bar + rs will contain all of the kappa future values, where chi-bar and s are the sample means and standard deviation obtained from n previous values. In the context of ground-water monitoring, the future samples may represent new monitoring values at each of kappa downgradient wells, and the n previous values might be the historical monitoring results for one or more upgradient wells. The Tables provided in this paper allow the computation of one-sided and two-sided 99% prediction intervals for previous sample sizes of n = 4 to 100 and future samples of kappa = 1 to 100. Modification of these intervals for log-normally distributed data is also presented.

  15. Availability analysis of mechanical systems with condition-based maintenance using semi-Markov and evaluation of optimal condition monitoring interval

    NASA Astrophysics Data System (ADS)

    Kumar, Girish; Jain, Vipul; Gandhi, O. P.

    2017-06-01

    Maintenance helps to extend equipment life by improving its condition and avoiding catastrophic failures. Appropriate model or mechanism is, thus, needed to quantify system availability vis-a-vis a given maintenance strategy, which will assist in decision-making for optimal utilization of maintenance resources. This paper deals with semi-Markov process (SMP) modeling for steady state availability analysis of mechanical systems that follow condition-based maintenance (CBM) and evaluation of optimal condition monitoring interval. The developed SMP model is solved using two-stage analytical approach for steady-state availability analysis of the system. Also, CBM interval is decided for maximizing system availability using Genetic Algorithm approach. The main contribution of the paper is in the form of a predictive tool for system availability that will help in deciding the optimum CBM policy. The proposed methodology is demonstrated for a centrifugal pump.

  16. Can different conditioning activities and rest intervals affect the acute performance of taekwondo turning kick?

    PubMed

    Santos, Jonatas F da Silva; Valenzuela, Tomás H; Franchini, Emerson

    2015-06-01

    This study compared the acute effect of strength, plyometric, and complex exercises (combined strength and plyometric exercise) in the countermovement jump (CMJ) and frequency speed of kick test (FSKT) and attempted to establish the best rest interval to maximize performance in the CMJ, number of kicks, and impact generated during FSKT. Eleven taekwondo athletes (mean ± SD; age: 20.3 ± 5.2 years; body mass: 71.8 ± 15.3 kg; height: 177 ± 7.2 cm) participated. One control and 9 experimental conditions were randomly applied. Each condition was composed of warm-up, conditioning activity (half-squat: 3 × 1 at 95% 1RM; jumps: 3 × 10 vertical jumps above 40-cm barrier; or complex exercise: half-squat 3 × 2 at 95% 1RM + 4 vertical jumps above 40-cm barrier), followed by different rest intervals (5-, 10-minute, and self-selected) before CMJ and FSKT. The conditions were compared using an analysis of variance with repeated measures, followed by Bonferroni's post hoc test. The alpha level was set at 5%. Significant difference was found in the number of kicks (F9,90 = 1.32; p = 0.239; and η2 = 0.116 [small]). The complex method with a 10-minute rest interval (23 ± 5 repetitions) was superior (p = 0.026) to the control (19 ± 3 repetitions), maximum strength with a self-selected rest interval (328 ± 139 seconds; 18 ± 2 repetitions) (p = 0.015), and plyometric with a 5-minute rest interval (18 ± 3 repetitions) (p < 0.001). Our results indicate that taekwondo athletes increased the number of kicks in a specific test by using the complex method when 10-minute rest interval was used.

  17. Prediction Interval: What to Expect When You’re Expecting … A Replication

    PubMed Central

    2016-01-01

    A challenge when interpreting replications is determining whether the results of a replication “successfully” replicate the original study. Looking for consistency between two studies is challenging because individual studies are susceptible to many sources of error that can cause study results to deviate from each other and the population effect in unpredictable directions and magnitudes. In the current paper, we derive methods to compute a prediction interval, a range of results that can be expected in a replication due to chance (i.e., sampling error), for means and commonly used indexes of effect size: correlations and d-values. The prediction interval is calculable based on objective study characteristics (i.e., effect size of the original study and sample sizes of the original study and planned replication) even when sample sizes across studies are unequal. The prediction interval provides an a priori method for assessing if the difference between an original and replication result is consistent with what can be expected due to sample error alone. We provide open-source software tools that allow researchers, reviewers, replicators, and editors to easily calculate prediction intervals. PMID:27644090

  18. Phonation interval modification and speech performance quality during fluency-inducing conditions by adults who stutter

    PubMed Central

    Ingham, Roger J.; Bothe, Anne K.; Wang, Yuedong; Purkhiser, Krystal; New, Anneliese

    2012-01-01

    Purpose To relate changes in four variables previously defined as characteristic of normally fluent speech to changes in phonatory behavior during oral reading by persons who stutter (PWS) and normally fluent controls under multiple fluency-inducing (FI) conditions. Method Twelve PWS and 12 controls each completed 4 ABA experiments. During A phases, participants read normally. B phases were 4 different FI conditions: auditory masking, chorus reading, whispering, and rhythmic stimulation. Dependent variables were the durations of accelerometer-recorded phonated intervals; self-judged speech effort; and observer-judged stuttering frequency, speech rate, and speech naturalness. The method enabled a systematic replication of Ingham et al. (2009). Results All FI conditions resulted in decreased stuttering and decreases in the number of short phonated intervals, as compared with baseline conditions, but the only FI condition that satisfied all four characteristics of normally fluent speech was chorus reading. Increases in longer phonated intervals were associated with decreased stuttering but also with poorer naturalness and/or increased speech effort. Previous findings concerning the effects of FI conditions on speech naturalness and effort were replicated. Conclusions Measuring all relevant characteristics of normally fluent speech, in the context of treatments that aim to reduce the occurrence of short-duration PIs, may aid the search for an explanation of the nature of stuttering and may also maximize treatment outcomes for adults who stutter. PMID:22365886

  19. Ensemble decadal predictions from analysed initial conditions.

    PubMed

    Troccoli, Alberto; Palmer, T N

    2007-08-15

    Sensitivity experiments using a coupled model initialized from analysed atmospheric and oceanic observations are used to investigate the potential for interannual-to-decadal predictability. The potential for extending seasonal predictions to longer time scales is explored using the same coupled model configuration and initialization procedure as used for seasonal prediction. It is found that, despite model drift, climatic signals on interannual-to-decadal time scales appear to be detectable. Two climatic states have been chosen: one starting in 1965, i.e. ahead of a period of global cooling, and the other in 1994, ahead of a period of global warming. The impact of initial conditions and of the different levels of greenhouse gases are isolated in order to gain insights into the source of predictability.

  20. Baseline HV-interval predicts complete AV-block secondary to transcatheter aortic valve implantation.

    PubMed

    Shin, Dong-In; Merx, Marc W; Meyer, Christian; Kirmanoglou, Kiriakos; Hellhammer, Katharina; Ohlig, Jan; Katsani, Dimitra; Zeus, Tobias; Westenfeld, Ralf; Eickholt, Christian; Linke, Axel; Kelm, Malte

    2015-10-01

    Development of AV-block is a frequent complication associated with transcatheter aortic valve implantation (TAVI). To date little is known about the predictive value of the HV-interval prior to TAVI with respect to the risk of AV-block development. HV-interval was determined in 25 consecutive elderly patients with severe aortic valve stenosis (AS) before and immediately after TAVI. All patients subsequently underwent TAVI and 8 of these 25 patients (32%) developed complete AV-block during the TAVI procedure requiring permanent pacemaker implantation. Six of these 8 patients (75%) had marked HV prolongation (>54 ms). Pre-procedural HV-interval was significantly prolonged in the subgroup developing complete AV-block (62.1 ms±13.0 vs 49.2 ms±12.9; P=0.029). Prolongation of the HV-interval above 54 ms was associated with a higher rate of complete AV-block (sensitivity 75.0%, specificity 77.8%, P=0.01). HV-interval was prolonged in approximately one third of our elderly patients with aortic valve stenosis and associated with a high rate of complete AV-block following TAVI. HV-interval is easily obtained during TAVI screening procedures, thus facilitating identification of patients at risk for complete AV-block due to TAVI and consequently enabling bespoke risk management.

  1. An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization.

    PubMed

    Lee, Ching-Hung; Chang, Feng-Yu; Lin, Chih-Min

    2014-03-01

    This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.

  2. A novel algorithm to predict the QT interval during intrinsic atrioventricular conduction from an electrocardiogram obtained during ventricular pacing.

    PubMed

    Sriwattanakomen, Roy; Mukamal, Kenneth J; Shvilkin, Alexei

    2016-10-01

    QT interval prolongation is a major arrhythmia risk factor. Standard QT interval limits are defined for preserved intrinsic atrioventricular and interventricular conduction. However, ventricular pacing (VP) prolongs the QRS duration, induces electrical remodeling, and therefore obscures the intrinsic QT interval. No consensus exists on QT interval monitoring during VP. The aim of this study was to develop an algorithm to predict the QT interval during intrinsic conduction (IC) from the VP electrocardiogram. We measured electrocardiographic intervals QRS, QT, QTpeak, JTpeak, and TpeakTend in 38 participants with cardiac devices and preserved atrioventricular and interventricular conduction. We performed paired measurements in AAI (IC) and DDD (VP) pacing modes at equal heart rates at baseline and after 1 week of VP. We fit linear mixed models to predict IC QT intervals from VP intervals and compared their fit with other proposed methods of IC QT interval estimation. After 1 week of VP, the IC QT interval prolonged while the VP QT interval shortened from their respective baseline values. VP QT interval shortening was due to TpeakTend interval shortening. JTpeak and QTpeak intervals prolonged in both pacing modes at 1 week. A formula using VP QTpeak interval and heart rate closely predicted the IC QT interval (r = 0.94), outperforming other methods, including subtraction of "excess" QRS duration from the actual QT interval (r = 0.64) and subtraction of fixed values from heart rate-corrected QT interval (r = 0.58 and r = 0.69). Validation in 2000 bootstrapped data sets confirmed the model's performance (r = 0.93) compared to others (r = 0.43-0.58). In patients with VP, a formula using the QTpeak interval accurately predicts the intrinsic QT interval. Copyright © 2016 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

  3. Prediction of genetic contributions and generation intervals in populations with overlapping generations under selection.

    PubMed Central

    Bijma, P; Woolliams, J A

    1999-01-01

    A method to predict long-term genetic contributions of ancestors to future generations is studied in detail for a population with overlapping generations under mass or sib index selection. An existing method provides insight into the mechanisms determining the flow of genes through selected populations, and takes account of selection by modeling the long-term genetic contribution as a linear regression on breeding value. Total genetic contributions of age classes are modeled using a modified gene flow approach and long-term predictions are obtained assuming equilibrium genetic parameters. Generation interval was defined as the time in which genetic contributions sum to unity, which is equal to the turnover time of genes. Accurate predictions of long-term genetic contributions of individual animals, as well as total contributions of age classes were obtained. Due to selection, offspring of young parents had an above-average breeding value. Long-term genetic contributions of youngest age classes were therefore higher than expected from the age class distribution of parents, and generation interval was shorter than the average age of parents at birth of their offspring. Due to an increased selective advantage of offspring of young parents, generation interval decreased with increasing heritability and selection intensity. The method was compared to conventional gene flow and showed more accurate predictions of long-term genetic contributions. PMID:10049935

  4. The Use of One-Sample Prediction Intervals for Estimating CO2 Scrubber Canister Durations

    DTIC Science & Technology

    2012-10-01

    testing) that describe the characteristics of carbon dioxide absorbent canisters in closed - and semiclosed- circuit UBAs ( rebreathers ). The...CONCLUSIONS As described in engineering statistics texts, the use of one-sample prediction intervals is profitably applied to the testing of closed - circuit ...Duration Limits for Closed - Circuit Underwater Breathing Apparatus, NEDU TR 2-99, Navy Experimental Diving Unit, April 1999. 2. J. Clarke, K

  5. Fast time-series prediction using high-dimensional data: evaluating confidence interval credibility.

    PubMed

    Hirata, Yoshito

    2014-05-01

    I propose an index for evaluating the credibility of confidence intervals for future observables predicted from high-dimensional time-series data. The index evaluates the distance from the current state to the data manifold. I demonstrate the index with artificial datasets generated from the Lorenz'96 II model [Lorenz, in Proceedings of the Seminar on Predictability, Vol. 1 (ECMWF, Reading, UK, 1996), p. 1], the Lorenz'96 I model [Hansen and Smith, 2859:TROOCI>2.0.CO;2">J. Atmos. Sci. 57, 2859 (2000).

  6. Prediction of temperature conditions at great depths

    SciTech Connect

    Lyubimova, Y.A.

    1981-02-01

    The effects of temperature and pressure on the heat conductivity of dry, and water- and oil-saturated rocks, which form the upper part of the Earth's crust were examined: carbonates, sulfates, clastic rocks (siltstones), and also granites and andesite-basalts. Using the data on the intensity of heat flow and changes in the heat-conductivity of rocks, as exemplified by the Front Ranges of Eastern Ciscaucasia, the predicted temperatures through the sequence of oil fields were calculated and a sketch map of the distribution of temperatures along the top of the Jurassic subsalt deposits was constructed. It has been established that neglect of the temperature and pressure effects on the heat-conductivity of rocks will lead to distortion of the heat-flow assessment, and for the conditions under discussion, its over-estimate is 20 to 25%. When determining the intensity of heat flow, distortion factors, such as surface, hydrogeologic, and sedimentation conditions, were accounted for. (JMT)

  7. Competition between novelty and cocaine conditioned reward is sensitive to drug dose and retention interval

    PubMed Central

    Reichel, Carmela M.; Bevins, Rick A.

    2010-01-01

    The following manuscript is the final accepted manuscript. It has not been subjected to the final copyediting, fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The American Psychological Association and its Council of Editors disclaim any responsibility or liabilities for errors or omissions of this manuscript version, any version derived from this manuscript by NIH, or other third parties. The published version is available at www.apa.org/pubs/journals/bne The conditioned rewarding effects of novelty compete with those of cocaine for control over choice behavior using a place-conditioning task. The purpose of the present study was to use multiple doses of cocaine to determine the extent of this competition and to determine whether novelty's impact on cocaine reward was maintained over an abstinence period. In Experiment 1, rats were conditioned with cocaine (7.5, 20, or 30 mg/kg, IP) to prefer one side of an unbiased place conditioning apparatus relative to the other. In a subsequent phase, all rats received alternating daily confinements to the previously cocaine-paired and unpaired sides of the apparatus. During this phase, half the rats had access to a novel object on their initially unpaired side; the remaining rats did not receive objects. The ability of novelty to compete with cocaine in a drug-free and cocaine-challenge test was sensitive to cocaine dose. In Experiment 2, a place preference was established with 10 mg/kg cocaine and testing occurred after 1, 14, or 28 day retention intervals. Findings indicate that choice behaviors mediated by cocaine conditioning are reduced with the passing of time. Taken together, competition between cocaine and novelty conditioned rewards are sensitive to drug dose and retention interval. PMID:20141289

  8. Predicting biological condition in southern California streams

    USGS Publications Warehouse

    Brown, Larry R.; May, Jason T.; Rehn, Andrew C.; Ode, Peter R.; Waite, Ian R.; Kennen, Jonathan G.

    2012-01-01

    As understanding of the complex relations among environmental stressors and biological responses improves, a logical next step is predictive modeling of biological condition at unsampled sites. We developed a boosted regression tree (BRT) model of biological condition, as measured by a benthic macroinvertebrate index of biotic integrity (BIBI), for streams in urbanized Southern Coastal California. We also developed a multiple linear regression (MLR) model as a benchmark for comparison with the BRT model. The BRT model explained 66% of the variance in B-IBI, identifying watershed population density and combined percentage agricultural and urban land cover in the riparian buffer as the most important predictors of B-IBI, but with watershed mean precipitation and watershed density of manmade channels also important. The MLR model explained 48% of the variance in B-IBI and included watershed population density and combined percentage agricultural and urban land cover in the riparian buffer. For a verification data set, the BRT model correctly classified 75% of impaired sites (B-IBI < 40) and 78% of unimpaired sites (B-IBI = 40). For the same verification data set, the MLR model correctly classified 69% of impaired sites and 87% of unimpaired sites. The BRT model should not be used to predict B-IBI for specific sites; however, the model can be useful for general applications such as identifying and prioritizing regions for monitoring, remediation or preservation, stratifying new bioassessments according to anticipated biological condition, or assessing the potential for change in stream biological condition based on anticipated changes in population density and development in stream buffers.

  9. Predicting watershed acidification under alternate rainfall conditions

    USGS Publications Warehouse

    Huntington, T.G.

    1996-01-01

    The effect of alternate rainfall scenarios on acidification of a forested watershed subjected to chronic acidic deposition was assessed using the model of acidification of groundwater in catchments (MAGIC). The model was calibrated at the Panola Mountain Research Watershed, near Atlanta, Georgia, U.S.A. using measured soil properties, wet and dry deposition, and modeled hydrologic routing. Model forecast simulations were evaluated to compare alternate temporal averaging of rainfall inputs and variations in rainfall amount and seasonal distribution. Soil water alkalinity was predicted to decrease to substantially lower concentrations under lower rainfall compared with current or higher rainfall conditions. Soil water alkalinity was also predicted to decrease to lower levels when the majority of rainfall occurred during the growing season compared with other rainfall distributions. Changes in rainfall distribution that result in decreases in net soil water flux will temporarily delay acidification. Ultimately, however, decreased soil water flux will result in larger increases in soil- adsorbed sulfur and soil-water sulfate concentrations and decreases in alkalinity when compared to higher water flux conditions. Potential climate change resulting in significant changes in rainfall amounts, seasonal distribution of rainfall, or evapotranspiration will change net soil water flux and, consequently, will affect the dynamics of the acidification response to continued sulfate loading.

  10. Analytical Conditions for Compact Earthquake Prediction Approaches

    NASA Astrophysics Data System (ADS)

    Sengor, T.

    2009-04-01

    This paper concerns itself with The atmosphere and ionosphere include non-uniform electric charge and current distributions during the earthquake activity. These charges and currents move irregularly when an activity is scheduled for an earthquake at the future. The electromagnetic characteristics of the region over the earth change to domains where irregular transportations of non-uniform electric charges are observed; therefore, the electromagnetism in the plasma, which moves irregularly and contains non-uniform charge distributions, is studied. These cases of charge distributions are called irregular and non-uniform plasmas. It is called the seismo-plasma if irregular and non-uniform plasma defines a real earthquake activity, which will come to truth. Some signals involving the above-mentioned coupling effects generate some analytical conditions giving the predictability of seismic processes [1]-[5]. These conditions will be discussed in this paper. 2 References [1] T. Sengor, "The electromagnetic device optimization modeling of seismo-electromagnetic processes," IUGG Perugia 2007. [2] T. Sengor, "The electromagnetic device optimization modeling of seismo-electromagnetic processes for Marmara Sea earthquakes," EGU 2008. [3] T. Sengor, "On the exact interaction mechanism of electromagnetically generated phenomena with significant earthquakes and the observations related the exact predictions before the significant earthquakes at July 1999-May 2000 period," Helsinki Univ. Tech. Electrom. Lab. Rept. 368, May 2001. [4] T. Sengor, "The Observational Findings Before The Great Earthquakes Of December 2004 And The Mechanism Extraction From Associated Electromagnetic Phenomena," Book of XXVIIIth URSI GA 2005, pp. 191, EGH.9 (01443) and Proceedings 2005 CD, New Delhi, India, Oct. 23-29, 2005. [5] T. Sengor, "The interaction mechanism among electromagnetic phenomena and geophysical-seismic-ionospheric phenomena with extraction for exact earthquake prediction genetics," 10

  11. Olfactory conditioning of the sting extension reflex in honeybees: Memory dependence on trial number, interstimulus interval, intertrial interval, and protein synthesis.

    PubMed

    Giurfa, Martin; Fabre, Eve; Flaven-Pouchon, Justin; Groll, Helga; Oberwallner, Barbara; Vergoz, Vanina; Roussel, Edith; Sandoz, Jean Christophe

    2009-12-01

    Harnessed bees learn to associate an odorant with an electric shock so that afterward the odorant alone elicits the sting extension response (SER). We studied the dependency of retention on interstimulus interval (ISI), intertrial interval (ITI), and number of conditioning trials in the framework of olfactory SER conditioning. Forward ISIs (conditioned stimulus [CS] before unconditioned stimulus [US]) supported higher retention than a backward one (US before CS) with an optimum around 3 sec. Spaced trials (ITI 10 min) supported higher retention than massed trials (ITI 1 min) and led to the formation of a late long-term memory (l-LTM) that depended on protein synthesis. Our results reaffirm olfactory SER conditioning as a reliable tool for the study of learning and memory.

  12. Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor-tracking radiotherapy.

    PubMed

    Mukumoto, Nobutaka; Nakamura, Mitsuhiro; Akimoto, Mami; Miyabe, Yuki; Yokota, Kenji; Matsuo, Yukinori; Mizowaki, Takashi; Hiraoka, Masahiro

    2017-08-01

    To explore the effect of sampling interval of training data acquisition on the intrafractional prediction error of surrogate signal-based dynamic tumor-tracking using a gimbal-mounted linac. Twenty pairs of respiratory motions were acquired from 20 patients (ten lung, five liver, and five pancreatic cancer patients) who underwent dynamic tumor-tracking with the Vero4DRT. First, respiratory motions were acquired as training data for an initial construction of the prediction model before the irradiation. Next, additional respiratory motions were acquired for an update of the prediction model due to the change of the respiratory pattern during the irradiation. The time elapsed prior to the second acquisition of the respiratory motion was 12.6 ± 3.1 min. A four-axis moving phantom reproduced patients' three dimensional (3D) target motions and one dimensional surrogate motions. To predict the future internal target motion from the external surrogate motion, prediction models were constructed by minimizing residual prediction errors for training data acquired at 80 and 320 ms sampling intervals for 20 s, and at 500, 1,000, and 2,000 ms sampling intervals for 60 s using orthogonal kV x-ray imaging systems. The accuracies of prediction models trained with various sampling intervals were estimated based on training data with each sampling interval during the training process. The intrafractional prediction errors for various prediction models were then calculated on intrafractional monitoring images taken for 30 s at the constant sampling interval of a 500 ms fairly to evaluate the prediction accuracy for the same motion pattern. In addition, the first respiratory motion was used for the training and the second respiratory motion was used for the evaluation of the intrafractional prediction errors for the changed respiratory motion to evaluate the robustness of the prediction models. The training error of the prediction model was 1.7 ± 0.7 mm in 3D for all sampling

  13. Weak eigenfunctions of two-interval Sturm-Liouville problems together with interaction conditions

    NASA Astrophysics Data System (ADS)

    Olǧar, H.; Mukhtarov, O. Sh.

    2017-04-01

    In this study, we consider a new type boundary value problem consisting of a Sturm-Liouville equation on two disjoint intervals together with interaction conditions and with eigenvalue parameter in the boundary conditions. We suggest a special technique to reduce the considered problem into an integral equation by the use of which we define a new concept, the so-called weak eigenfunction for the considered problem. Then we construct some Hilbert spaces and define some self-adjoint compact operators in these spaces in such a way that the considered problem can be interpreted as a self-adjoint operator-pencil equation. Finally, it is shown that the spectrum is discrete and the set of weak eigenfunctions form a Riesz basis of the suitable Hilbert space.

  14. Prediction intervals for future BMI values of individual children: a non-parametric approach by quantile boosting.

    PubMed

    Mayr, Andreas; Hothorn, Torsten; Fenske, Nora

    2012-01-25

    The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age. We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data. The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child. Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures.

  15. A note on the graphical presentation of prediction intervals in random-effects meta-analyses.

    PubMed

    Guddat, Charlotte; Grouven, Ulrich; Bender, Ralf; Skipka, Guido

    2012-07-28

    Meta-analysis is used to combine the results of several related studies. Two different models are generally applied: the fixed-effect (FE) and random-effects (RE) models. Although the two approaches estimate different parameters (that is, the true effect versus the expected value of the distribution of true effects) in practice, the graphical presentation of results is the same for both models. This means that in forest plots of RE meta-analyses, no estimate of the between-study variation is usually given graphically, even though it provides important information about the heterogeneity between the study effect sizes. In addition to the point estimate of the between-study variation, a prediction interval (PI) can be used to determine the degree of heterogeneity, as it provides a region in which about 95% of the true study effects are expected to be found. To distinguish between the confidence interval (CI) for the average effect and the PI, it may also be helpful to include the latter interval in forest plots. We propose a new graphical presentation of the PI; in our method, the summary statistics in forest plots of RE meta-analyses include an additional row, '95% prediction interval', and the PI itself is presented in the form of a rectangle below the usual diamond illustrating the estimated average effect and its CI. We then compare this new graphical presentation of PIs with previous proposals by other authors. The way the PI is presented in forest plots is crucial. In previous proposals, the distinction between the CI and the PI has not been made clear, as both intervals have been illustrated either by a diamond or by extra lines added to the diamond, which may result in misinterpretation. To distinguish graphically between the results of an FE and those of an RE meta-analysis, it is helpful to extend forest plots of the latter approach by including the PI. Clear presentation of the PI is necessary to avoid confusion with the CI of the average effect estimate.

  16. Mode-locking neurodynamics predict human auditory brainstem responses to musical intervals.

    PubMed

    Lerud, Karl D; Almonte, Felix V; Kim, Ji Chul; Large, Edward W

    2014-02-01

    The auditory nervous system is highly nonlinear. Some nonlinear responses arise through active processes in the cochlea, while others may arise in neural populations of the cochlear nucleus, inferior colliculus and higher auditory areas. In humans, auditory brainstem recordings reveal nonlinear population responses to combinations of pure tones, and to musical intervals composed of complex tones. Yet the biophysical origin of central auditory nonlinearities, their signal processing properties, and their relationship to auditory perception remain largely unknown. Both stimulus components and nonlinear resonances are well represented in auditory brainstem nuclei due to neural phase-locking. Recently mode-locking, a generalization of phase-locking that implies an intrinsically nonlinear processing of sound, has been observed in mammalian auditory brainstem nuclei. Here we show that a canonical model of mode-locked neural oscillation predicts the complex nonlinear population responses to musical intervals that have been observed in the human brainstem. The model makes predictions about auditory signal processing and perception that are different from traditional delay-based models, and may provide insight into the nature of auditory population responses. We anticipate that the application of dynamical systems analysis will provide the starting point for generic models of auditory population dynamics, and lead to a deeper understanding of nonlinear auditory signal processing possibly arising in excitatory-inhibitory networks of the central auditory nervous system. This approach has the potential to link neural dynamics with the perception of pitch, music, and speech, and lead to dynamical models of auditory system development.

  17. Estimation of confidence intervals of global horizontal irradiance obtained from a weather prediction model

    NASA Astrophysics Data System (ADS)

    Ohtake, Hideaki; Gari da Silva Fonseca, Joao, Jr.; Takashima, Takumi; Oozeki, Takashi; Yamada, Yoshinori

    2014-05-01

    Many photovoltaic (PV) systems have been installed in Japan after the introduction of the Feed-in-Tariff. For an energy management of electric power systems included many PV systems, the forecast of the PV power production are useful technology. Recently numerical weather predictions have been applied to forecast the PV power production while the forecasted values invariably have forecast errors for each modeling system. So, we must use the forecast data considering its error. In this study, we attempted to estimate confidence intervals for hourly forecasts of global horizontal irradiance (GHI) values obtained from a mesoscale model (MSM) de-veloped by the Japan Meteorological Agency. In the recent study, we found that the forecasted values of the GHI of the MSM have two systematical forecast errors; the first is that forecast values of the GHI are depended on the clearness indices, which are defined as the GHI values divided by the extraterrestrial solar irradiance. The second is that forecast errors have the seasonal variations; the overestimation of the GHI forecasts is found in winter while the underestimation of those is found in summer. The information of the errors of the hourly GHI forecasts, that is, confidence intervals of the forecasts, is of great significance for planning the energy management included a lot of PV systems by an electric company. On the PV systems, confidence intervals of the GHI forecasts are required for a pinpoint area or for a relatively large area control-ling the power system. For the relatively large area, a spatial-smoothing method of the GHI values is performed for both the observations and forecasts. The spatial-smoothing method caused the decline of confidence intervals of the hourly GHI forecasts on an extreme event of the GHI forecast (a case of large forecast error) over the relatively large area of the Tokyo electric company (approximately 68 % than for a pinpoint forecast). For more credible estimation of the confidence

  18. Ambulatory blood pressure reduction following high-intensity interval exercise performed in water or dryland condition.

    PubMed

    Sosner, Philippe; Gayda, Mathieu; Dupuy, Olivier; Garzon, Mauricio; Lemasson, Christopher; Gremeaux, Vincent; Lalongé, Julie; Gonzales, Mariel; Hayami, Douglas; Juneau, Martin; Nigam, Anil; Bosquet, Laurent

    2016-05-01

    We aimed to compare blood pressure (BP) responses following moderate-intensity continuous exercise (MICE), high-intensity interval exercise (HIIE) in dry land or HIIE in immersed condition, using 24-hour ambulatory BP monitoring. Forty-two individuals (65 ± 7 years, 52% men) with a baseline BP ≥ 130/85 mm Hg (systolic/diastolic blood pressures [SBP/DBP]) were randomly assigned to perform one of the three following exercises on a stationary cycle: MICE (24 minutes at 50% peak power output) or HIIE in dry land (two sets of 10 minutes with phases of 15 seconds 100% peak power output interspersed by 15 seconds of passive recovery) or HIIE in up-to-the-chest immersed condition. While MICE modified none of the 24-hour average hemodynamic variables, dryland HIIE induced a 24-hour BP decrease (SBP: -3.6 ± 5.7/DBP: -2.8 ± 3.0 mm Hg, P < .05) and, to a much greater extent, immersed HIIE (SBP: -6.8 ± 9.5/DBP: -3.0 ± 4.5 mm Hg, P < .05). The one condition that modified 24-hour pulse-wave velocity was immersed HIIE (-0.21 ± 0.30 m/s, P < .05).

  19. Condition-based predictive maintenance of industrial power systems

    NASA Astrophysics Data System (ADS)

    Azam, Mohammad; Tu, Fang; Pattipati, Krishna R.

    2002-07-01

    Traditional static maintenance scheduling based on lifetime data and replacement upon failure is adequate for typical power users. However, in the case of high reliability/availability-oriented industries (e.g., power systems for internet data centers have a desired availability of 0.99999 and, for semiconductor fabrication plants, have availability requirement of 0.9999999), this type of preventive maintenance scheduling is inadequate. A suitable approach in these situations is the adoption of condition-based predictive maintenance. Here the system condition is evaluated by processing the information gathered from the monitors placed at different points in the system, and maintenance is performed only when the failure/malfunction prognosis dictates. In the past, for power systems, voltages, currents, power, temperature and electromagnetic quantities had been monitored along with surface inspection and material quality tests at regular intervals. Diagnostic methods are already in place to indicate problems in industrial power systems by examining these monitored quantities. However, they lack the capability of looking into distant future. With the introduction of modern digital electronics-based smart monitors, the capability of logging power quality data at micro-second intervals, advanced signal processing tools for extracting features from collected data, and data mining techniques, a new horizon in maintenance scheduling has been unveiled. Trending techniques and techniques based on neural networks, when applied to the extracted features, enable us to predict the possible failures of individual equipment and subsystems well before they manifest. This paper considers the problem of evaluating the health indices of components of a power system by making use of the monitored power-quality data and classification techniques. Health index analysis distinguishes the healthy and risky components of the system. Results of these evaluations can be fed as inputs into a

  20. Prolonged QT interval at onset of acute myocardial infarction in predicting early phase ventricular tachycardia

    SciTech Connect

    Taylor, G.J.; Crampton, R.S.; Gibson, R.S.; Stebbins, P.T.; Waldman, M.T.; Beller, G.A.

    1981-07-01

    The prospectively assessed time course of changes in ventricular repolarization during acute myocardial infarction (AMI) is reported in 32 patients admitted 2.0 +/- 1.8 (SD) hours after AMI onset. The initial corrected QT interval (QTc) upon hospitalization was longer in the 14 patients developing ventricular tachycardia (VT) within the first 48 hours as compared to QTc in the eight patients with frequent ventricular premature beats (VPBs) and to QTc in the 10 patients with infrequent VPBs. By the fifth day after AMI onset, the QTc shortened significantly only in the VT group, suggesting a greater initial abnormality of repolarization in these patients. All 32 patients had coronary angiography, radionuclide ventriculography, and myocardial perfusion scintigraphy before hospital discharge. Significant discriminating factors related to early phase VT in AMI included initially longer QT and QTc intervals, faster heart rate, higher peak serum levels of creatine kinase, acute anterior infarction, angiographically documented proximal stenosis of the left anterior descending coronary artery, and scintigraphic evidence of hypoperfusion of the interventricular septum. Prior infarction, angina pectoris, hypertension, multivessel coronary artery disease, and depressed left ventricular ejection fraction did not provide discrimination among the three different ventricular arrhythmia AMI groups. Researchers conclude that (1) the QT interval is frequently prolonged early in AMI, (2) the initial transiently prolonged ventricular repolarization facilitates and predicts complex ventricular tachyarrhythmias within the first 48 hours of AMI, (3) jeopardized blood supply to the interventricular septum frequently coexists, and (4) therapeutic enhancement of rapid recovery of the ventricular repolarization process merits investigation for prevention of VT in AMI.

  1. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    PubMed

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

  2. Constructing Optimal Prediction Intervals by Using Neural Networks and Bootstrap Method.

    PubMed

    Khosravi, Abbas; Nahavandi, Saeid; Srinivasan, Dipti; Khosravi, Rihanna

    2015-08-01

    This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.

  3. Adaptive sensing of ECG signals using R-R interval prediction.

    PubMed

    Nakaya, Shogo; Nakamura, Yuichi

    2013-01-01

    There is growing demand for systems consisting of tiny sensor nodes powered with small batteries that acquire electrocardiogram (ECG) data and wirelessly transmit the data to remote base stations or mobile phones continuously over a long period. Conserving electric power in the wireless sensor nodes (WSNs) is essential in such systems. Adaptive sensing is promising for this purpose since it can reduce the energy consumed not only for data transmission but also for sensing. However, the basic method of adaptive sensing, referred to here as "plain adaptive sensing," is not suitable for ECG signals because it sometimes capture the R waves defectively. We introduce an improved adaptive sensing method for ECG signals by incorporating R-R interval prediction. Our method improves the characteristics of ECG compression and drastically reduces the total energy consumption of the WSNs.

  4. Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K.; Sudheer, K.

    2013-05-01

    Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

  5. Study on short term prediction using observed water quality from 8-day intervals in Nakdong river

    NASA Astrophysics Data System (ADS)

    Kim, M.; Shon, T.; Joo, J.; Kim, J.; Shin, H.

    2012-12-01

    There are lots of accidents on water quality, like green algal blooms, occurring in Nakdong river which is one of the largest river in Korea. This is because of climate change around the world. It is essential to develop scientific and quantitative assessment methods. In this study, artificial neural network based on back propagation algorithm, which is simple and flexible method, was used for forecasting the water quality on the purpose of water resources management. Especially, as used observed water quality data from 8-day intervals in Nakdong river, it makes to increase the accuracy of water quality forecast over short term. This was established for predicting the water quality factors 1, 3, and 7 days ahead. The best model, as evaluated by its performance functions with RMSE and R2, was selected and applied to established models of BOD, DO, COD, and Chl-a using artificial neural network. The results showed that the models were suitable for 1 and 3 days forecasts in particular. This method is strong and convenient to predict water quality factors over the short term easily based on observed data. It is possible to overcome and manage problems related to the water resources. In the future, this will be a powerful method because it is basically based on observed water quality data.

  6. Early photosensitizer uptake kinetics predict optimum drug-light interval for photodynamic therapy

    NASA Astrophysics Data System (ADS)

    Sinha, Lagnojita; Elliott, Jonathan T.; Hasan, Tayyaba; Pogue, Brian W.; Samkoe, Kimberley S.; Tichauer, Kenneth M.

    2015-03-01

    Photodynamic therapy (PDT) has shown promising results in targeted treatment of cancerous cells by developing localized toxicity with the help of light induced generation of reactive molecular species. The efficiency of this therapy depends on the product of the intensity of light dose and the concentration of photosensitizer (PS) in the region of interest (ROI). On account of this, the dynamic and variable nature of PS delivery and retention depends on many physiological factors that are known to be heterogeneous within and amongst tumors (e.g., blood flow, blood volume, vascular permeability, and lymph drainage rate). This presents a major challenge with respect to how the optimal time and interval of light delivery is chosen, which ideally would be when the concentration of PS molecule is at its maximum in the ROI. In this paper, a predictive algorithm is developed that takes into consideration the variability and dynamic nature of PS distribution in the body on a region-by-region basis and provides an estimate of the optimum time when the PS concentration will be maximum in the ROI. The advantage of the algorithm lies in the fact that it predicts the time in advance as it takes only a sample of initial data points (~12 min) as input. The optimum time calculated using the algorithm estimated a maximum dose that was only 0.58 +/- 1.92% under the true maximum dose compared to a mean dose error of 39.85 +/- 6.45% if a 1 h optimal light deliver time was assumed for patients with different efflux rate constants of the PS, assuming they have the same plasma function. Therefore, if the uptake values of PS for the blood and the ROI is known for only first 12 minutes, the entire curve along with the optimum time of light radiation can be predicted with the help of this algorithm.

  7. Individualized corrected QT interval is superior to QT interval corrected using the Bazett formula in predicting mutation carriage in families with long QT syndrome.

    PubMed

    Robyns, Tomas; Willems, Rik; Vandenberk, Bert; Ector, Joris; Garweg, Christophe; Kuiperi, Cuno; Breckpot, Jeroen; Corveleyn, Anniek; Janssens, Stefan; Heidbuchel, Hein; Nuyens, Dieter

    2017-03-01

    Long QT syndrome (LQTS) is characterized by reduced penetrance and variable QT prolongation over time, resulting in an estimate of 25% carriers of a pathogenic mutation with a normal corrected QT (QTc) interval on the resting electrocardiogram (ECG). The purpose of this study was to test the hypothesis that an individualized corrected QT interval derived from 24-hour Holter data more accurately predicts carriage of a pathogenic LQTS mutation than did QT derived from a standard 12-lead ECG and corrected using the Bazett formula (QTc interval). Carriers of a pathogenic LQTS mutation and their genotype-negative family members who had both resting ECG and Holter recordings available were included. Automated and manual measurements of QTc were performed. QTi was derived from 24-hour Holter recordings and defined as the QT value at the intersection of an RR interval of 1000 ms, with the linear regression line fitted through QT-RR data points of each individual patient. In total, 69 patients with LQTS (23 long QT type 1, 39 long QT type 2, and 7 long QT type 3) and 55 controls were selected. Demographic characteristics were comparable. A comparison of the receiver operating characteristic curves indicates that the test added diagnostic value compared to manual measurement (P = .02) or automated measurement (P = .005). The diagnostic accuracy of manually measured QTc using conventional cutoff criteria was 72%, while it was 92% using a sex-independent QTi cutoff of 445 ms. This was caused by a 39% increase in sensitivity without compromising the specificity. QTi derived from Holter recordings is superior to conventional QTc measured from a standard 12-lead ECG in predicting the mutation carrier state in families with LQTS. Copyright © 2016 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

  8. Predicting Culex pipiens/restuans population dynamics by interval lagged weather data

    PubMed Central

    2013-01-01

    Background Culex pipiens/restuans mosquitoes are important vectors for a variety of arthropod borne viral infections. In this study, the associations between 20 years of mosquito capture data and the time lagged environmental quantities daytime length, temperature, precipitation, relative humidity and wind speed were used to generate a predictive model for the population dynamics of this vector species. Methods Mosquito population in the study area was represented by averaged time series of mosquitos counts captured at 6 sites in Cook County (Illinois, USA). Cross-correlation maps (CCMs) were compiled to investigate the association between mosquito abundances and environmental quantities. The results obtained from the CCMs were incorporated into a Poisson regression to generate a predictive model. To optimize the predictive model the time lags obtained from the CCMs were adjusted using a genetic algorithm. Results CCMs for weekly data showed a highly positive correlation of mosquito abundances with daytime length 4 to 5 weeks prior to capture (quantified by a Spearman rank order correlation of rS = 0.898) and with temperature during 2 weeks prior to capture (rS = 0.870). Maximal negative correlations were found for wind speed averaged over 3 week prior to capture (rS = −0.621). Cx. pipiens/restuans population dynamics was predicted by integrating the CCM results in Poisson regression models. They were used to simulate the average seasonal cycle of the mosquito abundance. Verification with observations resulted in a correlation of rS = 0.899 for daily and rS = 0.917 for weekly data. Applying the optimized models to the entire 20-years time series also resulted in a suitable fit with rS = 0.876 for daily and rS = 0.899 for weekly data. Conclusions The study demonstrates the application of interval lagged weather data to predict mosquito abundances with a feasible accuracy, especially when related to weekly Cx. pipiens

  9. Infant rats can learn time intervals before the maturation of the striatum: evidence from odor fear conditioning

    PubMed Central

    Boulanger Bertolus, Julie; Hegoburu, Chloe; Ahers, Jessica L.; Londen, Elizabeth; Rousselot, Juliette; Szyba, Karina; Thévenet, Marc; Sullivan-Wilson, Tristan A.; Doyère, Valérie; Sullivan, Regina M.; Mouly, Anne-Marie

    2014-01-01

    Interval timing refers to the ability to perceive, estimate and discriminate durations in the range of seconds to minutes. Very little is currently known about the ontogeny of interval timing throughout development. On the other hand, even though the neural circuit sustaining interval timing is a matter of debate, the striatum has been suggested to be an important component of the system and its maturation occurs around the third post-natal (PN) week in rats. The global aim of the present study was to investigate interval timing abilities at an age for which striatum is not yet mature. We used odor fear conditioning, as it can be applied to very young animals. In odor fear conditioning, an odor is presented to the animal and a mild footshock is delivered after a fixed interval. Adult rats have been shown to learn the temporal relationships between the odor and the shock after a few associations. The first aim of the present study was to assess the activity of the striatum during odor fear conditioning using 2-Deoxyglucose autoradiography during development in rats. The data showed that although fear learning was displayed at all tested ages, activation of the striatum was observed in adults but not in juvenile animals. Next, we assessed the presence of evidence of interval timing in ages before and after the inclusion of the striatum into the fear conditioning circuit. We used an experimental setup allowing the simultaneous recording of freezing and respiration that have been demonstrated to be sensitive to interval timing in adult rats. This enabled the detection of duration-related temporal patterns for freezing and/or respiration curves in infants as young as 12 days PN during odor fear conditioning. This suggests that infants are able to encode time durations as well as and as quickly as adults while their striatum is not yet functional. Alternative networks possibly sustaining interval timing in infant rats are discussed. PMID:24860457

  10. Exertional responses to sprint interval training: a comparison of 30-sec. and 60-sec. conditions.

    PubMed

    Kilpatrick, Marcus W; Greeley, Samuel J

    2014-06-01

    The purpose of this study was to assess the effect of sprint interval training on rating of perceived exertion. 20 healthy participants (11 men, 9 women; M age = 23 yr.) completed a maximal cycle ergometer test and two high-intensity interval training cycling sessions. Each session utilized the same work-to-rest ratio (1:1), work intensity (90% max), recovery intensity (10% work intensity), and session duration (16 min.). Trials differed on duration of the interval segment, with a 30-sec. trial and a 60-sec. trial. Sessions required the same amount of total work over the duration of the trial. Rating of perceived exertion assessed before, during, and after exercise were higher for the 60-sec. trial than the 30-sec. trial despite no difference in total work. High intensity interval training trials utilizing the same total external work but differing in interval length produced different ratings of perceived exertion. Perceived exertion is significantly higher for sessions of exercise that utilize longer work intervals. These findings suggest that shorter intervals may produce more favorable exertional responses that could positively affect future behavior.

  11. Predicting Fatigue Lives Under Complex Loading Conditions

    NASA Technical Reports Server (NTRS)

    Mcgaw, Michael A.; Nelson, R. S.; Janitor, L. A.

    1995-01-01

    Cyclic Damage Accumulation (CDA) computer program performs high-temperature, low-cycle-fatigue life prediction for materials analysis. Designed to account for effects on creep-fatigue life of complex loadings involving such factors as thermomechanical fatigue, hold periods, wave-shapes, mean stresses, multiaxiality, cumulative damage, coatings, and environmental attack. Several features practical for application to actual component analysis using modern finite-element or boundary-element methods. Although developed for use in predicting crack-initiation lifetimes of gas-turbine-engine materials, also applied to other materials as well. Written in FORTRAN 77.

  12. Short-term load and wind power forecasting using neural network-based prediction intervals.

    PubMed

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2014-02-01

    Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

  13. An Interval-Valued Neural Network Approach for Uncertainty Quantification in Short-Term Wind Speed Prediction.

    PubMed

    Ak, Ronay; Vitelli, Valeria; Zio, Enrico

    2015-11-01

    We consider the task of performing prediction with neural networks (NNs) on the basis of uncertain input data expressed in the form of intervals. We aim at quantifying the uncertainty in the prediction arising from both the input data and the prediction model. A multilayer perceptron NN is trained to map interval-valued input data onto interval outputs, representing the prediction intervals (PIs) of the real target values. The NN training is performed by nondominated sorting genetic algorithm-II, so that the PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). Demonstration of the proposed method is given in two case studies: 1) a synthetic case study, in which the data have been generated with a 5-min time frequency from an autoregressive moving average model with either Gaussian or Chi-squared innovation distribution and 2) a real case study, in which experimental data consist of wind speed measurements with a time step of 1 h. Comparisons are given with a crisp (single-valued) approach. The results show that the crisp approach is less reliable than the interval-valued input approach in terms of capturing the variability in input.

  14. Reduced birth intervals following the birth of children with long-term illness: evidence supporting a conditional evolved response.

    PubMed

    Waynforth, David

    2015-10-01

    Human birth interval length is indicative of the level of parental investment that a child will receive: a short interval following birth means that parental resources must be split with a younger sibling during a period when the older sibling remains highly dependent on their parents. From a life-history theoretical perspective, it is likely that there are evolved mechanisms that serve to maximize fitness depending on context. One context that would be expected to result in short birth intervals, and lowered parental investment, is after a child with low expected fitness is born. Here, data drawn from a longitudinal British birth cohort study were used to test whether birth intervals were shorter following the birth of a child with a long-term health problem. Data on the timing of 4543 births were analysed using discrete-time event history analysis. The results were consistent with the hypothesis: birth intervals were shorter following the birth of a child diagnosed by a medical professional with a severe but non-fatal medical condition. Covariates in the analysis were also significantly associated with birth interval length: births of twins or multiple births, and relationship break-up were associated with significantly longer birth intervals.

  15. Bioaccumulation kinetics of organic xenobiotic pollutants in the freshwater invertebrate Gammarus pulex modeled with prediction intervals.

    PubMed

    Ashauer, Roman; Caravatti, Ivo; Hintermeister, Anita; Escher, Beate I

    2010-07-01

    Uptake and elimination rate constants, bioaccumulation factors, and elimination times in the freshwater arthropod Gammarus pulex were measured for 14 organic micropollutants covering a wide range of hydrophobicity (imidacloprid, aldicarb, ethylacrylate, 4,6-dinitro-o-cresol, carbofuran, malathion, 4-nitrobenzyl-chloride, 2,4-dichloroaniline, Sea-Nine, 2,4-dichlorophenol, diazinon, 2,4,5-trichlorophenol, 1,2,3-trichlorobenzene, and hexachlorobenzene; all 14C-labeled). The toxicokinetic parameters were determined by least-square fitting of a one-compartment first-order toxicokinetic model, followed by Markov Chain Monte Carlo parameter estimation. The parameter estimation methods used here account for decreasing aqueous concentrations during the exposure phase or increasing aqueous concentrations during the elimination phase of bioaccumulation experiments. It is not necessary to keep exposure concentrations constant or zero during uptake and elimination, respectively. Neither is it required to achieve steady state during the exposure phase; hence, tests can be shorter. Prediction intervals, which take the between-parameter correlation into account, were calculated for bioaccumulation factors and simulations of internal concentrations under variable exposure. The lipid content of Gammarus pulex was 1.3% of wet weight, consisting of 25% phospholipids and 75% triglycerides. Size-dependent bioaccumulation was observed for eight compounds, although the magnitudes of the relationships were too small to be of practical relevance. Elimination times ranged from 0.45 to 20 d, and bioaccumulation factors ranged from 1.7 to 4,449 L/kg. The identified compounds with unexpectedly long elimination times should be given priority in future studies investigating the biotransformation of these compounds.

  16. Predicting Comorbid Conditions and Trajectories using Social Health Records.

    PubMed

    Ji, Xiang; Ae Chun, Soon; Geller, James

    2016-05-05

    Many patients suffer from comorbidity conditions, for example, obese patients often develop type-2 diabetes and hypertension. In the US, 80% of Medicare spending is for managing patients with these multiple coexisting conditions. Predicting potential comorbidity conditions for an individual patient can promote preventive care and reduce costs. Predicting possible comorbidity progression paths can provide important insights into population heath and aid with decisions in public health policies. Discovering the comorbidity relationships is complex and difficult, due to limited access to Electronic Health Records by privacy laws. In this paper, we present a collaborative comorbidity prediction method to predict likely comorbid conditions for individual patients, and a trajectory prediction graph model to reveal progression paths of comorbid conditions. Our prediction approaches utilize patient generated health reports on online social media, called Social Health Records (SHR). The experimental results based on one SHR source show that our method is able to predict future comorbid conditions for a patient with coverage values of 48% and 75% for a top-20 and a top-100 ranked list, respectively. For risk trajectory prediction, our approach is able to reveal each potential progression trajectory between any two conditions and infer the confidence of the future trajectory, given any observed condition. The predicted trajectories are validated with existing comorbidity relations from the medical literature.

  17. Predicting Road Conditions with Internet Search.

    PubMed

    Askitas, Nikolaos

    2016-01-01

    Traffic congestion is an important problem both on an individual and on a societal level and much research has been done to explain and prevent their emergence. There are currently many systems which provide a reasonably good picture of actual road traffic by employing either fixed measurement points on highways or so called "floating car data" i.e. by using velocity and location data from roaming, networked, GPS enabled members of traffic. Some of these systems also offer forecasting of road conditions based on such historical data. To my knowledge there is as yet no system which offers advance notice on road conditions based on a signal which is guaranteed to occur in advance of these conditions and this is the novelty of this paper. Google Search intensity for the German word stau (i.e. traffic jam) peaks 2 hours ahead of the number of traffic jam reports as reported by the ADAC, a well known German automobile club and the largest of its kind in Europe. This is true both in the morning (7 am to 9 am) and in the evening (4 pm to 6 pm). The main result of this paper is then that after controlling for time-of-day and day-of-week effects we can still explain a significant additional portion of the variation of the number of traffic jam reports with Google Trends and we can thus explain well over 80% of the variation of road conditions using Google search activity. A one percent increase in Google stau searches implies a .4 percent increase of traffic jams. Our paper is a proof of concept that aggregate, timely delivered behavioural data can help fine tune modern societies and prompts for more research with better, more disaggregated data in order to also achieve practical solutions.

  18. Predicting Road Conditions with Internet Search

    PubMed Central

    2016-01-01

    Traffic congestion is an important problem both on an individual and on a societal level and much research has been done to explain and prevent their emergence. There are currently many systems which provide a reasonably good picture of actual road traffic by employing either fixed measurement points on highways or so called “floating car data” i.e. by using velocity and location data from roaming, networked, GPS enabled members of traffic. Some of these systems also offer forecasting of road conditions based on such historical data. To my knowledge there is as yet no system which offers advance notice on road conditions based on a signal which is guaranteed to occur in advance of these conditions and this is the novelty of this paper. Google Search intensity for the German word stau (i.e. traffic jam) peaks 2 hours ahead of the number of traffic jam reports as reported by the ADAC, a well known German automobile club and the largest of its kind in Europe. This is true both in the morning (7 am to 9 am) and in the evening (4 pm to 6 pm). The main result of this paper is then that after controlling for time-of-day and day-of-week effects we can still explain a significant additional portion of the variation of the number of traffic jam reports with Google Trends and we can thus explain well over 80% of the variation of road conditions using Google search activity. A one percent increase in Google stau searches implies a .4 percent increase of traffic jams. Our paper is a proof of concept that aggregate, timely delivered behavioural data can help fine tune modern societies and prompts for more research with better, more disaggregated data in order to also achieve practical solutions. PMID:27571518

  19. Increased Short-Term Variability of the QT Interval in Professional Soccer Players: Possible Implications for Arrhythmia Prediction

    PubMed Central

    Lengyel, Csaba; Orosz, Andrea; Hegyi, Péter; Komka, Zsolt; Udvardy, Anna; Bosnyák, Edit; Trájer, Emese; Pavlik, Gábor; Tóth, Miklós; Wittmann, Tibor; Papp, Julius Gy.; Varró, András; Baczkó, István

    2011-01-01

    Background Sudden cardiac death in competitive athletes is rare but it is significantly more frequent than in the normal population. The exact cause is seldom established and is mostly attributed to ventricular fibrillation. Myocardial hypertrophy and slow heart rate, both characteristic changes in top athletes in response to physical conditioning, could be associated with increased propensity for ventricular arrhythmias. We investigated conventional ECG parameters and temporal short-term beat-to-beat variability of repolarization (STVQT), a presumptive novel parameter for arrhythmia prediction, in professional soccer players. Methods Five-minute 12-lead electrocardiograms were recorded from professional soccer players (n = 76, all males, age 22.0±0.61 years) and age-matched healthy volunteers who do not participate in competitive sports (n = 76, all males, age 22.0±0.54 years). The ECGs were digitized and evaluated off-line. The temporal instability of beat-to-beat heart rate and repolarization were characterized by the calculation of short-term variability of the RR and QT intervals. Results Heart rate was significantly lower in professional soccer players at rest (61±1.2 vs. 72±1.5/min in controls). The QT interval was prolonged in players at rest (419±3.1 vs. 390±3.6 in controls, p<0.001). QTc was significantly longer in players compared to controls calculated with Fridericia and Hodges correction formulas. Importantly, STVQT was significantly higher in players both at rest and immediately after the game compared to controls (4.8±0.14 and 4.3±0.14 vs. 3.5±0.10 ms, both p<0.001, respectively). Conclusions STVQT is significantly higher in professional soccer players compared to age-matched controls, however, further studies are needed to relate this finding to increased arrhythmia propensity in this population. PMID:21526208

  20. Absolute beat-to-beat variability and instability parameters of ECG intervals: biomarkers for predicting ischaemia-induced ventricular fibrillation

    PubMed Central

    Sarusi, Annamária; Rárosi, Ferenc; Szűcs, Mónika; Csík, Norbert; Farkas, Attila S; Papp, Julius Gy; Varró, András; Forster, Tamás; Curtis, Michael J; Farkas, András

    2014-01-01

    Background and Purpose Predicting lethal arrhythmia liability from beat-to-beat variability and instability (BVI) of the ECG intervals is a useful technique in drug assessment. Most investigators use only arrhythmia-free ECGs for this. Recently, it was shown that drug-induced torsades de pointes (TdP) liability can be predicted more accurately from BVI measured irrespective of rhythm, even during arrhythmias (absolute BVI). The present study tested the broader applicability of this assessment by examining whether absolute BVI parameters predict another potential lethal arrhythmia, ischaemia-induced ventricular fibrillation (VF). Experimental Approach Langendorff-perfused rat hearts were subjected to regional ischaemia for 15 min. Absolute BVI parameters were derived from ECG intervals measured in 40 consecutive ventricular complexes (irrespective of rhythm) immediately preceding VF onset and compared with time-matched values in hearts not expressing VF. Key Results Increased frequency of non-sinus beats and ‘R on T’ arrhythmic beats, shortened mean RR and electrical diastolic intervals, and increased BVI of cycle length and repolarization predicted VF occurrence. Absolute BVI parameters that quantify variability of repolarization (e.g. ‘short-term variability’ of QT interval) had the best predictive power with high sensitivity and specificity. In contrast, VF was not predicted by any BVI parameter derived from the last arrhythmia-free interlude before VF. Conclusions and Implications The novel absolute BVI parameters that predicted TdP in rabbit also predict ischaemia-induced VF in rat, indicating a diagnostic and mechanistic congruence. Repolarization inhomogeneity represents a pivotal biomarker of ischaemia-induced VF. The newly validated biomarkers could serve as surrogates for VF in pre-clinical drug investigations. PMID:24417376

  1. Cues Produced by Reward and Nonreward and Temporal Cues Influence Responding in the Intertrial Interval and to the Conditioned Stimulus

    ERIC Educational Resources Information Center

    Capaldi, E. J.; Martins, Ana; Miller, Ronald M.

    2007-01-01

    Rats in a Pavlovian situation were trained under three different reward schedules, at either a 30 s or a 90 s intertrial interval (ITI): Consistent reward (C), 50% irregular reward (I), and single alternation of reward and nonrewarded trials (SA). Activity was recorded to the conditioned stimulus (CS) and in all 10 s bins in each ITI except the…

  2. Effects of Paradigm and Inter-Stimulus Interval on Age Differences in Eyeblink Classical Conditioning in Rabbits

    ERIC Educational Resources Information Center

    Woodruff-Pak, Diana S.; Seta, Susan E.; Roker, LaToya A.; Lehr, Melissa A.

    2007-01-01

    The aim of this study was to examine parameters affecting age differences in eyeblink classical conditioning in a large sample of young and middle-aged rabbits. A total of 122 rabbits of mean ages of 4 or 26 mo were tested at inter-stimulus intervals (ISIs) of 600 or 750 msec in the delay or trace paradigms. Paradigm affected both age groups…

  3. Cues Produced by Reward and Nonreward and Temporal Cues Influence Responding in the Intertrial Interval and to the Conditioned Stimulus

    ERIC Educational Resources Information Center

    Capaldi, E. J.; Martins, Ana; Miller, Ronald M.

    2007-01-01

    Rats in a Pavlovian situation were trained under three different reward schedules, at either a 30 s or a 90 s intertrial interval (ITI): Consistent reward (C), 50% irregular reward (I), and single alternation of reward and nonrewarded trials (SA). Activity was recorded to the conditioned stimulus (CS) and in all 10 s bins in each ITI except the…

  4. Effects of Paradigm and Inter-Stimulus Interval on Age Differences in Eyeblink Classical Conditioning in Rabbits

    ERIC Educational Resources Information Center

    Woodruff-Pak, Diana S.; Seta, Susan E.; Roker, LaToya A.; Lehr, Melissa A.

    2007-01-01

    The aim of this study was to examine parameters affecting age differences in eyeblink classical conditioning in a large sample of young and middle-aged rabbits. A total of 122 rabbits of mean ages of 4 or 26 mo were tested at inter-stimulus intervals (ISIs) of 600 or 750 msec in the delay or trace paradigms. Paradigm affected both age groups…

  5. Prediction bands and intervals for the scapulo-humeral coordination based on the Bootstrap and two Gaussian methods.

    PubMed

    Cutti, A G; Parel, I; Raggi, M; Petracci, E; Pellegrini, A; Accardo, A P; Sacchetti, R; Porcellini, G

    2014-03-21

    Quantitative motion analysis protocols have been developed to assess the coordination between scapula and humerus. However, the application of these protocols to test whether a subject's scapula resting position or pattern of coordination is "normal", is precluded by the unavailability of reference prediction intervals and bands, respectively. The aim of this study was to present such references for the "ISEO" protocol, by using the non-parametric Bootstrap approach and two parametric Gaussian methods (based on Student's T and Normal distributions). One hundred and eleven asymptomatic subjects were divided into three groups based on their age (18-30, 31-50, and 51-70). For each group, "monolateral" prediction bands and intervals were computed for the scapulo-humeral patterns and the scapula resting orientation, respectively. A fourth group included the 36 subjects (42 ± 13 year-old) for whom the scapulo-humeral coordination was measured bilaterally, and "differential" prediction bands and intervals were computed, which describe right-to-left side differences. Bootstrap and Gaussian methods were compared using cross-validation analyses, by evaluating the coverage probability in comparison to a 90% target. Results showed a mean coverage for Bootstrap from 86% to 90%, compared to 67-70% for parametric bands and 87-88% for parametric intervals. Bootstrap prediction bands showed a distinctive change in amplitude and mean pattern related to age, with an increase toward scapula retraction, lateral rotation and posterior tilt. In conclusion, Bootstrap ensures an optimal coverage and should be preferred over parametric methods. Moreover, the stratification of "monolateral" prediction bands and intervals by age appears relevant for the correct classification of patients. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Periodic boundary conditions for KdV-Burgers equation on an interval

    NASA Astrophysics Data System (ADS)

    Samokhin, Alexey

    2017-03-01

    For the KdV-Burgers equation on a finite interval the development of a regular profile starting from a constant one under a periodic perturbation on the boundary is studied. The equation describes a medium which is both dissipative and dispersive. For an appropriate combination of dispersion and dissipation the asymptotic profile looks like a periodical chain of shock fronts with a decreasing amplitude (similarly to the Burgers equation case). But due to dispersion each such front is followed by increasing oscillation leading to the next shock-like the ninth wave in rough seas. The development of such a profile is preceded by an initial shock of a constant height.

  7. Effect of a retention interval between pre-exposure and conditioning on latent inhibition in humans using a blink conditioning procedure.

    PubMed

    De la Casa Rivas, Luis Gonzalo; Traverso Arcos, Luis Miguel; Márquez Zamora, Raúl

    2010-11-01

    Latent inhibition, retarded learning after pre-exposure to the to-be-conditioned stimulus, was examined using a blink conditioned procedure in humans. Experiment 1 showed that the procedure is suited to inducing the latent inhibition effect. In Experiment 2, the introduction of a 3-minute interval between pre-exposure and conditioning phases attenuated latent inhibition. These results contribute to identify the mechanisms involved in pre-exposure and subsequent conditioning of a stimulus, which is particularly important if we bear in mind that latent inhibition has been used repeatedly as an instrument to analyze the course of attentional processes in normal and pathological populations.

  8. An Aquatic Decomposition Scoring Method to Potentially Predict the Postmortem Submersion Interval of Bodies Recovered from the North Sea.

    PubMed

    van Daalen, Marjolijn A; de Kat, Dorothée S; Oude Grotebevelsborg, Bernice F L; de Leeuwe, Roosje; Warnaar, Jeroen; Oostra, Roelof Jan; M Duijst-Heesters, Wilma L J

    2017-03-01

    This study aimed to develop an aquatic decomposition scoring (ADS) method and investigated the predictive value of this method in estimating the postmortem submersion interval (PMSI) of bodies recovered from the North Sea. This method, consisting of an ADS item list and a pictorial reference atlas, showed a high interobserver agreement (Krippendorff's alpha ≥ 0.93) and hence proved to be valid. This scoring method was applied to data, collected from closed cases-cases in which the postmortal submersion interval (PMSI) was known-concerning bodies recovered from the North Sea from 1990 to 2013. Thirty-eight cases met the inclusion criteria and were scored by quantifying the observed total aquatic decomposition score (TADS). Statistical analysis demonstrated that TADS accurately predicts the PMSI (p < 0.001), confirming that the decomposition process in the North Sea is strongly correlated to time.

  9. Relation between conditioned stimulus-elicit responses and unconditioned response diminution in long-interval human heart-rate classical conditioning.

    PubMed

    Marcos, J L; Redondo, J

    2001-05-01

    Previous research on electrodermal conditioning suggests that the conditioned diminution of the unconditioned response (UR) has an associative basis. The aim of this experiment was to test whether this phenomenon also occurs in heart rate (HR) classical conditioning. For this purpose, a differential classical conditioning was performed. The conditioned stimuli (CSs) were geometrical shapes (the CS+ was a square and the CS- was a triangle) displayed on a computer screen and a burst of white noise was used as unconditioned stimulus (US). For analysis of the conditioned response (CR) components, an interval between CS+ and US of 8 seconds was used. After the acquisition phase, participants were tested using trials with the US preceded either by a CS+, a CS-, or a neutral stimulus (a circle). The results showed conditioned diminution of the UR and suggest that the second heart rate deceleration component (D2) is responsible for the occurrence of this phenomenon.

  10. How Children Use Examples to Make Conditional Predictions

    ERIC Educational Resources Information Center

    Kalish, Charles W.

    2010-01-01

    Two experiments explored children's and adults' use of examples to make conditional predictions. In Experiment 1 adults (N = 20) but not 4-year-olds (N = 21) or 8-year-olds (N =1 8) distinguished predictable from unpredictable features when features were partially correlated (e.g., necessary but not sufficient). Children did make reliable…

  11. Super-Latent Inhibition of Conditioned Taste Preference with a Long Retention Interval

    ERIC Educational Resources Information Center

    De la Casa, L. G.; Marquez, R.; Lubow, R. E.

    2009-01-01

    A long delay inserted between conditioning and test phases of a 3-stage Latent Inhibition (LI) procedure produces differential effects on LI depending on the delay context. Thus, enhanced LI has been obtained when the delay is spent in a context that is different from the remaining experimental contexts, but not when it is the same. The present…

  12. Phonation Interval Modification and Speech Performance Quality during Fluency-Inducing Conditions by Adults Who Stutter

    ERIC Educational Resources Information Center

    Ingham, Roger J.; Bothe, Anne K.; Wang, Yuedong; Purkhiser, Krystal; New, Anneliese

    2012-01-01

    Purpose: To relate changes in four variables previously defined as characteristic of normally fluent speech to changes in phonatory behavior during oral reading by persons who stutter (PWS) and normally fluent controls under multiple fluency-inducing (FI) conditions. Method: Twelve PWS and 12 controls each completed 4 ABA experiments. During A…

  13. Phonation Interval Modification and Speech Performance Quality during Fluency-Inducing Conditions by Adults Who Stutter

    ERIC Educational Resources Information Center

    Ingham, Roger J.; Bothe, Anne K.; Wang, Yuedong; Purkhiser, Krystal; New, Anneliese

    2012-01-01

    Purpose: To relate changes in four variables previously defined as characteristic of normally fluent speech to changes in phonatory behavior during oral reading by persons who stutter (PWS) and normally fluent controls under multiple fluency-inducing (FI) conditions. Method: Twelve PWS and 12 controls each completed 4 ABA experiments. During A…

  14. Super-Latent Inhibition of Conditioned Taste Preference with a Long Retention Interval

    ERIC Educational Resources Information Center

    De la Casa, L. G.; Marquez, R.; Lubow, R. E.

    2009-01-01

    A long delay inserted between conditioning and test phases of a 3-stage Latent Inhibition (LI) procedure produces differential effects on LI depending on the delay context. Thus, enhanced LI has been obtained when the delay is spent in a context that is different from the remaining experimental contexts, but not when it is the same. The present…

  15. A Theory of Conditional Information for Probabilistic Inference in Intelligent Systems: 1. Interval of Events Approach

    DTIC Science & Technology

    1994-06-01

    This paper emphasizes the need to develop further probability theory at the service of probabilistic intelligent systems . In the field of...events,’ compatible with all conditional probability quantifications. We specify applications of this theory to various problems in intelligent systems . The

  16. Interstimulus Interval and Delivery Cues Influence Timed Conditioned Responding in Rats

    ERIC Educational Resources Information Center

    Williams, Douglas A.; Chubala, Chrissy M.; Mather, Amber A.; Johns, Kenneth W.

    2009-01-01

    Appetitive contextual excitation supported by intertrial unconditioned stimuli was more easily overcome by timed conditioned responding in rats using quiet (Experiment 1) rather than noisy (Experiment 2) food pellet deliveries. Head-entry responding in acquisition peaked above the contextual baseline when pellet delivery occurred 10, 30, 60, or 90…

  17. Interstimulus Interval and Delivery Cues Influence Timed Conditioned Responding in Rats

    ERIC Educational Resources Information Center

    Williams, Douglas A.; Chubala, Chrissy M.; Mather, Amber A.; Johns, Kenneth W.

    2009-01-01

    Appetitive contextual excitation supported by intertrial unconditioned stimuli was more easily overcome by timed conditioned responding in rats using quiet (Experiment 1) rather than noisy (Experiment 2) food pellet deliveries. Head-entry responding in acquisition peaked above the contextual baseline when pellet delivery occurred 10, 30, 60, or 90…

  18. Prediction of Fluid Responsiveness by a Non-invasive Respiratory Systolic Time Interval Variation Using Heart Sound Signals in Recipients Undergoing Liver Transplantation.

    PubMed

    Kim, S-H; Moon, Y-J; Kim, J-W; Song, J-G; Hwang, G-S

    2017-06-01

    The fluid management of cirrhotic patients undergoing liver transplantation (LT) is challenging. Phonocardiography, a graphic recording of heart sounds, provides valuable information concerning heart function and hemodynamic condition. We assessed whether the systolic time interval (STI) and its respiratory variation could predict fluid responsiveness in cirrhotic patients undergoing LT. Thirty LT recipients who needed volume expansion were included. The fluid challenge consisted of 500 mL 5% albumin administered over a period of 10 minutes. STI was measured as the time interval between the maximal amplitude of each heart sound corrected with the corresponding RR interval (cSTI). The respiratory variation in STI (STV) induced by mechanical ventilation was calculated. Responders were defined as those showing a ≥10% increase in stroke volume index after volume expansion. In all, 14 of the 30 patients were responders. Significant increases in cSTI were observed after volume expansion in both responders (P < .001) and non-responders (P = .008). Responders showed significant decreases in STV (11.1% ± 4.3% vs 6.1% ± 2.6%, P < .001) after fluid loading, whereas STV in non-responders remained unchanged (6.4% ± 2.6% vs 6.4% ± 4.2%, P = .86). A cut-off value of ≥7.5% STV from baseline could predict fluid responsiveness with an area under the receiver operating characteristic curve of 0.804 (95% confidence interval, 0.618-0.925). Intra-operative STV can predict fluid responsiveness in patients undergoing LT. Beat-to-beat monitoring of STI and STV may be useful as a non-invasive hemodynamic index and for fluid management. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Autonomic Function Predicts Fitness Response to Short-Term High-Intensity Interval Training.

    PubMed

    Kiviniemi, A M; Tulppo, M P; Eskelinen, J J; Savolainen, A M; Kapanen, J; Heinonen, I H A; Hautala, A J; Hannukainen, J C; Kalliokoski, K K

    2015-11-01

    We tested the hypothesis that baseline cardiac autonomic function and its acute response to all-out interval exercise explains individual fitness responses to high-intensity interval training (HIT). Healthy middle-aged sedentary men performed HIT (n=12, 4-6×30 s of all-out cycling efforts with 4-min recovery) or aerobic training (AET, n=9, 40-60 min at 60% of peak workload in exercise test [Loadpeak]), comprising 6 sessions within 2 weeks. Low (LF) and high frequency (HF) power of R-R interval oscillation were analyzed from data recorded at supine and standing position (5+5 min) every morning during the intervention. A significant training effect (p< 0.001), without a training*group interaction, was observed in Loadpeak and peak oxygen consumption (VO2peak). Pre-training supine LF/HF ratio, an estimate of sympathovagal balance, correlated with training outcome in Loadpeak (Spearman's rho [rs]=-0.74, p=0.006) and VO2peak (rs=- 0.59, p=0.042) in the HIT but not the AET group. Also, the mean change in the standing LF/HF ratio in the morning after an acute HIT exercise during the 1(st) week of intervention correlated with training response in Loadpeak (rs=- 0.68, p=0.014) and VO2peak (rs=-0.60, p=0.039) with HIT but not with AET. In conclusion, pre-training cardiac sympathovagal balance and its initial alterations in response to acute HIT exercise were related to fitness responses to short-term HIT.

  20. Developing equations to predict surface dose and therapeutic interval in bolused electron fields: A Monte Carlo Study

    NASA Astrophysics Data System (ADS)

    Jabbari, Nasrollah; Khalkhali, Hamid Reza

    2017-07-01

    In this research, we aim to investigate the influence of different materials, as a bolus, on the low-energy electron beam dose distributions and to develop equations for predicting surface dose based on bolus thickness, as well as the therapeutic interval based on surface dose. All the Monte Carlo (MC) calculations and measurements were conducted on a Siemens PRIMUS linac. Based on EGSnrc MC code, BEAMnrc system was used to model a Siemens linac and generate phase-space files for three electron beams (6, 8, and 10 MeV). The particles were transported from the phase-space files to the bolus materials and the simulated water phantom using DOSXYZnrc. Various materials with different thicknesses were examined as a bolus, and appropriate equations were determined for each material and electron beam. The comparison of percent depth dose (PDD) curves and beam profiles, using MC, with the measured data demonstrated that the calculated values properly matched with the measurements. The results indicated that the use of bolus materials with the density of higher than soft tissue can increase both surface dose and therapeutic interval simultaneously. This finding arises from the fact that the required bolus thickness for achieving the therapeutic surface dose decreases in the case of high-density materials. Two series of prediction equations were proposed for predicting the surface dose based on bolus thickness and the therapeutic interval based on surface dose. These equations are able to calculate properly the bolus thickness required for producing a therapeutic surface dose (above 90%) for any therapeutic interval.

  1. Bayesian multilevel discrete interval hazard analysis to predict dichlorodiphenyldichloroethylene mortality in Hyalella azteca based on body residues.

    PubMed

    Lee, Jong-Hyeon; Stow, Craig A; Landrum, Peter F

    2009-11-01

    We exposed Hyalella azteca to p,p'-dichlorodiphenyldichloroethylene for intervals of 1 to 4 d and followed mortality out to 10 d. Mortality was determined as the cessation of heartbeat; dead organism body residue was determined daily. To model mortality probability, body residues of the living organisms were estimated using published kinetic data with concentration-dependent rate constants. The estimated residues compared favorably with measured residues in the dead organisms (predicted body residue = 1.302 ± 0.142 measured body residue + 10.351 ± 15.766, r² = 0.64, n = 50). The response data were collected at discrete intervals; thus, it was not possible to determine the exact time of death for organisms. Consequently, we analyzed the mortality data using discrete interval analysis, in a Bayesian hierarchical framework, with body residue as the dose metric. The predicted body residues to produce mortality were similar across the duration of exposure when postexposure mortality was considered. The concentration for 50% mortality was 0.47 μmol/g (148.6 tg/g, range 0.32-0.66 μmol/g), and predictions of response indicted 95% (range 73-99.9%) mortality at 0.79 μmol/g (250 μg/g) and 4% (range 1.2-9.6%) mortality at 0.16 μmol/g (50 μg/g). The lethal residue for 50% mortality based on interval analysis for short-term exposures with postexposure mortality resulted in values similar to long-term continuous exposures for exposure durations of more than 600 h.

  2. Low-latitude Pi2 pulsations during intervals of quiet geomagnetic conditions (Kp≤1)

    NASA Astrophysics Data System (ADS)

    Kwon, H.-J.; Kim, K.-H.; Jun, C.-W.; Takahashi, K.; Lee, D.-H.; Lee, E.; Jin, H.; Seon, J.; Park, Y.-D.; Hwang, J.

    2013-10-01

    It has been reported that Pi2 pulsations can be excited under extremely quiet geomagnetic conditions (Kp=0). However, there have been few comprehensive reports of Pi2 pulsations in such a near ground state magnetosphere. To understand the characteristics of quiet-time Pi2 pulsations, we statistically examined Pi2 events observed on the nightside between 1800 and 0600 local time at the low-latitude Bohyun (BOH, L = 1.35) station in South Korea. We chose year 2008 for analysis because geomagnetic activity was unusually low in that year. A total of 982 Pi2 events were identified when Kp≤1. About 80% of the Pi2 pulsations had a period between 110 and 300 s, which significantly differs from the conventional Pi2 period from 40 to 150 s. Comparing Pi2 periods and solar wind conditions, we found that Pi2 periods decrease with increasing solar wind speed, consistent with the result of Troitskaya (1967). The observed wave properties are discussed in terms of plasmaspheric resonance, which has been proposed for Pi2 pulsations in the inner magnetosphere. We also found that Pi2 pulsations occur quasi-periodically with a repetition period of ˜23-38 min. We will discuss what determines such a recurrence time of Pi2 pulsations under quiet geomagnetic conditions.

  3. Development of Predictive Equations Based on Pavement Condition Index Data

    DTIC Science & Technology

    1992-03-01

    solutions to eliminate existing problems. A Spavement management system not only evaluates the present condition of a pavement but predicts its future...structural performance [1]. Condition rating data collected periodically will track the performance of a pavement. Most airports presently utilize...three primary objectives of rating a pavement based on the PCI method: (1) Determine present condition of the pavement in terms of the apparent

  4. Intermittent Drug Dosing Intervals Guided by the Operational Multiple Dosing Half Lives for Predictable Plasma Accumulation and Fluctuation

    PubMed Central

    Grover, Anita; Benet, Leslie Z.

    2013-01-01

    Intermittent drug dosing intervals are usually initially guided by the terminal pharmacokinetic half life and are dependent on drug formulation. For chronic multiple dosing and for extended release dosage forms, the terminal half life often does not predict the plasma drug accumulation or fluctuation observed. We define and advance applications for the operational multiple dosing half lives for drug accumulation and fluctuation after multiple oral dosing at steady-state. Using Monte Carlo simulation, our results predict a way to maximize the operational multiple dosing half lives relative to the terminal half life by using a first-order absorption rate constant close to the terminal elimination rate constant in the design of extended release dosage forms. In this way, drugs that may be eliminated early in the development pipeline due to a relatively short half life can be formulated to be dosed at intervals three times the terminal half life, maximizing compliance, while maintaining tight plasma concentration accumulation and fluctuation ranges. We also present situations in which the operational multiple dosing half lives will be especially relevant in the determination of dosing intervals, including for drugs that follow a direct PKPD model and have a narrow therapeutic index, as the rate of concentration decrease after chronic multiple dosing (that is not the terminal half life) can be determined via simulation. These principles are illustrated with case studies on valproic acid, diazepam, and anti-hypertensives. PMID:21499748

  5. Quantitative analysis of ventricular ectopic beats in short-term RR interval recordings to predict imminent ventricular tachyarrhythmia.

    PubMed

    Martínez-Alanis, Marisol; Ruiz-Velasco, Silvia; Lerma, Claudia

    2016-12-15

    Most approaches to predict ventricular tachyarrhythmias which are based on RR intervals consider only sinus beats, excluding premature ventricular complexes (PVCs). The method known as heartprint, which analyses PVCs and their characteristics, has prognostic value for fatal arrhythmias on long recordings of RR intervals (>70,000 beats). To evaluate characteristics of PVCs from short term recordings (around 1000 beats) and their prognostic value for imminent sustained tachyarrhythmia. We analyzed 132 pairs of short term RR interval recordings (one before tachyarrhythmia and one control) obtained from 78 patients. Patients were classified into two groups based on the history of accelerated heart rate (HR) (HR>90bpm) before a tachyarrhythmia episode. Heartprint indexes, such as mean coupling interval (meanCI) and the number of occurrences of the most prevalent form of PVCs (SNIB) were calculated. The predictive value of all the indexes and of the combination of different indexes was calculated. MeanCI shorter than 482ms and the occurrence of more repetitive arrhythmias (sNIB≥2.5), had a significant prognostic value for patients with accelerated heart rate: adjusted odds ratio of 2.63 (1.33-5.17) for meanCI and 2.28 (1.20-4.33) for sNIB. Combining these indexes increases the adjusted odds ratio: 10.94 (3.89-30.80). High prevalence of repeating forms of PVCs and shorter CI are potentially useful risk markers of imminent ventricular tachyarrhythmia. Knowing if a patient has history of VT/VF preceded by accelerated HR, improves the prognostic value of these risk markers. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  6. Origin of complexity and conditional predictability in cellular automata.

    PubMed

    García-Morales, Vladimir

    2013-10-01

    A simple mechanism for the emergence of complexity in cellular automata out of predictable dynamics is described. This leads to introduce the concept of conditional predictability for systems whose trajectory can only be piecewise known. The mechanism is used to construct a cellular automaton model for discrete chimeralike states, where synchrony and incoherence in an ensemble of identical oscillators coexist. The incoherent region is shown to have a periodicity that is three orders of magnitude longer than the period of the synchronous oscillation.

  7. Survival of Pochonia chlamydosporia on the soil surface after different exposure intervals at ambient conditions.

    PubMed

    Fernandes, Rafael Henrique; Lopes, Everaldo Antônio; Borges, Darlan Ferreira; Bontempo, Amanda Ferreira; Zanuncio, José Cola; Serrão, José Eduardo

    2017-09-25

    Exposure of the nematophagous fungus Pochonia chlamydosporia to solar radiation and elevated temperatures before being incorporated into the soil can reduce its survival and efficiency as biocontrol agent. A field experiment was carried out to assess the effect of the exposure period on the viability of P. chlamydosporia applied on the soil surface. A commercial bionematicide based on P. chlamydosporia was sprayed on soil, and soil samples were collected before and at 0, 30, 60, 90, 120, and 150min after fungal application. Relative humidity (RH), the irradiance (IR), air temperature (AT), and soil temperature (ST) were recorded. The number of P. chlamydosporia colony forming units (CFUs) was evaluated after 20 days of incubation. P. chlamydosporia survival decreased over the time of exposure on the soil surface. Overall, the number of CFUs decreased by more than 90% at 150min after application. Exposure to RH ≥61%, ST and AT between 25-35°C and 19-29°C, and IR between 1172 and 2126μmol of photons m(-2)s(-1) induced a negative exponential effect on the survival of the fungus over the time. Exposure to climatic conditions on the soil surface reduces P. chlamydosporia viability. Copyright © 2017 Asociación Española de Micología. Publicado por Elsevier España, S.L.U. All rights reserved.

  8. Technical note: A mathematical function to predict daily milk yield of dairy cows in relation to the interval between milkings.

    PubMed

    Klopčič, M; Koops, W J; Kuipers, A

    2013-09-01

    The milk production of a dairy cow is characterized by lactation production, which is calculated from daily milk yields (DMY) during lactation. The DMY is calculated from one or more milkings a day collected at the farm. Various milking systems are in use today, resulting in one or many recorded milk yields a day, from which different calculations are used to determine DMY. The primary objective of this study was to develop a mathematical function that described milk production of a dairy cow in relation to the interval between 2 milkings. The function was partly based on the biology of the milk production process. This function, called the 3K-function, was able to predict milk production over an interval of 12h, so DMY was twice this estimate. No external information is needed to incorporate this function in methods to predict DMY. Application of the function on data from different milking systems showed a good fit. This function could be a universal tool to predict DMY for a variety of milking systems, and it seems especially useful for data from robotic milking systems. Further study is needed to evaluate the function under a wide range of circumstances, and to see how it can be incorporated in existing milk recording systems. A secondary objective of using the 3K-function was to compare how much DMY based on different milking systems differed from that based on a twice-a-day milking. Differences were consistent with findings in the literature.

  9. Predicting a donor's likelihood of donating within a preselected time interval.

    PubMed

    Flegel, W A; Besenfelder, W; Wagner, F F

    2000-09-01

    The procurement of some advanced blood components, like quarantined plasma units, depends critically on retesting the donor within a fixed time frame. For health care systems, such as that in Germany, with mandatory retesting of donors before plasma release, the reliable identification of donors who are more likely to return in time has an immense practical implication, because their blood components could be preferably selected for quarantine purposes. The donation histories of about 760 000 donors with 4910 000 donation attempts were analysed. We developed a logistic regression model to calculate a probability of donation, p(Dts-te), within a preselected time frame (ts-te). The donation history was compounded in a score and shown to be very useful for determining p(Dts-te). A logistic regression model was developed with score and donor status as parameters; different regression coefficients applied to first-time-donors (ftd) and to repeat donors (intercept, int, and score factor, scf ). This model allowed us to determine the probability of donation, p(Dts-te), within a preselected time interval, e.g. 6-9 months after an index donation. The p(Dts-te) can be calculated for any donor of blood services. The p(D170-275 days) ranged from about 22% to 86% for any index donation in 1996/97. First-time donors had a p(D170-275 days) of 33% and were more likely to return within the time interval than certain subsets of repeat donors who can be defined by our model. We provided a technical procedure to increase the rate of plasma unit release after quarantine storage and showed the usefulness of our procedure for blood component management, if quarantine storage is required. By applying the model to our current plasma quarantine programme we could retrieve about 30% more units, which would represent about 30 000 units per year, without incurring additional costs. General implications for blood collection, like planning blood drives, were discussed. The whole demand of a

  10. The Influence of Prior Handling on the Effective CS-US Interval in Long-Trace Taste-Aversion Conditioning in Rats

    ERIC Educational Resources Information Center

    Hinderliter, Charles F.; Andrews, Amy; Misanin, James R.

    2012-01-01

    In conditioned taste aversion (CTA), a taste, the conditioned stimulus (CS), is paired with an illness-inducing stimulus, the unconditioned stimulus (US), to produce CS-US associations at very long (hours) intervals, a result that appears to violate the law of contiguity. The specific length of the maximum effective trace interval that has been…

  11. The Influence of Prior Handling on the Effective CS-US Interval in Long-Trace Taste-Aversion Conditioning in Rats

    ERIC Educational Resources Information Center

    Hinderliter, Charles F.; Andrews, Amy; Misanin, James R.

    2012-01-01

    In conditioned taste aversion (CTA), a taste, the conditioned stimulus (CS), is paired with an illness-inducing stimulus, the unconditioned stimulus (US), to produce CS-US associations at very long (hours) intervals, a result that appears to violate the law of contiguity. The specific length of the maximum effective trace interval that has been…

  12. An Evaluation of Conditioning Data for Solute Transport Prediction

    SciTech Connect

    Scheibe, Timothy D.; Chien, Yi-Ju

    2003-03-01

    The large and diverse body of subsurface characterization data generated at a field research site near Oyster, Virginia provides a unique opportunity to test the impact of conditioning data of various types on predictions of flow and transport. Bromide breakthrough curves (BTCs) were measured during a forced-gradient local-scale injection experiment conducted in 1999. Observed BTCs are available at 140 sampling points in a three dimensional array within the transport domain. A detailed three-dimensional numerical model is used to simulate breakthrough curves at the same locations as the observed BTCs under varying assumptions regarding the character of hydraulic conductivity spatial distributions, and variable amounts and types of conditioning data. We present comparative results of six different cases ranging from simple (deterministic homogeneous models) to complex (stochastic indicator simulation conditioned to cross-borehole geophysical observations). Quantitative measures of model goodness-of-fit are presented. The results show that conditioning to a large number of small-scale measurements does not significantly improve model predictions, and may lead to biased or overly confident predictions. However, conditioning to geophysical interpretations with larger spatial support significantly improves the accuracy and precision of model predictions. In all cases, the effects of model error appear to be significant in relation to parameter uncertainty.

  13. Improvements in heat tolerance induced by interval running training in the heat and in sweat clothing in cool conditions.

    PubMed

    Dawson, B; Pyke, F S; Morton, A R

    1989-01-01

    To compare the effectiveness of training in heat and in sweat clothing in cool conditions on improving heat tolerance, two groups of active subjects (n = 6 in each) performed an interval running heat-tolerance test before and after a 7-day experimental treatment. On each treatment day the subjects attempted to complete 4 x 15 min interval treadmill running periods (a 7.5 s effort every 30 s, on 15 km h-1, 15% grade; the same exercise format as the heat-tolerance test), which were interspersed with 5-min recovery periods (total time each day = 80 min). Group 1 (heat) ran in shorts, socks and shoes in hot humid conditions, and Group 2 (sweat clothing) ran in cool conditions dressed in shorts, socks and T-shirt covered by a polyester-cotton tracksuit, over which was worn 100% nylon spray-proof pants and jacket (cotton lined) with an acrylic cloth bobble hat (beanie) on the head. Both groups displayed changes typical of heat acclimatization over the 7-day period, with significant decreases in final rectal temperature (Tr) and heart rate (HR) being evident, but no change in sweat loss. Mean skin temperature (Tsk) was similar in both groups during the training sessions (heat group: 34.8-35.7 degrees C; sweat clothing group 34.9-35.5 degrees C). After the heat-tolerance test, both groups had significantly lower Tr, Tsk and HR values than before, and sweating sensitivity (g m-2 h-1 degrees C rise in Tr) was significantly increased. There was only one significant difference between the two groups (Tsk, 20th min value). It was concluded that training in sweat clothing in cool conditions can provide the same improvements in heat tolerance as training in hot humid conditions where a fixed exercise intensity and duration are used.

  14. Conditions for predicting quasistationary states by rearrangement formula.

    PubMed

    Yamaguchi, Yoshiyuki Y; Ogawa, Shun

    2015-10-01

    Predicting the long-lasting quasistationary state for a given initial state is one of central issues in Hamiltonian systems having long-range interaction. A recently proposed method is based on the Vlasov description and uniformly redistributes the initial distribution along contours of the asymptotic effective Hamiltonian, which is defined by the obtained quasistationary state and is determined self-consistently. The method, to which we refer as the rearrangement formula, was suggested to give precise prediction under limited situations. Restricting initial states consisting of a spatially homogeneous part and small perturbation, we numerically reveal two conditions that the rearrangement formula prefers: One is a no Landau damping condition for the unperturbed homogeneous part, and the other comes from the Casimir invariants. Mechanisms of these conditions are discussed. Clarifying these conditions, we validate to use the rearrangement formula as the response theory for an external field, and we shed light on improving the theory as a nonequilibrium statistical mechanics.

  15. Conditions for predicting quasistationary states by rearrangement formula

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Yoshiyuki Y.; Ogawa, Shun

    2015-10-01

    Predicting the long-lasting quasistationary state for a given initial state is one of central issues in Hamiltonian systems having long-range interaction. A recently proposed method is based on the Vlasov description and uniformly redistributes the initial distribution along contours of the asymptotic effective Hamiltonian, which is defined by the obtained quasistationary state and is determined self-consistently. The method, to which we refer as the rearrangement formula, was suggested to give precise prediction under limited situations. Restricting initial states consisting of a spatially homogeneous part and small perturbation, we numerically reveal two conditions that the rearrangement formula prefers: One is a no Landau damping condition for the unperturbed homogeneous part, and the other comes from the Casimir invariants. Mechanisms of these conditions are discussed. Clarifying these conditions, we validate to use the rearrangement formula as the response theory for an external field, and we shed light on improving the theory as a nonequilibrium statistical mechanics.

  16. The Prediction of the Motion of Atens, Apollos and Amors over Long Intervals of Time

    NASA Astrophysics Data System (ADS)

    Wlodarczyk, I.

    2002-01-01

    Equations of motion of 930 Atens, Apollos and Amors (AAA) were integrated 300,000 years forward using RA15 Everhart method (Everhart, 1974). The Osterwinter model of Solar System was used (Osterwinter and Cohen, 1972). The differences in mean anomaly between unchanged and changed orbits were calculated. The changed orbits were constructed by adding or subtracting to the starting orbital elements one after the other errors of determination of orbital elements. When the differences in mean anomaly were greater than 360 deg. then computations were stopped. In almost all cases after about 1000 years in forwards or backwards integrations differences in mean anomaly between neighbors orbits growth rapidly. It denotes that it is impossible to predict behavior of asteroids outside this time. This time I have called time of stability.

  17. Effects of paradigm and inter-stimulus interval on age differences in eyeblink classical conditioning in rabbits

    PubMed Central

    Woodruff-Pak, Diana S.; Seta, Susan E.; Roker, LaToya A.; Lehr, Melissa A.

    2007-01-01

    The aim of this study was to examine parameters affecting age differences in eyeblink classical conditioning in a large sample of young and middle-aged rabbits. A total of 122 rabbits of mean ages of 4 or 26 mo were tested at inter-stimulus intervals (ISIs) of 600 or 750 msec in the delay or trace paradigms. Paradigm affected both age groups dramatically, with superior performance in the delay paradigm. ISI was salient as middle-aged rabbits were significantly impaired in 750-msec compared with 600-msec delays, and young rabbits were significantly less impaired in 600-msec than in 750-msec trace. Young rabbits performed equally well at both delay ISIs, and consequently, there were significant age differences in 750-msec but not in 600-msec delays. Middle-aged rabbits performed poorly at both 600- and 750-msec trace, resulting in significant age differences in 600-msec but not in 750-msec trace. Timing of the conditioned response has been associated with cerebellar cortical function. Normal aging of the cerebellar cortex likely contributed to the magnitude of the effect of ISI in delay conditioning in middle-aged rabbits. Results demonstrate that the magnitude of age differences in eyeblink conditioning can be enlarged or eliminated by ISI and paradigm. PMID:17522017

  18. Evaluation of techniques for estimating the power spectral density of RR-intervals under paced respiration conditions.

    PubMed

    Schaffer, Thorsten; Hensel, Bernhard; Weigand, Christian; Schüttler, Jürgen; Jeleazcov, Christian

    2014-10-01

    Heart rate variability (HRV) analysis is increasingly used in anaesthesia and intensive care monitoring of spontaneous breathing and mechanical ventilated patients. In the frequency domain, different estimation methods of the power spectral density (PSD) of RR-intervals lead to different results. Therefore, we investigated the PSD estimates of fast Fourier transform (FFT), autoregressive modeling (AR) and Lomb-Scargle periodogram (LSP) for 25 young healthy subjects subjected to metronomic breathing. The optimum method for determination of HRV spectral parameters under paced respiration was identified by evaluating the relative error (RE) and the root mean square relative error (RMSRE) for each breathing frequency (BF) and spectral estimation method. Additionally, the sympathovagal balance was investigated by calculating the low frequency/high frequency (LF/HF) ratio. Above 7 breaths per minute, all methods showed a significant increase in LF/HF ratio with increasing BF. On average, the RMSRE of FFT was lower than for LSP and AR. Therefore, under paced respiration conditions, estimating RR-interval PSD using FFT is recommend.

  19. Predicted Turbine Heat Transfer for a Range of Test Conditions

    NASA Technical Reports Server (NTRS)

    Boyle, R. J.; Lucci, B. L.

    1996-01-01

    Comparisons are shown between predictions and experimental data for blade and endwall heat transfer. The comparisons of computational domain parisons are given for both vane and rotor geometries over an extensive range of Reynolds and Mach numbers. Comparisons are made with experimental data from a variety of sources. A number of turbulence models are available for predicting blade surface heat transfer, as well as aerodynamic performance. The results of an investigation to determine the turbulence model which gives the best agreement with experimental data over a wide range of test conditions are presented.

  20. Modified Taylor series method for solving nonlinear differential equations with mixed boundary conditions defined on finite intervals.

    PubMed

    Vazquez-Leal, Hector; Benhammouda, Brahim; Filobello-Nino, Uriel Antonio; Sarmiento-Reyes, Arturo; Jimenez-Fernandez, Victor Manuel; Marin-Hernandez, Antonio; Herrera-May, Agustin Leobardo; Diaz-Sanchez, Alejandro; Huerta-Chua, Jesus

    2014-01-01

    In this article, we propose the application of a modified Taylor series method (MTSM) for the approximation of nonlinear problems described on finite intervals. The issue of Taylor series method with mixed boundary conditions is circumvented using shooting constants and extra derivatives of the problem. In order to show the benefits of this proposal, three different kinds of problems are solved: three-point boundary valued problem (BVP) of third-order with a hyperbolic sine nonlinearity, two-point BVP for a second-order nonlinear differential equation with an exponential nonlinearity, and a two-point BVP for a third-order nonlinear differential equation with a radical nonlinearity. The result shows that the MTSM method is capable to generate easily computable and highly accurate approximations for nonlinear equations. 34L30.

  1. Time-interval for integration of stabilizing haptic and visual information in subjects balancing under static and dynamic conditions.

    PubMed

    Honeine, Jean-Louis; Schieppati, Marco

    2014-01-01

    Maintaining equilibrium is basically a sensorimotor integration task. The central nervous system (CNS) continually and selectively weights and rapidly integrates sensory inputs from multiple sources, and coordinates multiple outputs. The weighting process is based on the availability and accuracy of afferent signals at a given instant, on the time-period required to process each input, and possibly on the plasticity of the relevant pathways. The likelihood that sensory inflow changes while balancing under static or dynamic conditions is high, because subjects can pass from a dark to a well-lit environment or from a tactile-guided stabilization to loss of haptic inflow. This review article presents recent data on the temporal events accompanying sensory transition, on which basic information is fragmentary. The processing time from sensory shift to reaching a new steady state includes the time to (a) subtract or integrate sensory inputs; (b) move from allocentric to egocentric reference or vice versa; and (c) adjust the calibration of motor activity in time and amplitude to the new sensory set. We present examples of processes of integration of posture-stabilizing information, and of the respective sensorimotor time-intervals while allowing or occluding vision or adding or subtracting tactile information. These intervals are short, in the order of 1-2 s for different postural conditions, modalities and deliberate or passive shift. They are just longer for haptic than visual shift, just shorter on withdrawal than on addition of stabilizing input, and on deliberate than unexpected mode. The delays are the shortest (for haptic shift) in blind subjects. Since automatic balance stabilization may be vulnerable to sensory-integration delays and to interference from concurrent cognitive tasks in patients with sensorimotor problems, insight into the processing time for balance control represents a critical step in the design of new balance- and locomotion training devices.

  2. Time-interval for integration of stabilizing haptic and visual information in subjects balancing under static and dynamic conditions

    PubMed Central

    Honeine, Jean-Louis; Schieppati, Marco

    2014-01-01

    Maintaining equilibrium is basically a sensorimotor integration task. The central nervous system (CNS) continually and selectively weights and rapidly integrates sensory inputs from multiple sources, and coordinates multiple outputs. The weighting process is based on the availability and accuracy of afferent signals at a given instant, on the time-period required to process each input, and possibly on the plasticity of the relevant pathways. The likelihood that sensory inflow changes while balancing under static or dynamic conditions is high, because subjects can pass from a dark to a well-lit environment or from a tactile-guided stabilization to loss of haptic inflow. This review article presents recent data on the temporal events accompanying sensory transition, on which basic information is fragmentary. The processing time from sensory shift to reaching a new steady state includes the time to (a) subtract or integrate sensory inputs; (b) move from allocentric to egocentric reference or vice versa; and (c) adjust the calibration of motor activity in time and amplitude to the new sensory set. We present examples of processes of integration of posture-stabilizing information, and of the respective sensorimotor time-intervals while allowing or occluding vision or adding or subtracting tactile information. These intervals are short, in the order of 1–2 s for different postural conditions, modalities and deliberate or passive shift. They are just longer for haptic than visual shift, just shorter on withdrawal than on addition of stabilizing input, and on deliberate than unexpected mode. The delays are the shortest (for haptic shift) in blind subjects. Since automatic balance stabilization may be vulnerable to sensory-integration delays and to interference from concurrent cognitive tasks in patients with sensorimotor problems, insight into the processing time for balance control represents a critical step in the design of new balance- and locomotion training devices

  3. Methods to correct and compute confidence and prediction intervals of models neglecting sub-parameterization heterogeneity - From the ideal toward practice

    NASA Astrophysics Data System (ADS)

    Christensen, Steen

    2017-02-01

    This paper derives and tests methods to correct regression-based confidence and prediction intervals for groundwater models that neglect sub-parameterization heterogeneity within the hydraulic property fields of the groundwater system. Several levels of knowledge and uncertainty about the system are considered. It is shown by a two-dimensional groundwater flow example that when reliable probabilistic models are available for the property fields, the corrected confidence and prediction intervals are nearly accurate; when the probabilistic models must be suggested from subjective judgment, the corrected confidence intervals are likely to be much more accurate than their uncorrected counterparts; when no probabilistic information is available then conservative bound values can be used to correct the intervals but they are likely to be very wide. The paper also shows how confidence and prediction intervals can be computed and corrected when the weights applied to the data are estimated as part of the regression. It is demonstrated that in this case it cannot be guaranteed that applying the conservative bound values will lead to conservative confidence and prediction intervals. Finally, it is demonstrated by the two-dimensional flow example that the accuracy of the corrected confidence and prediction intervals deteriorates for very large covariance of the log-transmissivity field, and particularly when the weight matrix differs from the inverse total error covariance matrix. It is argued that such deterioration is less likely to happen for three-dimensional groundwater flow systems.

  4. Fuel Conditioning Facility Electrorefiner Model Predictions versus Measurements

    SciTech Connect

    D Vaden

    2007-10-01

    Electrometallurgical treatment of spent nuclear fuel is performed in the Fuel Conditioning Facility (FCF) at the Idaho National Laboratory (INL) by electrochemically separating uranium from the fission products and structural materials in a vessel called an electrorefiner (ER). To continue processing without waiting for sample analyses to assess process conditions, an ER process model predicts the composition of the ER inventory and effluent streams via multicomponent, multi-phase chemical equilibrium for chemical reactions and a numerical solution to differential equations for electro-chemical transport. The results of the process model were compared to the electrorefiner measured data.

  5. Predicting tree pollen season start dates using thermal conditions.

    PubMed

    Myszkowska, Dorota

    2014-01-01

    Thermal conditions at the beginning of the year determine the timing of pollen seasons of early flowering trees. The aims of this study were to quantify the relationship between the tree pollen season start dates and the thermal conditions just before the beginning of the season and to construct models predicting the start of the pollen season in a given year. The study was performed in Krakow (Southern Poland); the pollen data of Alnus, Corylus and Betula were obtained in 1991-2012 using a volumetric method. The relationship between the tree pollen season start, calculated by the cumulated pollen grain sum method, and a 5-day running means of maximum (for Alnus and Corylus) and mean (for Betula) daily temperature was found and used in the logistic regression models. The estimation of model parameters indicated their statistically significance for all studied taxa; the odds ratio was higher in models for Betula, comparing to Alnus and Corylus. The proposed model makes the accuracy of prediction in 83.58 % of cases for Alnus, in 84.29 % of cases for Corylus and in 90.41 % of cases for Betula. In years of model verification (2011 and 2012), the season start of Alnus and Corylus was predicted more precisely in 2011, while in case of Betula, the model predictions achieved 100 % of accuracy in both years. The correctness of prediction indicated that the data used for the model arrangement fitted the models well and stressed the high efficacy of model prediction estimated using the pollen data in 1991-2010.

  6. Prediction of intake in growing dairy heifers under tropical conditions.

    PubMed

    Oliveira, A S; Ferreira, V B

    2016-02-01

    A meta-analysis was conducted to develop models of the prediction of dry matter intake (DMI) in growing dairy heifers [postweaning to 390 kg of body weight (BW)] under tropical conditions. The adequacy of the models was assessed in a comparison with the 4 US models currently used to predict DMI [Quigley; National Research Council; and 2 Hoffman models]. The data set was created using 95 treatment means from 28 studies published in journals. The data set (studies) was randomly divided into 2 data subsets for the statistical analyses. The first data subset was used to develop the prediction equations for DMI (17 studies; 58 treatment means), and the second data subset was used to assess the adequacy of the predictive models (11 studies; 37 treatment means). The models were developed using nonlinear and linear mixed analyses. Breed (Bos taurus vs. Bos taurus × Bos indicus), BW (240.2±62.2 kg), and average daily gain (ADG, 0.83±0.28 kg/d) were the independent variables. No significant effects of the breed or the interactions between the breed and metabolic BW (BW(0.75)) or breed and ADG were detected. Thus, nonlinear [DMI=0.1175 × BW(0.75) - 3.4984 × e((-2.4690 × ADG))] and linear models [DMI=8.7147 - 0.2402 × BW(0.75) + 0.0027 × (BW(0.75))(2) + 3.6050 × ADG - 1.4168 × ADG(2)] were proposed for both breeds. The nonlinear model explained 81% of the variation in the DMI, over-predicted the DMI by 0.21 kg/d and predicted the DMI with a higher accuracy and precision than the linear model [root mean square error of prediction (RMSEP)=8.82 vs. 10.71% of the observed DMI, respectively]. The Quigley model explained only 54% of the variation in the DMI and was the fourth most accurate and precise model (RMSEP=11.21% of the observed DMI). The National Research Council model explained 69% of the variation in the DMI but under-predicted the DMI by 0.53 kg/d, with an RMSEP of 12.72% of the observed DMI and presence of systematic constant bias. The Hoffman exponential

  7. Expert system for predicting reaction conditions: the Michael reaction case.

    PubMed

    Marcou, G; Aires de Sousa, J; Latino, D A R S; de Luca, A; Horvath, D; Rietsch, V; Varnek, A

    2015-02-23

    A generic chemical transformation may often be achieved under various synthetic conditions. However, for any specific reagents, only one or a few among the reported synthetic protocols may be successful. For example, Michael β-addition reactions may proceed under different choices of solvent (e.g., hydrophobic, aprotic polar, protic) and catalyst (e.g., Brønsted acid, Lewis acid, Lewis base, etc.). Chemoinformatics methods could be efficiently used to establish a relationship between the reagent structures and the required reaction conditions, which would allow synthetic chemists to waste less time and resources in trying out various protocols in search for the appropriate one. In order to address this problem, a number of 2-classes classification models have been built on a set of 198 Michael reactions retrieved from literature. Trained models discriminate between processes that are compatible and respectively processes not feasible under a specific reaction condition option (feasible or not with a Lewis acid catalyst, feasible or not in hydrophobic solvent, etc.). Eight distinct models were built to decide the compatibility of a Michael addition process with each considered reaction condition option, while a ninth model was aimed to predict whether the assumed Michael addition is feasible at all. Different machine-learning methods (Support Vector Machine, Naive Bayes, and Random Forest) in combination with different types of descriptors (ISIDA fragments issued from Condensed Graphs of Reactions, MOLMAP, Electronic Effect Descriptors, and Chemistry Development Kit computed descriptors) have been used. Models have good predictive performance in 3-fold cross-validation done three times: balanced accuracy varies from 0.7 to 1. Developed models are available for the users at http://infochim.u-strasbg.fr/webserv/VSEngine.html . Eventually, these were challenged to predict feasibility conditions for ∼50 novel Michael reactions from the eNovalys database (originally

  8. Effect of suckling restriction with nose plates and premature weaning on postpartum anestrous interval in primiparous cows under range conditions.

    PubMed

    Quintans, G; Vázquez, A I; Weigel, K A

    2009-11-01

    Suckling and nutrition are generally recognized as two major factors controlling the duration of the postpartum anovulatory period. In the present study, the effect of premature weaning and suckling restriction with nose plates (NPs) on cow and calf performance was evaluated. The study was conducted over 2 years; primiparous Hereford cows, weighing (mean+/-S.E.M.) 344+/-3.5kg and with 4.1+/-0.05 units of body condition score (BCS) (scale 1-8 [Vizcarra, J.A., Ibañez, W., Orcasberro, R., 1986. Repetibilidad y reproductibilidad de dos escalas para estimar la condición corporal de vacas Hereford. Investigaciones Agronómicas 7 (1), 45-47]) at calving, remained with their calves until 72.5+/-1.2 days postpartum (day 0). They were then assigned to one of three treatments: (i) calves with free access to their dams and ad libitum suckling (S, n=29); (ii) calves fitted with NPs for 14 days, but remained with their dams (NP, n=29), and (iii) calves that were weaned from their dams (W, n=28). All cows were anestrus at the time treatments commenced (day 0). All cows were blood sampled twice weekly from 1 week before the beginning of the experiment until the end of the mating period (day 74) for progesterone analysis. The mating period began on day 14. Cows in W treatment had ovulations earlier (P<0.05) than those in NP and S groups. Cows in the NP group had longer (P<0.05) intervals between the first progesterone increase and normal luteal phase than cows in the other two treatments groups (23.3+/-3.2 vs. 6.5+/-3.2 and 5.2+/-3.3 days for NP, S and W cows, respectively). Fifty per cent of the cows with NP had a short cycle (7 days) but there was a group of cows that had longer (P<0.05) intervals (66 days) between first progesterone increase and normal estrous activity. In the NP group, 8 of 29 cows had a short luteal phase and then a normal one; for 9 of these 29 cows progesterone concentrations remained low for 6 weeks from the beginning of the treatment; and for 12 of these

  9. Video compression using conditional replenishment and motion prediction

    NASA Technical Reports Server (NTRS)

    Hein, D. N.; Ahmed, N.

    1984-01-01

    A study of a low-rate monochrome video compression system is presentd in this paper. This system is a conditional-replenishment coder that uses two-dimensional Walsh-transform coding within each video frame. The conditional-replenishment algorithm works by transmitting only the portions of an image that are changing in time. This system is augmented with a motn-prediction algorithm that measures spatial dispalcement parameters from frame to frame, and codes the data using these parameters. A comparison is made between the conditional-replenishment system with, and without, the motion-predictinalgorthm. Subsampling in time is ued to maintain the data rate rate at a fixed value. Average bit rates of 1 bit/picture element (pel) to 1/16 bit/pel are considered. The resultant performance of the compression simulations is presented in terms of the average frame rates produced.

  10. USING CONDITION MONITORING TO PREDICT REMAINING LIFE OF ELECTRIC CABLES.

    SciTech Connect

    LOFARO,R.; SOO,P.; VILLARAN,M.; GROVE,E.

    2001-03-29

    Electric cables are passive components used extensively throughout nuclear power stations to perform numerous safety and non-safety functions. It is known that the polymers commonly used to insulate the conductors on these cables can degrade with time; the rate of degradation being dependent on the severity of the conditions in which the cables operate. Cables do not receive routine maintenance and, since it can be very costly, they are not replaced on a regular basis. Therefore, to ensure their continued functional performance, it would be beneficial if condition monitoring techniques could be used to estimate the remaining useful life of these components. A great deal of research has been performed on various condition monitoring techniques for use on electric cables. In a research program sponsored by the U.S. Nuclear Regulatory Commission, several promising techniques were evaluated and found to provide trendable information on the condition of low-voltage electric cables. These techniques may be useful for predicting remaining life if well defined limiting values for the aging properties being measured can be determined. However, each technique has advantages and limitations that must be addressed in order to use it effectively, and the necessary limiting values are not always easy to obtain. This paper discusses how condition monitoring measurements can be used to predict the remaining useful life of electric cables. The attributes of an appropriate condition monitoring technique are presented, and the process to be used in estimating the remaining useful life of a cable is discussed along with the difficulties that must be addressed.

  11. Predictable weathering of puparial hydrocarbons of necrophagous flies for determining the postmortem interval: a field experiment using Chrysomya rufifacies.

    PubMed

    Zhu, Guang-Hui; Jia, Zheng-Jun; Yu, Xiao-Jun; Wu, Ku-Sheng; Chen, Lu-Shi; Lv, Jun-Yao; Eric Benbow, M

    2017-05-01

    Preadult development of necrophagous flies is commonly recognized as an accurate method for estimating the minimum postmortem interval (PMImin). However, once the PMImin exceeds the duration of preadult development, the method is less accurate. Recently, fly puparial hydrocarbons were found to significantly change with weathering time in the field, indicating their potential use for PMImin estimates. However, additional studies are required to demonstrate how the weathering varies among species. In this study, the puparia of Chrysomya rufifacies were placed in the field to experience natural weathering to characterize hydrocarbon composition change over time. We found that weathering of the puparial hydrocarbons was regular and highly predictable in the field. For most of the hydrocarbons, the abundance decreased significantly and could be modeled using a modified exponent function. In addition, the weathering rate was significantly correlated with the hydrocarbon classes. The weathering rate of 2-methyl alkanes was significantly lower than that of alkenes and internal methyl alkanes, and alkenes were higher than the other two classes. For mono-methyl alkanes, the rate was significantly and positively associated with carbon chain length and branch position. These results indicate that puparial hydrocarbon weathering is highly predictable and can be used for estimating long-term PMImin.

  12. Time series and recurrence interval models to predict the vulnerability of streams to episodic acidification in Shenandoah National Park, Virginia

    USGS Publications Warehouse

    Deviney, F.A.; Rice, Karen C.; Hornberger, G.M.

    2006-01-01

    Acid rain affects headwater streams by temporarily reducing the acid-neutralizing capacity (ANC) of the water, a process termed episodic acidification. The increase in acidic components in stream water can have deleterious effects on the aquatic biota. Although acidic deposition is uniform across Shenandoah National Park (SNP) in north central Virginia, the stream water quality response during rain events varies substantially. This response is a function of the catchment's underlying geology and topography. Geologic and topographic data for SNP's 231 catchments are readily available; however, long-term measurements (tens of years) of ANC and accompanying discharge are not and would be prohibitively expensive to collect. Transfer function time series models were developed to predict hourly ANC from discharge for five SNP catchments with long-term water-quality and discharge records. Hourly ANC predictions over short time periods (≤1 week) were averaged, and distributions of the recurrence intervals of annual water-year minimum ANC values were model-simulated for periods of 6, 24, 72, and 168 hours. The distributions were extrapolated to the rest of the SNP catchments on the basis of catchment geology and topography. On the basis of the models, large numbers of SNP streams have 6- to 168-hour periods of low-ANC values, which may stress resident fish populations. Smaller catchments are more vulnerable to episodic acidification than larger catchments underlain by the same bedrock. Catchments with similar topography and size are more vulnerable if underlain by less basaltic/carbonate bedrock. Many catchments are predicted to have successive years of low-ANC values potentially sufficient to extirpate some species.

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

    PubMed

    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.

  14. [Primary Study on Predicting the Termination of Paroxysmal Atrial Fibrillation Based on a Novel RdR RR Intervals Scatter Plot].

    PubMed

    Lu, Hongwei; Zhang, Chenxi; Sun, Ying; Hao, Zhidong; Wang, Chunfang; Tian, Jiajia

    2015-08-01

    Predicting the termination of paroxysmal atrial fibrillation (AF) may provide a signal to decide whether there is a need to intervene the AF timely. We proposed a novel RdR RR intervals scatter plot in our study. The abscissa of the RdR scatter plot was set to RR intervals and the ordinate was set as the difference between successive RR intervals. The RdR scatter plot includes information of RR intervals and difference between successive RR intervals, which captures more heart rate variability (HRV) information. By RdR scatter plot analysis of one minute RR intervals for 50 segments with non-terminating AF and immediately terminating AF, it was found that the points in RdR scatter plot of non-terminating AF were more decentralized than the ones of immediately terminating AF. By dividing the RdR scatter plot into uniform grids and counting the number of non-empty grids, non-terminating AF and immediately terminating AF segments were differentiated. By utilizing 49 RR intervals, for 20 segments of learning set, 17 segments were correctly detected, and for 30 segments of test set, 20 segments were detected. While utilizing 66 RR intervals, for 18 segments of learning set, 16 segments were correctly detected, and for 28 segments of test set, 20 segments were detected. The results demonstrated that during the last one minute before the termination of paroxysmal AF, the variance of the RR intervals and the difference of the neighboring two RR intervals became smaller. The termination of paroxysmal AF could be successfully predicted by utilizing the RdR scatter plot, while the predicting accuracy should be further improved.

  15. Usefulness of time interval between end of diastolic mitral annular velocity pattern and onset of QRS for predicting left ventricular end-diastolic pressure.

    PubMed

    Su, Ho-Ming; Lin, Tsung-Hsien; Voon, Wen-Chol; Lee, Kun-Tai; Chu, Chih-Sheng; Cheng, Kai-Hung; Yen, Hsueh-Wei; Lai, Wen-Ter; Sheu, Sheng-Hsiung

    2007-01-01

    Diastolic mitral annular motion may terminate earlier in patients with higher left ventricular end-diastolic pressure (LVEDP). It was therefore hypothesized that the time interval measured from the end of the diastolic mitral annular velocity pattern to the onset of QRS (the AQ interval) would be a useful parameter in predicting LVEDP. The aim of this study was to evaluate the relation between the AQ interval and LVEDP. Forty-six patients with suspected coronary artery disease who underwent Doppler echocardiographic studies and cardiac catheterization were included. LVEDP was determined using a micromanometer-tipped catheter. On univariate analysis, the AQ interval had positive correlations with the PR interval (r = 0.405, p = 0.005), transmitral E-wave velocity (r = 0.502, p <0.001), isovolumic contraction time (r = 0.635, p <0.001), and LVEDP (r = 0.514, p <0.001) and a negative correlation with E-wave deceleration time (r = -0.430, p = 0.003). After stepwise multiple linear regression analysis, the PR interval, transmitral E-wave velocity, and LVEDP were the independent predictors of the AQ interval (beta = 0.234, p = 0.033; beta = 0.331, p = 0.004; and beta = 0.350, p = 0.003, respectively). In conclusion, the AQ interval is a novel, simple, and easily obtained index in the prediction of LVEDP.

  16. Power-Law Inter-Spike Interval Distributions Infer a Conditional Maximization of Entropy in Cortical Neurons

    PubMed Central

    Tsubo, Yasuhiro; Isomura, Yoshikazu; Fukai, Tomoki

    2012-01-01

    The brain is considered to use a relatively small amount of energy for its efficient information processing. Under a severe restriction on the energy consumption, the maximization of mutual information (MMI), which is adequate for designing artificial processing machines, may not suit for the brain. The MMI attempts to send information as accurate as possible and this usually requires a sufficient energy supply for establishing clearly discretized communication bands. Here, we derive an alternative hypothesis for neural code from the neuronal activities recorded juxtacellularly in the sensorimotor cortex of behaving rats. Our hypothesis states that in vivo cortical neurons maximize the entropy of neuronal firing under two constraints, one limiting the energy consumption (as assumed previously) and one restricting the uncertainty in output spike sequences at given firing rate. Thus, the conditional maximization of firing-rate entropy (CMFE) solves a tradeoff between the energy cost and noise in neuronal response. In short, the CMFE sends a rich variety of information through broader communication bands (i.e., widely distributed firing rates) at the cost of accuracy. We demonstrate that the CMFE is reflected in the long-tailed, typically power law, distributions of inter-spike intervals obtained for the majority of recorded neurons. In other words, the power-law tails are more consistent with the CMFE rather than the MMI. Thus, we propose the mathematical principle by which cortical neurons may represent information about synaptic input into their output spike trains. PMID:22511856

  17. Visual Bias Predicts Gait Adaptability in Novel Sensory Discordant Conditions

    NASA Technical Reports Server (NTRS)

    Brady, Rachel A.; Batson, Crystal D.; Peters, Brian T.; Mulavara, Ajitkumar P.; Bloomberg, Jacob J.

    2010-01-01

    We designed a gait training study that presented combinations of visual flow and support-surface manipulations to investigate the response of healthy adults to novel discordant sensorimotor conditions. We aimed to determine whether a relationship existed between subjects visual dependence and their postural stability and cognitive performance in a new discordant environment presented at the conclusion of training (Transfer Test). Our training system comprised a treadmill placed on a motion base facing a virtual visual scene that provided a variety of sensory challenges. Ten healthy adults completed 3 training sessions during which they walked on a treadmill at 1.1 m/s while receiving discordant support-surface and visual manipulations. At the first visit, in an analysis of normalized torso translation measured in a scene-movement-only condition, 3 of 10 subjects were classified as visually dependent. During the Transfer Test, all participants received a 2-minute novel exposure. In a combined measure of stride frequency and reaction time, the non-visually dependent subjects showed improved adaptation on the Transfer Test compared to their visually dependent counterparts. This finding suggests that individual differences in the ability to adapt to new sensorimotor conditions may be explained by individuals innate sensory biases. An accurate preflight assessment of crewmembers biases for visual dependence could be used to predict their propensities to adapt to novel sensory conditions. It may also facilitate the development of customized training regimens that could expedite adaptation to alternate gravitational environments.

  18. Economic Conditions Predict Prevalence of West Nile Virus

    PubMed Central

    Buermann, Wolfgang; Cummings, Robert F.; Kahn, Matthew E.; Smith, Thomas B.

    2010-01-01

    Understanding the conditions underlying the proliferation of infectious diseases is crucial for mitigating future outbreaks. Since its arrival in North America in 1999, West Nile virus (WNV) has led to population-wide declines of bird species, morbidity and mortality of humans, and expenditures of millions of dollars on treatment and control. To understand the environmental conditions that best explain and predict WNV prevalence, we employed recently developed spatial modeling techniques in a recognized WNV hotspot, Orange County, California. Our models explained 85–95% of the variation of WNV prevalence in mosquito vectors, and WNV presence in secondary human hosts. Prevalence in both vectors and humans was best explained by economic variables, specifically per capita income, and by anthropogenic characteristics of the environment, particularly human population and neglected swimming pool density. While previous studies have shown associations between anthropogenic change and pathogen presence, results show that poorer economic conditions may act as a direct surrogate for environmental characteristics related to WNV prevalence. Low-income areas may be associated with higher prevalence for a number of reasons, including variations in property upkeep, microhabitat conditions conducive to viral amplification in both vectors and hosts, host community composition, and human behavioral responses related to differences in education or political participation. Results emphasize the importance and utility of including economic variables in mapping spatial risk assessments of disease. PMID:21103053

  19. Economic conditions predict prevalence of West Nile virus.

    PubMed

    Harrigan, Ryan J; Thomassen, Henri A; Buermann, Wolfgang; Cummings, Robert F; Kahn, Matthew E; Smith, Thomas B

    2010-11-12

    Understanding the conditions underlying the proliferation of infectious diseases is crucial for mitigating future outbreaks. Since its arrival in North America in 1999, West Nile virus (WNV) has led to population-wide declines of bird species, morbidity and mortality of humans, and expenditures of millions of dollars on treatment and control. To understand the environmental conditions that best explain and predict WNV prevalence, we employed recently developed spatial modeling techniques in a recognized WNV hotspot, Orange County, California. Our models explained 85-95% of the variation of WNV prevalence in mosquito vectors, and WNV presence in secondary human hosts. Prevalence in both vectors and humans was best explained by economic variables, specifically per capita income, and by anthropogenic characteristics of the environment, particularly human population and neglected swimming pool density. While previous studies have shown associations between anthropogenic change and pathogen presence, results show that poorer economic conditions may act as a direct surrogate for environmental characteristics related to WNV prevalence. Low-income areas may be associated with higher prevalence for a number of reasons, including variations in property upkeep, microhabitat conditions conducive to viral amplification in both vectors and hosts, host community composition, and human behavioral responses related to differences in education or political participation. Results emphasize the importance and utility of including economic variables in mapping spatial risk assessments of disease.

  20. Arc Jet Facility Test Condition Predictions Using the ADSI Code

    NASA Technical Reports Server (NTRS)

    Palmer, Grant; Prabhu, Dinesh; Terrazas-Salinas, Imelda

    2015-01-01

    The Aerothermal Design Space Interpolation (ADSI) tool is used to interpolate databases of previously computed computational fluid dynamic solutions for test articles in a NASA Ames arc jet facility. The arc jet databases are generated using an Navier-Stokes flow solver using previously determined best practices. The arc jet mass flow rates and arc currents used to discretize the database are chosen to span the operating conditions possible in the arc jet, and are based on previous arc jet experimental conditions where possible. The ADSI code is a database interpolation, manipulation, and examination tool that can be used to estimate the stagnation point pressure and heating rate for user-specified values of arc jet mass flow rate and arc current. The interpolation is performed in the other direction (predicting mass flow and current to achieve a desired stagnation point pressure and heating rate). ADSI is also used to generate 2-D response surfaces of stagnation point pressure and heating rate as a function of mass flow rate and arc current (or vice versa). Arc jet test data is used to assess the predictive capability of the ADSI code.

  1. Autoregressive conditional duration as a model for financial market crashes prediction

    NASA Astrophysics Data System (ADS)

    Pyrlik, Vladimir

    2013-12-01

    There is an increasing number of studies showing that financial market crashes can be detected and predicted. The main aim of the research was to develop a technique for crashes prediction based on the analysis of durations between sequent crashes of a certain magnitude of Dow Jones Industrial Average. We have found significant autocorrelation in the series of durations between sequent crashes and suggest autoregressive conditional duration models (ACD) to forecast the crashes. We apply the rolling intervals technique in the sample of more than 400 DJIA crashes in 1896-2011 and repeatedly use the data on 100 sequent crashes to estimate a family of ACD models and calculate forecasts of the one following crash. It appears that the ACD models provide significant predictive power when combined with the inter-event waiting time technique. This suggests that despite the high quality of retrospective predictions, using the technique for real-time forecasting seems rather ineffective, as in the case of every particular crash the specification of the ACD model, which would provide the best quality prediction, is rather hard to identify.

  2. Use of early lactation milk recording data to predict the calving to conception interval in dairy herds.

    PubMed

    Cook, J G; Green, M J

    2016-06-01

    Economic success in dairy herds is heavily reliant on obtaining pregnancies at an early stage of lactation. Our objective in this study was to attempt to predict the likelihood of conception occurring by d 100 and 150 of lactation (days in milk, DIM) by Markov chain Monte Carlo analysis using test day milk recording data and reproductive records gathered retrospectively from 8,750 cows from 33 dairy herds located in the United Kingdom. Overall, 65% of cows recalved with 30, 46, and 65% of cows conceiving by 100 DIM, 150 DIM, and beyond 150 DIM, respectively. Overall conception rate (total cows pregnant/total number of inseminations) was 27.47%. Median and mean calving to conception intervals were 123 and 105 d, respectively. The probability of conception by both 100 DIM and 150 DIM was positively associated with the average daily milk weight produced during the fourth week of lactation (W4MK) and protein percentage for test day samples collected between 0 to 30 and 31 to 60 DIM. Butterfat percentage at 0 to 30 DIM was negatively associated with the probability of conception by 100 DIM but not at 150 DIM. High somatic cell count (SCC) at both 0 to 30 and 31 to 60 DIM was negatively associated with the probability of conception by 100 DIM, whereas high SCC at 31 to 60 DIM was associated with a reduced probability of conception by 150 DIM. Increasing parity was associated with a reduced odds of pregnancy. Posterior predictions of the likelihood of conception for cows categorized as having "good" (W4MK >30kg and protein percentage at 0 to 30 and 31 to 60 DIM >3.2%) or "poor" (W4MK <25kg and protein percentage at 0 to 30 and 31 to 60 DIM <3.0%) early lactation attributes with actual observed values indicated model fit was good. The predicted likelihood of a "good" cow conceiving by 100 and 150 DIM was 0.39 and 0.57, respectively (actual observed values 0.40 and 0.59). The corresponding values for a "poor" cow were 0.28 and 0.42 (actual observed values 0.26 and 0

  3. Predicting Redox Conditions in Groundwater Using Statistical Techniques: Implications for Nitrate Transport in Groundwater and Streams

    NASA Astrophysics Data System (ADS)

    Tesoriero, A. J.; Terziotti, S.

    2014-12-01

    Nitrate trends in streams often do not match expectations based on recent nitrogen source loadings to the land surface. Groundwater discharge with long travel times has been suggested as the likely cause for these observations. The fate of nitrate in groundwater depends to a large extent on the occurrence of denitrification along flow paths. Because denitrification in groundwater is inhibited when dissolved oxygen (DO) concentrations are high, defining the oxic-suboxic interface has been critical in determining pathways for nitrate transport in groundwater and to streams at the local scale. Predicting redox conditions on a regional scale is complicated by the spatial variability of reaction rates. In this study, logistic regression and boosted classification tree analysis were used to predict the probability of oxic water in groundwater in the Chesapeake Bay watershed. The probability of oxic water (DO > 2 mg/L) was predicted by relating DO concentrations in over 3,000 groundwater samples to indicators of residence time and/or electron donor availability. Variables that describe position in the flow system (e.g., depth to top of the open interval), soil drainage and surficial geology were the most important predictors of oxic water. Logistic regression and boosted classification tree analysis correctly predicted the presence or absence of oxic conditions in over 75 % of the samples in both training and validation data sets. Predictions of the percentages of oxic wells in deciles of risk were very accurate (r2>0.9) in both the training and validation data sets. Depth to the bottom of the oxic layer was predicted and is being used to estimate the effect that groundwater denitrification has on stream nitrate concentrations and the time lag between the application of nitrogen at the land surface and its effect on streams.

  4. Operational seasonal and interannual predictions of ocean conditions

    NASA Technical Reports Server (NTRS)

    Leetmaa, Ants

    1992-01-01

    Dr. Leetmaa described current work at the U.S. National Meteorological Center (NMC) on coupled systems leading to a seasonal prediction system. He described the way in which ocean thermal data is quality controlled and used in a four dimensional data assimilation system. This consists of a statistical interpolation scheme, a primitive equation ocean general circulation model, and the atmospheric fluxes that are required to force this. This whole process generated dynamically consist thermohaline and velocity fields for the ocean. Currently routine weekly analyses are performed for the Atlantic and Pacific oceans. These analyses are used for ocean climate diagnostics and as initial conditions for coupled forecast models. Specific examples of output products were shown both in the Pacific and the Atlantic Ocean.

  5. The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results.

    PubMed

    Goodman, S N; Berlin, J A

    1994-08-01

    Although there is a growing understanding of the importance of statistical power considerations when designing studies and of the value of confidence intervals when interpreting data, confusion exists about the reverse arrangement: the role of confidence intervals in study design and of power in interpretation. Confidence intervals should play an important role when setting sample size, and power should play no role once the data have been collected, but exactly the opposite procedure is widely practiced. In this commentary, we present the reasons why the calculation of power after a study is over is inappropriate and how confidence intervals can be used during both study design and study interpretation.

  6. A Variable Oscillator Underlies the Measurement of Time Intervals in the Rostral Medial Prefrontal Cortex during Classical Eyeblink Conditioning in Rabbits.

    PubMed

    Caro-Martín, C Rocío; Leal-Campanario, Rocío; Sánchez-Campusano, Raudel; Delgado-García, José M; Gruart, Agnès

    2015-11-04

    We were interested in determining whether rostral medial prefrontal cortex (rmPFC) neurons participate in the measurement of conditioned stimulus-unconditioned stimulus (CS-US) time intervals during classical eyeblink conditioning. Rabbits were conditioned with a delay paradigm consisting of a tone as CS. The CS started 50, 250, 500, 1000, or 2000 ms before and coterminated with an air puff (100 ms) directed at the cornea as the US. Eyelid movements were recorded with the magnetic search coil technique and the EMG activity of the orbicularis oculi muscle. Firing activities of rmPFC neurons were recorded across conditioning sessions. Reflex and conditioned eyelid responses presented a dominant oscillatory frequency of ≈12 Hz. The firing rate of each recorded neuron presented a single peak of activity with a frequency dependent on the CS-US interval (i.e., ≈12 Hz for 250 ms, ≈6 Hz for 500 ms, and≈3 Hz for 1000 ms). Interestingly, rmPFC neurons presented their dominant firing peaks at three precise times evenly distributed with respect to CS start and also depending on the duration of the CS-US interval (only for intervals of 250, 500, and 1000 ms). No significant neural responses were recorded at very short (50 ms) or long (2000 ms) CS-US intervals. rmPFC neurons seem not to encode the oscillatory properties characterizing conditioned eyelid responses in rabbits, but are probably involved in the determination of CS-US intervals of an intermediate range (250-1000 ms). We propose that a variable oscillator underlies the generation of working memories in rabbits. The way in which brains generate working memories (those used for the transient processing and storage of newly acquired information) is still an intriguing question. Here, we report that the firing activities of neurons located in the rostromedial prefrontal cortex recorded in alert behaving rabbits are controlled by a dynamic oscillator. This oscillator generated firing frequencies in a variable band

  7. Rubber yield prediction by meteorological conditions using mixed models and multi-model inference techniques

    NASA Astrophysics Data System (ADS)

    Golbon, Reza; Ogutu, Joseph Ochieng; Cotter, Marc; Sauerborn, Joachim

    2015-12-01

    Linear mixed models were developed and used to predict rubber ( Hevea brasiliensis) yield based on meteorological conditions to which rubber trees had been exposed for periods ranging from 1 day to 2 months prior to tapping events. Predictors included a range of moving averages of meteorological covariates spanning different windows of time before the date of the tapping events. Serial autocorrelation in the latex yield measurements was accounted for using random effects and a spatial generalization of the autoregressive error covariance structure suited to data sampled at irregular time intervals. Information theoretics, specifically the Akaike information criterion (AIC), AIC corrected for small sample size (AICc), and Akaike weights, was used to select models with the greatest strength of support in the data from a set of competing candidate models. The predictive performance of the selected best model was evaluated using both leave-one-out cross-validation (LOOCV) and an independent test set. Moving averages of precipitation, minimum and maximum temperature, and maximum relative humidity with a 30-day lead period were identified as the best yield predictors. Prediction accuracy expressed in terms of the percentage of predictions within a measurement error of 5 g for cross-validation and also for the test dataset was above 99 %.

  8. Rubber yield prediction by meteorological conditions using mixed models and multi-model inference techniques.

    PubMed

    Golbon, Reza; Ogutu, Joseph Ochieng; Cotter, Marc; Sauerborn, Joachim

    2015-12-01

    Linear mixed models were developed and used to predict rubber (Hevea brasiliensis) yield based on meteorological conditions to which rubber trees had been exposed for periods ranging from 1 day to 2 months prior to tapping events. Predictors included a range of moving averages of meteorological covariates spanning different windows of time before the date of the tapping events. Serial autocorrelation in the latex yield measurements was accounted for using random effects and a spatial generalization of the autoregressive error covariance structure suited to data sampled at irregular time intervals. Information theoretics, specifically the Akaike information criterion (AIC), AIC corrected for small sample size (AICc), and Akaike weights, was used to select models with the greatest strength of support in the data from a set of competing candidate models. The predictive performance of the selected best model was evaluated using both leave-one-out cross-validation (LOOCV) and an independent test set. Moving averages of precipitation, minimum and maximum temperature, and maximum relative humidity with a 30-day lead period were identified as the best yield predictors. Prediction accuracy expressed in terms of the percentage of predictions within a measurement error of 5 g for cross-validation and also for the test dataset was above 99 %.

  9. Prolonged corrected QT interval is predictive of future stroke events even in subjects without ECG-diagnosed left ventricular hypertrophy.

    PubMed

    Ishikawa, Joji; Ishikawa, Shizukiyo; Kario, Kazuomi

    2015-03-01

    We attempted to evaluate whether subjects who exhibit prolonged corrected QT (QTc) interval (≥440 ms in men and ≥460 ms in women) on ECG, with and without ECG-diagnosed left ventricular hypertrophy (ECG-LVH; Cornell product, ≥244 mV×ms), are at increased risk of stroke. Among the 10 643 subjects, there were a total of 375 stroke events during the follow-up period (128.7±28.1 months; 114 142 person-years). The subjects with prolonged QTc interval (hazard ratio, 2.13; 95% confidence interval, 1.22-3.73) had an increased risk of stroke even after adjustment for ECG-LVH (hazard ratio, 1.71; 95% confidence interval, 1.22-2.40). When we stratified the subjects into those with neither a prolonged QTc interval nor ECG-LVH, those with a prolonged QTc interval but without ECG-LVH, and those with ECG-LVH, multivariate-adjusted Cox proportional hazards analysis demonstrated that the subjects with prolonged QTc intervals but not ECG-LVH (1.2% of all subjects; incidence, 10.7%; hazard ratio, 2.70, 95% confidence interval, 1.48-4.94) and those with ECG-LVH (incidence, 7.9%; hazard ratio, 1.83; 95% confidence interval, 1.31-2.57) had an increased risk of stroke events, compared with those with neither a prolonged QTc interval nor ECG-LVH. In conclusion, prolonged QTc interval was associated with stroke risk even among patients without ECG-LVH in the general population. © 2014 American Heart Association, Inc.

  10. Short-Term Prediction of Traffic Rate Interval Router Using Hybrid Training of Dynamic Synapse Neural Network Structure

    NASA Astrophysics Data System (ADS)

    Shakiba, M.; Teshnehlab, M.; Zokaie, S.; Zakermoshfegh, M.

    In this study, a hybrid learning algorithm for training the Dynamic Synapse Neural Network (DSNN) to high accurate prediction of congestion in TCP computer networks is introduced. The idea behind this technique is to inform the TCP transmitters of congestion before it happens and to make transmitters decrease their data sending rate to a level which does not overflow the routers buffer. Traffic rate data are available in the format of time series and these real data are used to train and predict the future traffic rate condition. Hybrid learning algorithm aims to solve main problems of the Gradient Descent (GD) based method for the optimization of the DSNN, which are instability, local minima and the problem of generalization of trained network to the test data. In this method, Adaptable Weighted Particle Swarm Optimization (AWPSO) as a global optimizer is used to optimize the parameters of synaptic plasticity and the GD algorithm is used to optimize the weighted parameters of DSNN. As AWPSO is a derivative free optimization technique, a simpler method for the train of DSNN is achieved. Also the results are compared to GD algorithm.

  11. Prediction of glass durability as a function of environmental conditions

    SciTech Connect

    Jantzen, C M

    1988-01-01

    A thermodynamic model of glass durability is applied to natural, ancient, and nuclear waste glasses. The durabilities of over 150 different natural and man-made glasses, including actual ancient Roman and Islamic glasses (Jalame ca. 350 AD, Nishapur 10-11th century AD and Gorgon 9-11th century AD), are compared. Glass durability is a function of the thermodynamic hydration free energy, ..delta..G/sub hyd/, which can be calculated from glass composition and solution pH. The durability of the most durable nuclear waste glasses examined was /approximately/10/sup 6/ years. The least durable waste glass formulations were comparable in durability to the most durable simulated medieval window glasses of /approximately/10/sup 3/ years. In this manner, the durability of nuclear waste glasses has been interpolated to be /approximately/10/sup 6/ years and no less than 10/sup 3/ years. Hydration thermodynamics have been shown to be applicable to the dissolution of glass in various natural environments. Groundwater-glass interactions relative to geologic disposal of nuclear waste, hydration rind dating of obsidians, andor other archeological studies can be modeled, e.g., the relative durabilities of six simulated medieval window glasses have been correctly predicted for both laboratory (one month) and burial (5 years) experiments. Effects of solution pH on glass dissolution has been determined experimentally for the 150 different glasses and can be predicted theoretically by hydration thermodynamics. The effects of solution redox on dissolution of glass matrix elements such as SI and B have shown to be minimal. The combined effects of solution pH and Eh have been described and unified by construction of thermodynamically calculated Pourbaix (pH-Eh) diagrams for glass dissolution. The Pourbaix diagrams have been quantified to describe glass dissolution as a function of environmental conditions by use of the data derived from hydration thermodynamics. 56 refs., 7 figs.

  12. Conditional Weather Resampling Method for Seasonal Ensemble Streamflow Prediction

    NASA Astrophysics Data System (ADS)

    Beckers, Joost; Weerts, Albrecht; Welles, Edwin

    2014-05-01

    Ensemble Streamflow Prediction (ESP) is a commonly used method for water resources planning on the seasonal time scale. The starting point for the ESP is the current state of the hydrological system, which is generated form a short historical simulation up to the time of forecast. Starting from this initial state, a hydrologic model is run to produce an ensemble of possible realizations of future streamflows, taking meteorological time series from historical years as input. It is assumed that these historical weather time series represent climatology. One disadvantage of the original ESP method is that an expected deviation from average climatology is not accounted for. Here, we propose a variation to the ESP, in which shorter periods from historical time years are resampled and assembled to generate additional possible realizations of future weather. The resampling is done in such a way as to incorporate statistical deviations from the average climate that are linked to climate modes, such as El Niño Southern Oscillation (ENSO) or Pacific Decadal Oscillation (PDO). These climate modes are known to affect the local weather in many regions around the world. The resampling of historical weather periods is conditioned on the climate mode indices, starting with the current climate index value and searching for historical years with similar climate indices. The resampled weather time series are used as input for the hydrological model, similar to the original ESP procedure. The method was implemented in the operational forecasting environment of Bonneville Power Administration (BPA), which based on Delft-FEWS. The method was run for 55 non-operational years of hindcasts (forecasts in retrospect) for the Columbia River in the North-West of the U.S. An increase in forecast skill up to 5% was found relative to the standard ESP for streamflow predictions at three test-locations.

  13. Electrical PR Interval Variation Predicts New Occurrence of Atrial Fibrillation in Patients With Frequent Premature Atrial Contractions

    PubMed Central

    Chun, Kwang Jin; Hwang, Jin Kyung; Park, Seung-Jung; On, Young Keun; Kim, June Soo; Park, Kyoung-Min

    2016-01-01

    Abstract Atrial fibrillation (AF) is associated with the autonomic nervous system (ANS), and fluctuation of autonomic tone is more prominent in patients with AF. As autonomic tone affects the heart rate (HR), and there is an inverse relationship between HR and PR interval, PR interval variation could be greater in patients with AF than in those without AF. The purpose of this study was to investigate the correlation between PR interval variation and new-onset AF in patients with frequent PACs. We retrospectively enrolled 207 patients with frequent PACs who underwent electrocardiographs at least 4 times during the follow-up period. The PR variation was calculated by subtracting the minimum PR interval from the maximum PR interval. The outcomes were new occurrence of AF and all-cause mortality during the follow-up period. During a median follow-up of 8.3 years, 24 patients (11.6%) developed new-onset AF. Univariate analysis showed that prolonged PR interval (PR interval > 200 ms, P = 0.021), long PR variation (PR variation > 36.5 ms, P = 0.018), and PR variation (P = 0.004) as a continuous variable were associated with an increased risk of AF. Cox regression analysis showed that prolonged PR interval (hazard ratio = 3.321, 95% CI 1.064–10.362, P = 0.039) and PR variation (hazard ratio = 1.013, 95% CI 1.002–1.024, P = 0.022) were independent predictors for new-onset AF. However, PR variation and prolonged PR interval were not associated with all-cause mortality (P = 0.465 and 0.774, respectively). PR interval variation and prolonged PR interval are independent risk factors for new-onset AF in patients with frequent PACs. However we were unable to determine a cut-off value of PR interval variation for new-onset AF. PMID:27057868

  14. Predictive hydrogeochemical modelling of bauxite residue sand in field conditions.

    PubMed

    Wissmeier, Laurin; Barry, David A; Phillips, Ian R

    2011-07-15

    The suitability of residue sand (the coarse fraction remaining from Bayer's process of bauxite refining) for constructing the surface cover of closed bauxite residue storage areas was investigated. Specifically, its properties as a medium for plant growth are of interest to ensure residue sand can support a sustainable ecosystem following site closure. The geochemical evolution of the residue sand under field conditions, its plant nutrient status and soil moisture retention were studied by integrated modelling of geochemical and hydrological processes. For the parameterization of mineral reactions, amounts and reaction kinetics of the mineral phases natron, calcite, tricalcium aluminate, sodalite, muscovite and analcime were derived from measured acid neutralization curves. The effective exchange capacity for ion adsorption was measured using three independent exchange methods. The geochemical model, which accounts for mineral reactions, cation exchange and activity corrected solution speciation, was formulated in the geochemical modelling framework PHREEQC, and partially validated in a saturated-flow column experiment. For the integration of variably saturated flow with multi-component solute transport in heterogeneous 2D domains, a coupling of PHREEQC with the multi-purpose finite-element solver COMSOL was established. The integrated hydrogeochemical model was applied to predict water availability and quality in a vertical flow lysimeter and a cover design for a storage facility using measured time series of rainfall and evaporation from southwest Western Australia. In both scenarios the sand was fertigated and gypsum-amended. Results show poor long-term retention of fertilizer ions and buffering of the pH around 10 for more than 5 y of leaching. It was concluded that fertigation, gypsum amendment and rainfall leaching alone were insufficient to render the geochemical conditions of residue sand suitable for optimal plant growth within the given timeframe. The

  15. Predicting EMIC wave properties from ring current plasma conditions

    NASA Astrophysics Data System (ADS)

    Cowee, M.; Fu, X.; Jordanova, V.

    2015-12-01

    Recently, sophisticated computer models have shown that accurate, dynamic modelling of the energetic electrons in the radiation belt requires global and real-time plasma and wave conditions. Data provided by in-situ spacecraft measurement are too sparse to supply enough inputs for continuous global modeling of the radiation belt. Here we present a model to predict amplitude, peak frequency and spectral width of the electromagnetic ion cyclotron (EMIC) wave from the anisotropic ring current ion distributions, which are the source of the wave. The model is derived from hybrid simulations in a large initial parameter space for plasmas consisting of electrons, protons, and helium ions. Key parameters include the ratio of plasma frequency to ion gyrofrequency, the density, temperature and anisotropy of hot ions, and the cold-ion composition. The results show that amplitude, peak frequency and spectral width of EMIC waves can be related to linear properties of the anisotropy-driven instability, e.g. growth rate and plasma beta, through simple analytic formulas. Combined with a dynamic ring current model, this model can provide global EMIC wave information needed for radiation-belt modeling.

  16. Error estimation for CFD aeroheating prediction under rarefied flow condition

    NASA Astrophysics Data System (ADS)

    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.

  17. Total atrial conduction time assessed by tissue doppler imaging (PA-TDI Interval) to predict early recurrence of persistent atrial fibrillation after successful electrical cardioversion.

    PubMed

    Müller, Patrick; Schiedat, Fabian; Bialek, Anna; Bösche, Leif; Ewers, Aydan; Kara, Kaffer; Dietrich, Johannes W; Mügge, Andreas; Deneke, Thomas

    2014-02-01

    The aim of this study was to investigate whether total atrial conduction time (TACT) assessed via tissue Doppler imaging (PA-TDI interval) can identify patients with early recurrent atrial fibrillation (ERAF) after successful direct-current electrical cardioversion (CV) of persistent atrial fibrillation (persPAF). A total of 54 patients without antiarrhythmic drug medication (mean ± SD: 66 ± 10.4 years; 33% women) with persPAF and successful CV were enrolled between May 2012 and May 2013. TACT was measured 6 hours after successful CV in the left atrium by tissue Doppler imaging (PA-TDI interval). ERAF was determined via Holter-electrocardiogram over a period of 7 days after CV. Receiver opearting characteristic analysis was used to determine an optimal cutoff value of PA-TDI interval for prognosis of ERAF. Based on this result, recurrence-free survival was assessed with Mantel-Haenszel's log-rank test. ERAF occurred in 23 patients (43%). PA-TDI interval was longer in patients with ERAF compared to those who maintained sinus rhythm (mean ± SD: 163.5 ± 11.1 vs 132.3 ± 11.2 milliseconds; P < 0.00001). At the cutoff value of 152 milliseconds, PA-TDI interval sensitivity and specificity related to ERAF were 87% and 100%, respectively. Measuring PA-TDI interval may help to predict ERAF after successful CV in patients with persAF. © 2013 Wiley Periodicals, Inc.

  18. Maternal plasma procalcitonin concentrations in patients with preterm labor and intact membranes--prediction of preterm delivery and admission-to-delivery interval.

    PubMed

    Torbé, Andrzej; Czajka, Ryszard

    2004-01-01

    The purpose of this study was to evaluate procalcitonin (PCT) plasma levels in pregnancy complicated by preterm labor and to determine their value in the prediction of preterm delivery and the length of the admission-to-delivery interval. The study population consisted of 53 patients with preterm labor and 31 healthy pregnants. The study patients were divided according to the delivery time and to the admission-to-delivery interval. Plasma PCT concentrations were higher in preterm labor than in healthy pregnants. Although at the onset of preterm labor plasma PCT concentrations in patients who delivered prematurely were higher than in patients who, after tocolytic treatment, delivered at term, the difference was not significant. Also in cases of preterm labor delivered within and after three and seven days of admission no differences were observed. The highest values in the prediction of preterm delivery and the length of admission-to-delivery interval corresponded to a PCT concentration of 1.7 ng/ml. These findings suggest that although preterm labor is associated with increased PCT concentrations in maternal plasma, there is no significant association either between plasma concentration of PCT at the moment of threat and preterm delivery, or the admission-to-delivery interval. The predictive value of plasma PCT determinations is unsatisfactory.

  19. Predicting deoxynivalenol in oats under conditions representing Scandinavian production regions.

    PubMed

    Persson, Tomas; Eckersten, Henrik; Elen, Oleif; Roer Hjelkrem, Anne-Grete; Markgren, Joel; Söderström, Mats; Börjesson, Thomas

    2017-06-01

    Deoxynivalenol (DON) in cereals, produced by Fusarium fungi, cause poisoning in humans and animals. Fusarium infections in cereals are favoured by humid conditions. Host species are susceptible mainly during the anthesis stage. Infections are also positively correlated with a regional history of Fusarium infections, frequent cereal production and non-tillage field management practices. Here, previously developed process-based models based on relative air humidity, rain and temperature conditions, Fusarium sporulation, host phenology and mycelium growth in host tissue were adapted and tested on oats. Model outputs were used to calculate risk indices. Statistical multivariate models, where independent variables were constructed from weather data, were also developed. Regressions of the risk indices obtained against DON concentrations in field experiments on oats in Sweden and Norway 2012-14 had coefficient of determination values (R(2)) between 0.84 and 0.88. Regressions of the same indices against DON concentrations in oat samples averaged for 11 × 11 km grids in farmers' fields in Sweden 2012-14 resulted in R(2) values between 0.27 and 0.41 for randomly selected grids and between 0.31 and 0.62 for grids with average DON concentration above 1000 μg kg(-)(1) grain in the previous year. When data from all three years were evaluated together, a cross-validated statistical partial least squares model resulted in R(2) = 0.70 and a standard error of cross-validation (SECV) = 522 μg kg(-)(1) grain for the period 1 April-28 August in the construction of independent variables and R(2) = 0.54 and SECV = 647 μg kg(-)(1) grain for 1 April-23 June. Factors that were not accounted for in this study probably explain large parts of the variation in DON among samples and make further model development necessary before these models can be used practically. DON prediction in oats could potentially be improved by combining weather-based risk index outputs with

  20. Predictability in France : atmospheric forcing or land surface initial conditions?

    NASA Astrophysics Data System (ADS)

    Singla, S.; Martin, E.; Céron, J.-P.; Regimbeau, F.

    2010-09-01

    A first study of a hydrological forecasting suite has already been done at seasonal time scales over France (Céron and al., 2010) in a context of adaptation for water resources management. The results showed the feasibility of hydrological seasonal forecasts by forcing the hydrometeorological model Safran-Isba-Modcou (SIM) with seasonal atmospheric forecasts from the DEMETER project. Scores were better for hydrological variables than for atmospheric variables for four river catchments for the spring season. The purpose of the present study is to quantify the sources of predictability of the hydrometeorological system. Two experiences were conducted in order to address this issue. The first experience consisted in testing the impact of the land surface initial conditions. We used realistic land surface initial state produced by the operational SIM model for the specific year and 9 random years of Safran atmospheric analyses (temperature and precipitation) from 1971 to 2001, in a consistent way with the previous study (Céron et al, 2010). The other atmospheric parameters (wind, specific humidity, long wave and short wave radiation and cloudiness) come from the SAFRAN climatology over the same period. The second experience was designed to evaluate the impact of the atmospheric forcing with 9 random years, chosen for the land surface initial state. The atmospheric forcing (temperature and precipitation) comes from the Safran analysis system for the corresponding year. Some results of this study will be presented on soil wetness index (SWI) forecasts and river flows forecasts for all stations in France. We will compare deterministic and probabilistic scores of the two experiences with those of the hydrological forecasting suite built with the seasonal forecasts from the DEMETER project. Perspectives for the downscaling of seasonal forecasts will be discussed in a last part. Céron J-P, Tanguy G, Franchistéguy L, Martin E, Regimbeau F and Vidal J-P, 2010. Hydrological

  1. Prediction of Graft Patency and Mortality after Distal Revascularization and Interval Ligation for Hemodialysis Access Related Hand Ischemia

    PubMed Central

    Scali, Salvatore T.; Chang, Catherine K.; Raghinaru, Dan; Daniels, Michael; Beck, Adam W.; Feezor, Robert J.; Berceli, Scott A.; Huber, Thomas S.

    2014-01-01

    Objective The treatment goals for access related hand ischemia (ARHI) are to reverse symptoms and salvage the access. Many procedures have been described, but the optimal treatment strategy remains unresolved. In an effort to guide clinical decision making, this study was undertaken to document our outcomes for distal revascularization and interval ligation (DRIL) and identify predictors of bypass patency and patient mortality. Methods A retrospective review was performed of all patients who underwent DRIL at the University of Florida from 2002–2011. Diagnosis of ARHI was based primarily upon clinical symptoms with non-invasive studies used to corroborate in equivocal cases. Patient demographics, procedure-outcome variables and re-interventions were recorded. Bypass patency and mortality were estimated using cumulative incidence and Kaplan-Meier methodology, respectively. Cumulative incidence and Cox regression analysis were performed to determine predictors of bypass patency and mortality, respectively. Results 134 DRILs were performed in 126 patients (age 57±12yrs (mean±SD)) following brachial artery-based access. The post-operative complication rate was 27% (19%-wound), and 30-day mortality was 2%. The wrist/brachial (WBI) and digital/brachial (DBI) indices increased 0.31±0.25 and 0.25±0.29, respectively. Symptoms resolved in 82% of patients, and 85% continued to use their access. Cumulative incidences of loss of primary and primary-assisted patency rates were 5±2%, 4±2% and 22±5%, 18±5% at 1 and 5 years, respectively with a mean follow-up of 14.8 months. Univariate predictors of primary patency failure were DRIL complications (3.3; 1.2–8.9, p=0.02), configuration other than brachiobasilic/brachiocephalic autogenous access (3.4; 1.4–8.3, p=0.009), and ≥2 prior access attempts (4.1; 1.6–10.4, p=0.004). Brachiocephalic access configuration (0.2; 0.04–0.8, p =0.02), and autogenous vein conduit (0.2; 0.06–0.58, p=0.004) were predictors of

  2. Predicting optimal conditions to minimize quality deterioration while maximizing safety and functional properties of irradiated egg

    NASA Astrophysics Data System (ADS)

    Yun, Hyejeong; Jung, Yeonkuk; Lee, Kyung Haeng; Song, Hyun Pa; Kim, Keehyuk; Jo, Cheorun

    2012-08-01

    Irradiation is an excellent method for improving the safety and functional properties of egg. However, the internal quality of egg can be deteriorated due to a rapid decrease in Haugh units. In this study, the optimal conditions for maintaining the quality and maximizing the safety and functional properties of egg were determined when combination of irradiation and chitosan coating was treated using response surface methodology (RSM). Independent degradation parameters—irradiation dose (0-2 kGy) and concentration of chitosan coating (0-2%) were assigned (-2,-1, 0, 1, 2), and 10 intervals were set on the basis of central composite design for the degradation experiment. The dependant variables within a confidence level less than 5% were Haugh units, foaming ability, foam stability, and number of Salmonella typhimurium. The predicted maximum values of Haugh units and foaming ability were 82.7 (irradiation dose 0.0006 kGy and concentration of chitosan solution 1.03%) and 62.2 mm (1.99 kGy and 0.86%), respectively. S. typhimurium inoculated on the egg surface was not detected after 1.86 kGy and 0.48%. Based on superimposing four-dimensional RSM with respect to freshness (Haugh units), functional property (foaming capacity and foam stability), and reduction of S. typhimurium, the predicted optimum ranges for irradiation dose and chitosan solution concentration were 0.35-0.65 kGy and 0.25-0.85%, respectively. The predicted optimum values were obtained from 0.45 kGy and 0.525%. This methodology can be used to predict egg quality and safety when different combination treatments were applied.

  3. Interval Between Hysterectomy and Start of Radiation Treatment Is Predictive of Recurrence in Patients With Endometrial Carcinoma

    SciTech Connect

    Cattaneo, Richard; Hanna, Rabbie K.; Jacobsen, Gordon; Elshaikh, Mohamed A.

    2014-03-15

    Purpose: Adjuvant radiation therapy (RT) has been shown to improve local control in patients with endometrial carcinoma. We analyzed the impact of the time interval between hysterectomy and RT initiation in patients with endometrial carcinoma. Methods and Materials: In this institutional review board-approved study, we identified 308 patients with endometrial carcinoma who received adjuvant RT after hysterectomy. All patients had undergone hysterectomy, oophorectomy, and pelvic and para-aortic lymph node evaluation from 1988 to 2010. Patients' demographics, pathologic features, and treatments were compared. The time interval between hysterectomy and the start of RT was calculated. The effects of time interval on recurrence-free (RFS), disease-specific (DSS), and overall survival (OS) were calculated. Following univariate analysis, multivariate modeling was performed. Results: The median age and follow-up for the study cohort was 65 years and 72 months, respectively. Eighty-five percent of the patients had endometrioid carcinoma. RT was delivered with high-dose-rate brachytherapy alone (29%), pelvic RT alone (20%), or both (51%). Median time interval to start RT was 42 days (range, 21-130 days). A total of 269 patients (74%) started their RT <9 weeks after undergoing hysterectomy (group 1) and 26% started ≥9 weeks after surgery (group 2). There were a total of 43 recurrences. Tumor recurrence was significantly associated with treatment delay of ≥9 weeks, with 5-year RFS of 90% for group 1 compared to only 39% for group 2 (P<.001). On multivariate analysis, RT delay of ≥9 weeks (P<.001), presence of lymphovascular space involvement (P=.001), and higher International Federation of Gynecology and Obstetrics grade (P=.012) were independent predictors of recurrence. In addition, RT delay of ≥9 weeks was an independent significant predictor for worse DSS and OS (P=.001 and P=.01, respectively). Conclusions: Delay in administering adjuvant RT after hysterectomy was

  4. Body condition predicts energy stores in apex predatory sharks

    PubMed Central

    Gallagher, Austin J.; Wagner, Dominique N.; Irschick, Duncan J.; Hammerschlag, Neil

    2014-01-01

    Animal condition typically reflects the accumulation of energy stores (e.g. fatty acids), which can influence an individual's decision to undertake challenging life-history events, such as migration and reproduction. Accordingly, researchers often use measures of animal body size and/or weight as an index of condition. However, values of condition, such as fatty acid levels, may not always reflect the physiological state of animals accurately. While the relationships between condition indices and energy stores have been explored in some species (e.g. birds), they have yet to be examined in top predatory fishes, which often undertake extensive and energetically expensive migrations. We used an apex predatory shark (Galeocerdo cuvier, the tiger shark) as a model species to evaluate the relationship between triglycerides (energy metabolite) and a metric of overall body condition. We captured, blood sampled, measured and released 28 sharks (size range 125–303 cm pre-caudal length). In the laboratory, we assayed each plasma sample for triglyceride values. We detected a positive and significant relationship between condition and triglyceride values (P < 0.02). This result may have conservation implications if the largest and highest-condition sharks are exploited in fisheries, because these individuals are likely to have the highest potential for successful reproduction. Our results suggest that researchers may use either plasma triglyceride values or an appropriate measure of body condition for assessing health in large sharks. PMID:27293643

  5. Effect of clasp design on retention at different intervals using different abutment materials and in a simulated oral condition.

    PubMed

    Helal, Mohamed A; Baraka, Osama A; Sanad, Mohamed E; Al-Khiary, Yasser; Ludwig, Klaus; Kern, Matthias

    2014-02-01

    The purpose of this study was to compare the retention of circlet (E) clasps and back-action clasps against three abutment surface materials during long-term simulation of attachment and detachment. Forty-eight test models were constructed by placing premolars (natural abutments or metal dies) inside metal blocks to test different abutment retention surface materials (sound enamel, composite resin, and glass-ceramic; 16 each). The models were duplicated into investment models for construction of circlet (E) and back-action clasps. Removal and insertion cycling of clasps was carried out for 250, 500, 1000, 2000, 4000, 8000, and 16,000 cycles. The retention of each clasp was measured before cycling and after each interval. Data were analyzed using 1-way-ANOVA, 2-way-ANOVA, and Mann-Whitney U tests. No significant differences in retention of either clasp were found between the three abutment material surfaces; however, there was a significant decrease in retention force of the circlet (E) clasp between 1000 and 2000 cycles but not of the back-action clasp. (1) The back-action clasp maintains its retention force for a longer period than the circlet (E) clasp. (2) Composite resin contouring of teeth provided retention comparable to enamel and a ceramic material. © 2013 by the American College of Prosthodontists.

  6. Simple spatial prediction - least squares prediction, simple kriging, and conditional expectation of normal vector

    NASA Astrophysics Data System (ADS)

    Ligas, Marcin; Kulczycki, Marek

    2010-01-01

    The aim of the paper is the comparison of the least squares prediction presented by Heiskanen and Moritz (1967) in the classical handbook "Physical Geodesy" with the geostatistical method of simple kriging as well as in case of Gaussian random fields their equivalence to conditional expectation. The paper contains also short notes on the extension of simple kriging to ordinary kriging by dropping the assumption of known mean value of a random field as well as some necessary information on random fields, covariance function and semivariogram function. The semivariogram is emphasized in the paper, for two reasons. Firstly, the semivariogram describes broader class of phenomena, and for the second order stationary processes it is equivalent to the covariance function. Secondly, the analysis of different kinds of phenomena in terms of covariance is more common. Thus, it is worth introducing another function describing spatial continuity and variability. For the ease of presentation all the considerations were limited to the Euclidean space (thus, for limited areas) although with some extra effort they can be extended to manifolds like sphere, ellipsoid, etc.

  7. Noise Prediction of NASA SR2 Propeller in Transonic Conditions

    NASA Astrophysics Data System (ADS)

    Gennaro, Michele De; Caridi, Domenico; Nicola, Carlo De

    2010-09-01

    In this paper we propose a numerical approach for noise prediction of high-speed propellers for Turboprop applications. It is based on a RANS approach for aerodynamic simulation coupled with Ffowcs Williams-Hawkings (FW-H) Acoustic Analogy for propeller noise prediction. The test-case geometry adopted for this study is the 8-bladed NASA SR2 transonic cruise propeller, and simulated Sound Pressure Levels (SPL) have been compared with experimental data available from Wind Tunnel and Flight Tests for different microphone locations in a range of Mach numbers between 0.78 and 0.85 and rotational velocities between 7000 and 9000 rpm. Results show the ability of this approach to predict noise to within a few dB of experimental data. Moreover corrections are provided to be applied to acoustic numerical results in order for them to be compared with Wind Tunnel and Flight Test experimental data, as well computational grid requirements and guidelines in order to perform complete aerodynamic and aeroacoustic calculations with highly competitive computational cost.

  8. Baseline activity predicts working memory load of preceding task condition.

    PubMed

    Pyka, Martin; Hahn, Tim; Heider, Dominik; Krug, Axel; Sommer, Jens; Kircher, Tilo; Jansen, Andreas

    2013-11-01

    The conceptual notion of the so-called resting state of the brain has been recently challenged by studies indicating a continuing effect of cognitive processes on subsequent rest. In particular, activity in posterior parietal and medial prefrontal areas has been found to be modulated by preceding experimental conditions. In this study, we investigated which brain areas show working memory dependent patterns in subsequent baseline periods and how specific they are for the preceding experimental condition. During functional magnetic resonance imaging, 94 subjects performed a letter-version of the n-back task with the conditions 0-back and 2-back followed by a low-level baseline in which subjects had to passively observe the letters appearing. In a univariate analysis, 2-back served as control condition while 0-back, baseline after 0-back and baseline after 2-back were modeled as regressors to test for activity changes between both baseline conditions. Additionally, we tested, using Gaussian process classifiers, the recognition of task condition from functional images acquired during baseline. Besides the expected activity changes in the precuneus and medial prefrontal cortex, we found differential activity in the thalamus, putamen, and postcentral gyrus that were affected by the preceding task. The multivariate analysis revealed that images of the subsequent baseline block contain task related patterns that yield a recognition rate of 70%. The results suggest that the influence of a cognitive task on subsequent baseline is strong and specific for some areas but not restricted to areas of the so-called default mode network.

  9. Can we ease the financial burden of colonoscopy? Using real-time endoscopic assessment of polyp histology to predict surveillance intervals.

    PubMed

    Chandran, S; Parker, F; Lontos, S; Vaughan, R; Efthymiou, M

    2015-12-01

    Polyps identified at colonoscopy are predominantly diminutive (<5 mm) with a small risk (>1%) of high-grade dysplasia or carcinoma; however, the cost of histological assessment is substantial. The aim of this study was to determine whether prediction of colonoscopy surveillance intervals based on real-time endoscopic assessment of polyp histology is accurate and cost effective. A prospective cohort study was conducted across a tertiary care and private community hospital. Ninety-four patients underwent colonoscopy and polypectomy of diminutive (≤5 mm) polyps from October 2012 to July 2013, yielding a total of 159 polyps. Polyps were examined and classified according to the Sano-Emura classification system. The endoscopic assessment (optical diagnosis) of polyp histology was used to predict appropriate colonoscopy surveillance intervals. The main outcome measure was the accuracy of optical diagnosis of diminutive colonic polyps against the gold standard of histological assessment. Optical diagnosis was correct in 105/108 (97.2%) adenomas. This yielded a sensitivity, specificity and positive and negative predictive values (with 95%CI) of 97.2% (92.1-99.4%), 78.4% (64.7-88.7%), 90.5% (83.7-95.2%) and 93% (80.9-98.5%) respectively. Ninety-two (98%) patients were correctly triaged to their repeat surveillance colonoscopy. Based on these findings, a cut and discard approach would have resulted in a saving of $319.77 per patient. Endoscopists within a tertiary care setting can accurately predict diminutive polyp histology and confer an appropriate surveillance interval with an associated financial benefit to the healthcare system. However, limitations to its application in the community setting exist, which may improve with further training and high-definition colonoscopes. © 2015 Royal Australasian College of Physicians.

  10. Developing Landscape Level Indicators for Predicting Watershed Condition

    EPA Science Inventory

    Drainage basins (watersheds) exert a strong influence on the condition of water bodies such as streams and lakes. Watersheds and associated aquatic systems respond differently to stressors (e.g., land use change) or restoration activities depending on the climatic setting, bedroc...

  11. Developing Landscape Level Indicators for Predicting Watershed Condition

    EPA Science Inventory

    Drainage basins (watersheds) exert a strong influence on the condition of water bodies such as streams and lakes. Watersheds and associated aquatic systems respond differently to stressors (e.g., land use change) or restoration activities depending on the climatic setting, bedroc...

  12. Initial-Condition Sensitivities and the Predictability of Downslope Winds

    DTIC Science & Technology

    2009-11-01

    Also plotted are the distributions for the ( d ) 6-h, 1800 UTC IOP 6 forecast; (e) 6-h, 0000 UTC IOP 13 forecast; and ( f ) 12-h, 1800 UTC IOP 13...Institute of Physics, 247–270. ——, Y. Kuo, D . P. Baumhefner, R. M. Errico , and T. W. Bettge, 1985: Prediction of mesoscale atmospheric motions. Advances in...W. D . Hall, R. M. Kerr, L. Radke, F . M. Ralph, P. J. Neiman, and D . Levinson, 2000: Origins of aircraft-damaging clear-air turbulence during the 9

  13. Predicting Redox Conditions in Groundwater at a Regional Scale.

    PubMed

    Tesoriero, Anthony J; Terziotti, Silvia; Abrams, Daniel B

    2015-08-18

    Defining the oxic-suboxic interface is often critical for determining pathways for nitrate transport in groundwater and to streams at the local scale. Defining this interface on a regional scale is complicated by the spatial variability of reaction rates. The probability of oxic groundwater in the Chesapeake Bay watershed was predicted by relating dissolved O2 concentrations in groundwater samples to indicators of residence time and/or electron donor availability using logistic regression. Variables that describe surficial geology, position in the flow system, and soil drainage were important predictors of oxic water. The probability of encountering oxic groundwater at a 30 m depth and the depth to the bottom of the oxic layer were predicted for the Chesapeake Bay watershed. The influence of depth to the bottom of the oxic layer on stream nitrate concentrations and time lags (i.e., time period between land application of nitrogen and its effect on streams) are illustrated using model simulations for hypothetical basins. Regional maps of the probability of oxic groundwater should prove useful as indicators of groundwater susceptibility and stream susceptibility to contaminant sources derived from groundwater.

  14. Predicting redox conditions in groundwater at a regional scale

    USGS Publications Warehouse

    Tesoriero, Anthony J.; Terziotti, Silvia; Abrams, Daniel B.

    2015-01-01

    Defining the oxic-suboxic interface is often critical for determining pathways for nitrate transport in groundwater and to streams at the local scale. Defining this interface on a regional scale is complicated by the spatial variability of reaction rates. The probability of oxic groundwater in the Chesapeake Bay watershed was predicted by relating dissolved O2 concentrations in groundwater samples to indicators of residence time and/or electron donor availability using logistic regression. Variables that describe surficial geology, position in the flow system, and soil drainage were important predictors of oxic water. The probability of encountering oxic groundwater at a 30 m depth and the depth to the bottom of the oxic layer were predicted for the Chesapeake Bay watershed. The influence of depth to the bottom of the oxic layer on stream nitrate concentrations and time lags (i.e., time period between land application of nitrogen and its effect on streams) are illustrated using model simulations for hypothetical basins. Regional maps of the probability of oxic groundwater should prove useful as indicators of groundwater susceptibility and stream susceptibility to contaminant sources derived from groundwater.

  15. Analytical ice shape predictions for flight in natural icing conditions

    NASA Technical Reports Server (NTRS)

    Berkowitz, Brian M.; Riley, James T.

    1988-01-01

    LEWICE is an analytical ice prediction code that has been evaluated against icing tunnel data, but on a more limited basis against flight data. Ice shapes predicted by LEWICE is compared with experimental ice shapes accreted on the NASA Lewis Icing Research Aircraft. The flight data selected for comparison includes liquid water content recorded using a hot wire device and droplet distribution data from a laser spectrometer; the ice shape is recorded using stereo photography. The main findings are as follows: (1) An equivalent sand grain roughness correlation different from that used for LEWICE tunnel comparisons must be employed to obtain satisfactory results for flight; (2) Using this correlation and making no other changes in the code, the comparisons to ice shapes accreted in flight are in general as good as the comparisons to ice shapes accreted in the tunnel (as in the case of tunnel ice shapes, agreement is least reliable for large glaze ice shapes at high angles of attack); (3) In some cases comparisons can be somewhat improved by utilizing the code so as to take account of the variation of parameters such as liquid water content, which may vary significantly in flight.

  16. Correct use of cone penetrometer sensors to predict subsurface conditions

    SciTech Connect

    Walker, J.L.; Rose, C.M.; Armstrong, S.C.; Burton, J.C.

    1997-09-01

    When cone penetrometer testing (CPT) technology is used with in-situ sensors and probes to characterize subsurface conditions in environmental investigations, each sensor must be calibrated with high quality, site specific data to establish essential interpretation criteria. Mechanical, geophysical, and chemical sensor data collected for a site in South Carolina without such controls were misleading. Core logs obtained subsequently had major lithologic discrepancies with the soil classification based on the CPT sensor data. In addition, detailed core sampling and laboratory analysis showed that the sensor data on chemical contaminants included false positive and false negative results. In contrast, for a site in Nebraska, CPT data calibrated with high quality site controls provided a detailed interpretation of subsurface conditions relevant to contaminant fate and transport. On the basis of the work in Nebraska, Argonne scientists are continuing to develop criteria to improve the interpretation of complex subsurface stratigraphy.

  17. Interval Training

    MedlinePlus

    ... before trying any type of interval training. Recent studies suggest, however, that interval training can be used safely for short periods even in individuals with heart disease. Also keep the risk of overuse injury in mind. If you rush into a strenuous workout before ...

  18. Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification

    NASA Astrophysics Data System (ADS)

    Lu, Dan; Ye, Ming; Hill, Mary C.

    2012-09-01

    Confidence intervals based on classical regression theories augmented to include prior information and credible intervals based on Bayesian theories are conceptually different ways to quantify parametric and predictive uncertainties. Because both confidence and credible intervals are used in environmental modeling, we seek to understand their differences and similarities. This is of interest in part because calculating confidence intervals typically requires tens to thousands of model runs, while Bayesian credible intervals typically require tens of thousands to millions of model runs. Given multi-Gaussian distributed observation errors, our theoretical analysis shows that, for linear or linearized-nonlinear models, confidence and credible intervals are always numerically identical when consistent prior information is used. For nonlinear models, nonlinear confidence and credible intervals can be numerically identical if parameter confidence regions defined using the approximate likelihood method and parameter credible regions estimated using Markov chain Monte Carlo realizations are numerically identical and predictions are a smooth, monotonic function of the parameters. Both occur if intrinsic model nonlinearity is small. While the conditions of Gaussian errors and small intrinsic model nonlinearity are violated by many environmental models, heuristic tests using analytical and numerical models suggest that linear and nonlinear confidence intervals can be useful approximations of uncertainty even under significantly nonideal conditions. In the context of epistemic model error for a complex synthetic nonlinear groundwater problem, the linear and nonlinear confidence and credible intervals for individual models performed similarly enough to indicate that the computationally frugal confidence intervals can be useful in many circumstances. Experiences with these groundwater models are expected to be broadly applicable to many environmental models. We suggest that for

  19. QCT/FEA predictions of femoral stiffness are strongly affected by boundary condition modeling

    PubMed Central

    Rossman, Timothy; Kushvaha, Vinod; Dragomir-Daescu, Dan

    2015-01-01

    Quantitative computed tomography-based finite element models of proximal femora must be validated with cadaveric experiments before using them to assess fracture risk in osteoporotic patients. During validation it is essential to carefully assess whether the boundary condition modeling matches the experimental conditions. This study evaluated proximal femur stiffness results predicted by six different boundary condition methods on a sample of 30 cadaveric femora and compared the predictions with experimental data. The average stiffness varied by 280% among the six boundary conditions. Compared with experimental data the predictions ranged from overestimating the average stiffness by 65% to underestimating it by 41%. In addition we found that the boundary condition that distributed the load to the contact surfaces similar to the expected contact mechanics predictions had the best agreement with experimental stiffness. We concluded that boundary conditions modeling introduced large variations in proximal femora stiffness predictions. PMID:25804260

  20. Prediction of lagoons' natural conditions using satellite data and GIS.

    PubMed

    Goksel, Cigdem; Seker, Dursun Z; Kabdasli, Sedat

    2003-08-01

    In this study, monitoring and management of coastlines were emphasized and usage of remotely sensed data and GIS has been proposed as alternative solution to conventional studies. An example of using satellite data for depth measurement in shallow areas is given. Past morphological and hydrodynamic structures were obtained by means of Remote Sensing technique and obtained data have been transferred to GIS. Results extracted from these measurements were compared with the bathymetric map and visualized by means of GIS. Our proposal is to use remotely sensed data combined with GIS in the cases where the data obtained via ground measurements have been inadequate. It has been shown that the integrated approach can be used satisfactorily in order to predict the possible effects of a river induced parameters such as turbidity on the coasts, because the suspended material in the water can be used as the tracer material in interpretation of remotely sensed data.

  1. Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences

    PubMed Central

    Polley, Eric C.; Briggs, Farren B. S.; van der Laan, Mark J.; Hubbard, Alan

    2016-01-01

    Comparing the relative fit of competing models can be used to address many different scientific questions. In classical statistics one can, if appropriate, use likelihood ratio tests and information based criterion, whereas clinical medicine has tended to rely on comparisons of fit metrics like C-statistics. However, for many data adaptive modelling procedures such approaches are not suitable. In these cases, statisticians have used cross-validation, which can make inference challenging. In this paper we propose a general approach that focuses on the “conditional” risk difference (conditional on the model fits being fixed) for the improvement in prediction risk. Specifically, we derive a Wald-type test statistic and associated confidence intervals for cross-validated test sets utilizing the independent validation within cross-validation in conjunction with a test for multiple comparisons. We show that this test maintains proper Type I Error under the null fit, and can be used as a general test of relative fit for any semi-parametric model alternative. We apply the test to a candidate gene study to test for the association of a set of genes in a genetic pathway. PMID:26529567

  2. Prediction and monitoring of fluid responsiveness after coronary bypass surgery using the Initial Systolic Time Interval: Preliminary results

    NASA Astrophysics Data System (ADS)

    Smorenberg, A.; Lust, E. J.; Verdaasdonk, R. M.; Groeneveld, A. B. J.; Meijer, J. H.

    2010-04-01

    The objective of the study is to develop a non-invasive method to optimize the assessment of cardiac preload and therapeutic fluid administration after coronary artery bypass surgery. Previous studies have reported that the pre-ejection period (PEP), obtained from the electro-cardiogram (ECG) and from the invasively measured arterial pressure Pa, can be used for this assessment as it is dependent on the cardiac preload. The Initial Systolic Time Interval (ISTI), obtained non-invasively by simultaneous measurement of the Electro-CardioGram (ECG) and Impedance CardioGram (ICG), is expected to depend on the cardiac preload as well. 16 patients, admitted to the Intensive Care Unit after coronary artery bypass surgery and presumably hypovolaemic, were measured during administration of 2×250 ml of an isosmotic colloidal fluid solution. The parameters PEP and ISTI were determined before and after the administrations and compared with the change in cardiac output (CO), obtained by a thermodilution technique. Preliminary results show significant relationships between ISTI and CO and between changes in both of these variables before and after fluid administration.

  3. Geomagnetic Secular Variation Prediction with Thermal Heterogeneous Boundary Conditions

    NASA Technical Reports Server (NTRS)

    Kuang, Weijia; Tangborn, Andrew; Jiang, Weiyuan

    2011-01-01

    It has long been conjectured that thermal heterogeneity at the core-mantle boundary (CMB) affects the geodynamo substantially. The observed two pairs of steady and strong magnetic flux lobes near the Polar Regions and the low secular variation in the Pacific over the past 400 years (and perhaps longer) are likely the consequences of this CMB thermal heterogeneity. There are several studies on the impact of the thermal heterogeneity with numerical geodynamo simulations. However, direct correlation between the numerical results and the observations is found very difficult, except qualitative comparisons of certain features in the radial component of the magnetic field at the CMB. This makes it difficult to assess accurately the impact of thermal heterogeneity on the geodynamo and the geomagnetic secular variation. We revisit this problem with our MoSST_DAS system in which geomagnetic data are assimilated with our geodynamo model to predict geomagnetic secular variations. In this study, we implement a heterogeneous heat flux across the CMB that is chosen based on the seismic tomography of the lowermost mantle. The amplitude of the heat flux (relative to the mean heat flux across the CMB) varies in the simulation. With these assimilation studies, we will examine the influences of the heterogeneity on the forecast accuracies, e.g. the accuracies as functions of the heterogeneity amplitude. With these, we could be able to assess the model errors to the true core state, and thus the thermal heterogeneity in geodynamo modeling.

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

    ERIC Educational Resources Information Center

    Ladd, Helen F.

    2009-01-01

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

  5. The Use of Commercial Remote Sensing Systems in Predicting Helicopter Brownout Conditions

    DTIC Science & Technology

    2009-09-01

    REMOTE SENSING IN PREDICTING HELICOPTER BROWNOUT CONDITIONS by Christine Kay Rabaja September 2009 Thesis Advisor: Richard C. Olsen...Master’s Thesis 4. TITLE AND SUBTITLE The Use of Commercial Remote Sensing Systems in Predicting Helicopter Brownout Conditions 6. AUTHOR...soils susceptible to helicopter brownout . Helicopter brownout occurs when downwash disturbs the dust and sand beneath the aircraft during takeoff

  6. Interoceptive fear conditioning and panic disorder: the role of conditioned stimulus-unconditioned stimulus predictability.

    PubMed

    Acheson, Dean T; Forsyth, John P; Moses, Erica

    2012-03-01

    Interoceptive fear conditioning is at the core of contemporary behavioral accounts of panic disorder. Yet, to date only one study has attempted to evaluate interoceptive fear conditioning in humans (see Acheson, Forsyth, Prenoveau, & Bouton, 2007). That study used brief (physiologically inert) and longer-duration (panicogenic) inhalations of 20% CO(2)-enriched air as an interoceptive conditioned (CS) and unconditioned (US) stimulus and evaluated fear learning in three conditions: CS only, CS-US paired, and CS-US unpaired. Results showed fear conditioning in the paired condition, and fearful responding and resistance to extinction in an unpaired condition. The authors speculated that such effects may be due to difficulty discriminating between the CS and the US. The aims of the present study are to (a) replicate and expand this line of work using an improved methodology, and (b) clarify the role of CS-US discrimination difficulties in either potentiating or depotentiating fear learning. Healthy participants (N=104) were randomly assigned to one of four conditions: (a) CS only, (b) contingent CS-US pairings, (c) unpaired CS and US presentations, or (d) an unpaired "discrimination" contingency, which included an exteroceptive discrimination cue concurrently with CS onset. Electrodermal and self-report ratings served as indices of conditioned responding. Consistent with expectation, the paired contingency and unpaired contingencies yielded elevated fearful responding to the CS alone. Moreover, adding a discrimination cue to the unpaired contingency effectively attenuated fearful responding. Overall, findings are consistent with modern learning theory accounts of panic and highlight the role of interoceptive conditioning and unpredictability in the etiology of panic disorder. Copyright © 2011. Published by Elsevier Ltd.

  7. Comparison and Validation of Recommended QT Interval Correction Formulas for Predicting Cardiac Arrhythmias in Patients With Advanced Heart Failure and Cardiac Resynchronization Devices.

    PubMed

    Iglesias-Álvarez, Diego; Rodríguez-Mañero, Moisés; García-Seara, Francisco Javier; Kreidieh, Omar; Martínez-Sande, José Luis; Álvarez-Álvarez, Belén; Fernández-López, Xesús Alberte; González-Melchor, Laila; Lage-Fernández, Ricardo; Moscoso-Galán, Isabel; González-Juanatey, José Ramón

    2017-09-15

    QT interval prolongation is an important marker for the development of cardiac arrhythmias (CAs). Optimal methods to estimate QT/QTc intervals in patients with ventricular pacing (VP) and its correlation with CA have not been widely investigated. We aimed to validate the currently available formulas for QT determination during VP and to compare their abilities in predicting the occurrence of CA (atrial fibrillation [AF] and malignant ventricular arrhythmias [VAs] in patients with advanced heart failure and cardiac resynchronization therapy). Consecutive patients with advanced heart failure who underwent cardiac resynchronization therapy implantation between August 2001 and April 2015 were included in a retrospective study. Four proposed formulas for QT correction in VP rhythms were evaluated. One hundred eighty patients were enrolled. During 44 months of follow-up, 43 patients (37.7%) developed AF and 16 patients (8.9%) developed VA. There was no correlation between corrected QT increments and AF risk with any of the formulas for paced rhythms. Regarding VA, higher corrected QT values measured with Massachusetts' formula (QTcM) were found to have a higher risk of event (p = 0.036) (Beta = 1.012 [1.001 to 1.023]). Each 1 ms increase in QTc increased the probability of experiencing VA by 12‰. QTcM >444 was found to be a strong predictor of VA. In conclusion, there are significant differences in mean QTc interval measured by the currently advised formulas. QTc interval was not associated with AF in any of the formulas. Only the QTcM formula showed a significant stepwise increase in the risk of experiencing malignant VA. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. User guide to the UNC process and three utility programs for computation of nonlinear confidence and prediction intervals using MODFLOW-2000

    USGS Publications Warehouse

    Christensen, Steen; Cooley, Richard L.

    2006-01-01

    This report introduces and documents the Uncertainty (UNC) Process, a new Process in MODFLOW-2000 that calculates uncertainty measures for model parameters and for predictions produced by the model. Uncertainty measures can be computed by various methods, but when regression is applied to calibrate a model (for example when using the Parameter-Estimation Process of MODFLOW-2000) it is advantageous to also use regression-based methods to quantify uncertainty. For this reason the UNC Process computes (1) confidence intervals for parameters of the Parameter-Estimation Process and (2) confidence and prediction intervals for most types of functions that can be computed by a MODFLOW-2000 model calibrated by the Parameter-Estimation Process. The types of functions for which the Process works include hydraulic heads, hydraulic head differences, head-dependent flows computed by the head-dependent flow packages for drains (DRN6), rivers (RIV6), general-head boundaries (GHB6), streams (STR6), drain-return cells (DRT1), and constant-head boundaries (CHD), and for differences between flows computed by any of the mentioned flow packages. The UNC Process does not allow computation of intervals for the difference between flows computed by two different flow packages. The report also documents three programs, RESAN2-2k, BEALE2-2k, and CORFAC-2k, which are valuable for the evaluation of results from the Parameter-Estimation Process and for the preparation of input values for the UNC Process. RESAN2-2k and BEALE2-2k are significant updates of the residual analysis and modified Beale's measure programs first published by Cooley and Naff (1990) and later modified for use with MODFLOWP (Hill, 1994) and MODFLOW-2000 (Hill and others, 2000). CORFAC-2k is a new program that computes correction factors to be used by UNC.

  9. Predicting soil fumigant air concentrations under regional and diverse agronomic conditions.

    PubMed

    Cryer, Steven A

    2005-01-01

    SOFEA (SOil Fumigant Exposure Assessment system; Dow AgroSciences, Indianapolis, IN) is a new stochastic numerical modeling tool for evaluating and managing human inhalation exposure potential associated with the use of soil fumigants. SOFEA calculates fumigant concentrations in air arising from volatility losses from treated fields for large agricultural regions using multiple transient source terms (treated fields), geographical information systems (GIS) information, agronomic specific variables, user-specified buffer zones, and field reentry intervals. A modified version of the USEPA Industrial Source Complex Short Term model (ISCST3) is used for air dispersion calculations. Weather information, field size, application date, application rate, application type, soil incorporation depth, pesticide degradation rates in air, tarp presence, field retreatment, and other sensitive parameters are varied stochastically using Monte Carlo techniques to mimic region and crop specific agronomic practices. Regional land cover, elevation, and population information can be used to refine source placement (treated fields), dispersion calculations, and risk assessments. This paper describes the technical algorithms of SOFEA and offers comparisons of simulation predictions for the soil fumigant 1,3-dichloropropene (1,3-D) to actual regional air monitoring measurements from Kern, California. Comparison of simulation results to daily air monitoring observations is remarkable over the entire concentration distribution (average percent deviation of 44% and model efficiency of 0.98), especially considering numerous inputs such as meteorological conditions for SOFEA were unavailable and approximated by neighboring regions. Both current and anticipated and/or forecasted fumigant scenarios can be simulated using SOFEA to provide risk managers and product stewards the necessary information to make sound regulatory decisions regarding the use of soil fumigants in agriculture.

  10. Differential involvement of medial prefrontal cortex and basolateral amygdala extracellular signal-regulated kinase in extinction of conditioned taste aversion is dependent on different intervals of extinction following conditioning.

    PubMed

    Lin, P-Y; Wang, S-P; Tai, M-Y; Tsai, Y-F

    2010-11-24

    Extinction reflects a decrease in the conditioned response (CR) following non-reinforcement of a conditioned stimulus. Behavioral evidence indicates that extinction involves an inhibitory learning mechanism in which the extinguished CR reappears with presentation of an unconditioned stimulus. However, recent studies on fear conditioning suggest that extinction erases the original conditioning if the time interval between fear acquisition and extinction is short. The present study examined the effects of different intervals between acquisition and extinction of the original memory in conditioned taste aversion (CTA). Male Long-Evans rats acquired CTA by associating a 0.2% sucrose solution with malaise induced by i.p. injection of 4 ml/kg 0.15 M LiCl. Two different time intervals, 5 and 24 h, between CTA acquisition and extinction were used. Five or 24 h after CTA acquisition, extinction trials were performed, in which a bottle containing 20 ml of a 0.2% sucrose solution was provided for 10 min without subsequent LiCl injection. If sucrose consumption during the extinction trials was greater than the average water consumption, then rats were considered to have reached CTA extinction. Rats subjected to extinction trials lasting 24 h, but not 5 h, after acquisition re-exhibited the extinguished CR following injection of 0.15 M LiCl alone 7 days after acquisition. Extracellular signal-regulated kinase (ERK) in the medial prefrontal cortex (mPFC) and basolateral nucleus of the amygdala (BLA) was examined by Western blot after the first extinction trial. ERK activation in the mPFC was induced after the extinction trial beginning 5 h after acquisition, whereas the extinction trial performed 24 h after acquisition induced ERK activation in the BLA. These data suggest that the original conditioning can be inhibited or retained by CTA extinction depending on the time interval between acquisition and extinction and that the ERK transduction pathway in the mPFC and BLA is

  11. Prediction of an estimated delivery date should take into account both the length of a previous pregnancy and the interpregnancy interval.

    PubMed

    Ng, Ka Ying Bonnie; Steer, Philip J

    2016-06-01

    To assess the relationship between gestational lengths of the first and second pregnancies in the same women. Observational study. We used information from a dataset of over 500,000 pregnancies from 15 maternity units in the North West Thames, London. Data on the gestational length in days of the first pregnancy and the gestational length in days of the second pregnancy were correlated using regression models. First and second pregnancies were ascribed to the same women by identical maternal date of birth, ethnicity and maternal height (to within ±3cm). There is a statistically significant cubic relationship between the gestational lengths of the first birth and the second birth (R 0.102, p<0.001). The gestational length of the second pregnancy is likely to be closer to 280 days than the first pregnancy. In the 20% of women who had an interpregnancy interval of less than one year, the next pregnancy was one day shorter for every three months less than 12. Although the gestation of second pregnancies exhibits regression towards the mean of 280 days, there is still a clinically important tendency for both preterm and postdates pregnancies to recur. Prediction of an estimated delivery date for second pregnancies should take into account both the length of the first pregnancy and the interpregnancy interval if it is less than 12 months. Crown Copyright © 2016. Published by Elsevier Ireland Ltd. All rights reserved.

  12. Sensitivity of Typhoon Track Predictions in a Regional Prediction System to Initial and Lateral Boundary Conditions

    DTIC Science & Technology

    2009-09-01

    less forecast skill due to a coarser res- olution. Miguez- Macho and Paegle (2000) suggest that accurate initial and lateral boundary conditions for a...nondeveloping ver- sus developing systems. J. Atmos. Sci., 38, 1132–1151. Miguez- Macho , G., and J. Paegle, 2000: Sensitivity of a global forecast model

  13. Body Condition Indices Predict Reproductive Success but Not Survival in a Sedentary, Tropical Bird

    PubMed Central

    Milenkaya, Olga; Catlin, Daniel H.; Legge, Sarah; Walters, Jeffrey R.

    2015-01-01

    Body condition may predict individual fitness because those in better condition have more resources to allocate towards improving their fitness. However, the hypothesis that condition indices are meaningful proxies for fitness has been questioned. Here, we ask if intraspecific variation in condition indices predicts annual reproductive success and survival. We monitored a population of Neochmia phaeton (crimson finch), a sedentary, tropical passerine, for reproductive success and survival over four breeding seasons, and sampled them for commonly used condition indices: mass adjusted for body size, muscle and fat scores, packed cell volume, hemoglobin concentration, total plasma protein, and heterophil to lymphocyte ratio. Our study population is well suited for this research because individuals forage in common areas and do not hold territories such that variation in condition between individuals is not confounded by differences in habitat quality. Furthermore, we controlled for factors that are known to impact condition indices in our study population (e.g., breeding stage) such that we assessed individual condition relative to others in the same context. Condition indices that reflect energy reserves predicted both the probability of an individual fledging young and the number of young produced that survived to independence, but only during some years. Those that were relatively heavy for their body size produced about three times more independent young compared to light individuals. That energy reserves are a meaningful predictor of reproductive success in a sedentary passerine supports the idea that energy reserves are at least sometimes predictors of fitness. However, hematological indices failed to predict reproductive success and none of the indices predicted survival. Therefore, some but not all condition indices may be informative, but because we found that most indices did not predict any component of fitness, we question the ubiquitous interpretation of

  14. Body Condition Indices Predict Reproductive Success but Not Survival in a Sedentary, Tropical Bird.

    PubMed

    Milenkaya, Olga; Catlin, Daniel H; Legge, Sarah; Walters, Jeffrey R

    2015-01-01

    Body condition may predict individual fitness because those in better condition have more resources to allocate towards improving their fitness. However, the hypothesis that condition indices are meaningful proxies for fitness has been questioned. Here, we ask if intraspecific variation in condition indices predicts annual reproductive success and survival. We monitored a population of Neochmia phaeton (crimson finch), a sedentary, tropical passerine, for reproductive success and survival over four breeding seasons, and sampled them for commonly used condition indices: mass adjusted for body size, muscle and fat scores, packed cell volume, hemoglobin concentration, total plasma protein, and heterophil to lymphocyte ratio. Our study population is well suited for this research because individuals forage in common areas and do not hold territories such that variation in condition between individuals is not confounded by differences in habitat quality. Furthermore, we controlled for factors that are known to impact condition indices in our study population (e.g., breeding stage) such that we assessed individual condition relative to others in the same context. Condition indices that reflect energy reserves predicted both the probability of an individual fledging young and the number of young produced that survived to independence, but only during some years. Those that were relatively heavy for their body size produced about three times more independent young compared to light individuals. That energy reserves are a meaningful predictor of reproductive success in a sedentary passerine supports the idea that energy reserves are at least sometimes predictors of fitness. However, hematological indices failed to predict reproductive success and none of the indices predicted survival. Therefore, some but not all condition indices may be informative, but because we found that most indices did not predict any component of fitness, we question the ubiquitous interpretation of

  15. Predictions of Daily Milk and Fat Yields, Major Groups of Fatty Acids, and C18:1 cis-9 from Single Milking Data without a Milking Interval

    PubMed Central

    Arnould, Valérie M. R.; Reding, Romain; Bormann, Jeanne; Gengler, Nicolas; Soyeurt, Hélène

    2015-01-01

    Simple Summary Reducing the frequency of milk recording decreases the costs of official milk recording. However, this approach can negatively affect the accuracy of predicting daily yields. Equations to predict daily yield from morning or evening data were developed in this study for fatty milk components from traits recorded easily by milk recording organizations. The correlation values ranged from 96.4% to 97.6% (96.9% to 98.3%) when the daily yields were estimated from the morning (evening) milkings. The simplicity of the proposed models which do not include the milking interval should facilitate their use by breeding and milk recording organizations. Abstract Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem has been investigated in numerous studies. In addition, published equations take into account milking intervals (MI), and these are often not available and/or are unreliable in practice. The first objective of this study was to propose models in which the MI was replaced by a combination of data easily recorded by dairy farmers. The second objective was to further investigate the fatty acids (FA) present in milk. Equations to predict daily yield from AM or PM data were based on a calibration database containing 79,971 records related to 51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)]. These equations were validated using two distinct external datasets. The results obtained from the proposed models were compared to previously published results for

  16. Prediction of pharmacologically induced baroreflex sensitivity from local time and frequency domain indices of R-R interval and systolic blood pressure signals obtained during deep breathing.

    PubMed

    Arica, Sami; Firat Ince, N; Bozkurt, Abdi; Tewfik, Ahmed H; Birand, Ahmet

    2011-07-01

    Pharmacological measurement of baroreflex sensitivity (BRS) is widely accepted and used in clinical practice. Following the introduction of pharmacologically induced BRS (p-BRS), alternative assessment methods eliminating the use of drugs were in the center of interest of the cardiovascular research community. In this study we investigated whether p-BRS using phenylephrine injection can be predicted from non-pharmacological time and frequency domain indices computed from electrocardiogram (ECG) and blood pressure (BP) data acquired during deep breathing. In this scheme, ECG and BP data were recorded from 16 subjects in a two-phase experiment. In the first phase the subjects performed irregular deep breaths and in the second phase the subjects received phenylephrine injection. From the first phase of the experiment, a large pool of predictors describing the local characteristic of beat-to-beat interval tachogram (RR) and systolic blood pressure (SBP) were extracted in time and frequency domains. A subset of these indices was selected using twelve subjects with an exhaustive search fused with a leave one subject out cross validation procedure. The selected indices were used to predict the p-BRS on the remaining four test subjects. A multivariate regression was used in all prediction steps. The algorithm achieved best prediction accuracy with only two features extracted from the deep breathing data, one from the frequency and the other from the time domain. The normalized L2-norm error was computed as 22.9% and the correlation coefficient was 0.97 (p=0.03). These results suggest that the p-BRS can be estimated from non-pharmacological indices computed from ECG and invasive BP data related to deep breathing. Copyright © 2011 Elsevier Ltd. All rights reserved.

  17. Age related vascular endothelial function following lifelong sedentariness: positive impact of cardiovascular conditioning without further improvement following low frequency high intensity interval training.

    PubMed

    Grace, Fergal M; Herbert, Peter; Ratcliffe, John W; New, Karl J; Baker, Julien S; Sculthorpe, Nicholas F

    2015-01-01

    Aging is associated with diffuse impairments in vascular endothelial function and traditional aerobic exercise is known to ameliorate these changes. High intensity interval training (HIIT) is effective at improving vascular function in aging men with existing disease, but its effectiveness remains to be demonstrated in otherwise healthy sedentary aging. However, the frequency of commonly used HIIT protocols may be poorly tolerated in older cohorts. Therefore, the present study investigated the effectiveness of lower frequency HIIT (LfHIIT) on vascular function in a cohort of lifelong sedentary (SED; n = 22, age 62.7 ± 5.2 years) men compared with a positive control group of lifelong exercisers (LEX; n = 17, age 61.1 ± 5.4 years). The study consisted of three assessment phases; enrolment to the study (Phase A), following 6 weeks of conditioning exercise in SED (Phase B) and following 6 weeks of low frequency HIIT in both SED and LEX (LfHIIT; Phase C). Conditioning exercise improved FMD in SED (3.4 ± 1.5% to 4.9 ± 1.1%; P < 0.01) such that the difference between groups on enrolment (3.4 ± 1.5% vs. 5.3 ± 1.4%; P < 0.01) was abrogated. This was maintained but not further improved following LfHIIT in SED whilst FMD remained unaffected by LfHIIT in LEX. In conclusion, LfHIIT is effective at maintaining improvements in vascular function achieved during conditioning exercise in SED. LfHIIT is a well-tolerated and effective exercise mode for reducing cardiovascular risk and maintaining but does not improve vascular function beyond that achieved by conditioning exercise in aging men, irrespective of fitness level.

  18. Age related vascular endothelial function following lifelong sedentariness: positive impact of cardiovascular conditioning without further improvement following low frequency high intensity interval training

    PubMed Central

    Grace, Fergal M.; Herbert, Peter; Ratcliffe, John W.; New, Karl J.; Baker, Julien S.; Sculthorpe, Nicholas F.

    2015-01-01

    Abstract Aging is associated with diffuse impairments in vascular endothelial function and traditional aerobic exercise is known to ameliorate these changes. High intensity interval training (HIIT) is effective at improving vascular function in aging men with existing disease, but its effectiveness remains to be demonstrated in otherwise healthy sedentary aging. However, the frequency of commonly used HIIT protocols may be poorly tolerated in older cohorts. Therefore, the present study investigated the effectiveness of lower frequency HIIT (LfHIIT) on vascular function in a cohort of lifelong sedentary (SED; n =22, age 62.7 ± 5.2 years) men compared with a positive control group of lifelong exercisers (LEX; n = 17, age 61.1 ± 5.4 years). The study consisted of three assessment phases; enrolment to the study (Phase A), following 6 weeks of conditioning exercise in SED (Phase B) and following 6 weeks of low frequency HIIT in both SED and LEX (LfHIIT; Phase C). Conditioning exercise improved FMD in SED (3.4 ± 1.5% to 4.9 ± 1.1%; P <0.01) such that the difference between groups on enrolment (3.4 ± 1.5% vs. 5.3 ± 1.4%; P <0.01) was abrogated. This was maintained but not further improved following LfHIIT in SED whilst FMD remained unaffected by LfHIIT in LEX. In conclusion, LfHIIT is effective at maintaining improvements in vascular function achieved during conditioning exercise in SED. LfHIIT is a well‐tolerated and effective exercise mode for reducing cardiovascular risk and maintaining but does not improve vascular function beyond that achieved by conditioning exercise in aging men, irrespective of fitness level. PMID:25626864

  19. High intensity interval training vs. high-volume running training during pre-season conditioning in high-level youth football: a cross-over trial.

    PubMed

    Faude, Oliver; Schnittker, Reinhard; Schulte-Zurhausen, Roman; Müller, Florian; Meyer, Tim

    2013-01-01

    We aimed at comparing the endurance effects of high-intensity interval training (HIIT) with high-volume running training (HVT) during pre-season conditioning in 20 high-level youth football players (15.9 (s 0.8) years). Players either conducted HVT or HIIT during the summer preparation period. During winter preparation they performed the other training programme. Before and after each training period several fitness tests were conducted: multi-stage running test (to assess the individual anaerobic threshold (IAT) and maximal running velocity (Vmax)), vertical jumping height, and straight sprinting. A significant increase from pre- to post-test was observed in IAT velocity (P < 0.001) with a greater increase after HVT (+0.8 km · h(-1) vs. +0.5 km · h(-1) after HIIT, P = 0.04). Maximal velocity during the incremental exercise test also slightly increased with time (P = 0.09). Forty per cent (HIIT) and 15% (HVT) of all players did not improve IAT beyond baseline variability. The players who did not respond to HIIT were significantly slower during 30 m sprinting than responders (P = 0.02). No further significant differences between responders and non-responders were observed. Jump heights deteriorated significantly after both training periods (P < 0.003). Both training programmes seem to be promising means to improve endurance capacity in high-level youth football players during pre-season conditioning.

  20. Prediction of sand transport over immobile gravel from supply limited to capacity conditions

    USDA-ARS?s Scientific Manuscript database

    The prediction of the transport of sand in armored gravel reaches downstream of dams is complicated by variable bed conditions ranging from sand transported through gravel to sand in transport over buried gravel. Knowledge of the rate of sand transport in these conditions, however, is necessary for...

  1. The importance of spring atmospheric conditions for predictions of the Arctic summer sea ice extent

    NASA Astrophysics Data System (ADS)

    Kapsch, Marie-Luise; Graversen, Rune G.; Economou, Theodoros; Tjernström, Michael

    2014-07-01

    Recent studies have shown that atmospheric processes in spring play an important role for the initiation of the summer ice melt and therefore may strongly influence the September sea ice concentration (SSIC). Here a simple statistical regression model based on only atmospheric spring parameters is applied in order to predict the SSIC over the major part of the Arctic Ocean. By using spring anomalies of downwelling longwave radiation or atmospheric water vapor as predictor variables, correlation coefficients between observed and predicted SSIC of up to 0.5 are found. These skills of seasonal SSIC predictions are similar to those obtained using more complex dynamical forecast systems, despite the fact that the simple model applied here takes neither information of the sea ice state, oceanic conditions nor feedback mechanisms during summer into account. The results indicate that a realistic representation of spring atmospheric conditions in the prediction system plays an important role for the predictive skills of a model system.

  2. Sea surface temperatures and environmental conditions during the ;warm Pliocene; interval ( 4.1-3.2 Ma) in the Eastern Mediterranean (Cyprus)

    NASA Astrophysics Data System (ADS)

    Athanasiou, M.; Bouloubassi, I.; Gogou, A.; Klein, V.; Dimiza, M. D.; Parinos, C.; Skampa, E.; Triantaphyllou, M. V.

    2017-03-01

    Organic geochemical (alkenones) and micropaleontological (nannofossil) data from the Pissouri South section (PPS) in the island of Cyprus provided a detailed description of the paleoclimatic (sea surface temperature-SST) and paleoenvironmental conditions during the ;warm Pliocene; (c. 4.1-3.25 Ma) in the Eastern Mediterranean. We found that the suite of sapropel events recorded in the studied interval took place under conditions of increased SST, enhanced water column stratification and development of a productive deep chlorophyll maximum (DCM), as witnessed by the dominance of Florisphaera profunda species. Such conditions are similar to those prevailing during Quaternary sapropel formation, triggered by freshwater discharges from the N. African margin due to insolation-driven intensification of the African monsoon. The absence of F. profunda in Pliocene sapropels from central Mediterranean records highlights the sensitive response of the eastern basin to freshwater perturbations. Comparisons between alkenone and calcareous nannofossil assemblage patterns infer Pseudoemiliania lacunosa as the main alkenone producer in sapropel layers; yet Reticulofenestra spp. contribution cannot be ruled out. The first Pliocene alkenone-SST record in the E. Mediterranean presented here documents the ;warm Pliocene; period ( 4.1-3.25 Ma) characterized by mean SST of c. 26 °C. Distinct SST minima at 3.9 Ma, 3.58 Ma and between 3.34 and 3.31 Ma, correspond to the MIS Gi16, MIS MG12 and MIS M2 global cooling episodes, before the onset of the Northern Hemisphere glaciation. Our findings imply that the peak of the MIS M2 cooling in the Eastern Mediterranean may be up to 40 kyrs older than the age attributed before to benthic stable oxygen isotopes records of this event.

  3. Predictive Mapping of the Biotic Condition of Conterminous-USA Rivers and Streams.

    PubMed

    Hill, Ryan A; Fox, Eric W; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

    2017-09-04

    Understanding and mapping the spatial variation in stream biological condition could provide an important tool for conservation, assessment, and restoration of stream ecosystems. The USEPA's 2008-2009 National Rivers and Streams Assessment (NRSA) summarizes the percent of stream lengths within the conterminous US that are in good, fair, or poor biological condition based on a multimetric index of benthic invertebrate assemblages. However, condition is usually summarized at regional or national scales, and these assessments do not provide substantial insight into the spatial distribution of conditions at unsampled locations. We used random forests to model and predict the probable condition of several million kilometers of streams across the conterminous US based on nearby and upstream landscape features, including human-related alterations to watersheds. To do so, we linked NRSA sample sites to the USEPA's StreamCat Dataset; a database of several hundred landscape metrics for all 1:100,000-scale streams and their associated watersheds within the conterminous US. The StreamCat data provided geospatial indicators of nearby and upstream land use, land cover, climate, and other landscape features for modeling. Nationally, the model correctly predicted the biological condition class of 75% of NRSA sites. Although model evaluations suggested good discrimination among condition classes, we present maps as predicted probabilities of good condition, given upstream and nearby landscape settings. Inversely, the maps can be interpreted as the probability of a stream being in poor condition, given human-related watershed alterations. These predictions are available for download from the USEPA's StreamCat website (https://w w w .epa. gov/national-aquatic-resource-surveys/streamcat). Finally, we illustrate how these predictions could be used to prioritize streams for conservation or restoration. This article is protected by copyright. All rights reserved. This article is

  4. Pharmacological aversion treatment of alcohol dependence. I. Production and prediction of conditioned alcohol aversion.

    PubMed

    Howard, M O

    2001-08-01

    Eighty-two hospitalized alcoholics receiving pharmacological aversion therapy (PAT) over a 10-day treatment interval completed cognitive, behavioral, and psychophysiological measures evaluating conditioned aversion to alcohol. Pre-post assessments provided convergent support for the efficacy of PAT vis-à-vis production of conditioned aversion to alcohol. Positive alcohol-related outcome expectancies were significantly reduced, whereas confidence that drinking could be avoided in various high-risk situations for consumption was increased following PAT. Behavioral and cardiac rate assessments revealed significant changes following PAT that were specific to alcoholic beverages and potentially reflective of conditioned alcohol aversion. Patients with more extensive pretreatment experiences with alcohol-associated nausea and greater involvement in antisocial conduct appeared to be less susceptible to the PAT conditioning protocol.

  5. Interval to Biochemical Failure Predicts Clinical Outcomes in Patients With High-Risk Prostate Cancer Treated by Combined-Modality Radiation Therapy

    SciTech Connect

    Shilkrut, Mark; McLaughlin, P. William; Merrick, Gregory S.; Vainshtein, Jeffrey M.; Feng, Felix Y.; Hamstra, Daniel A.

    2013-07-15

    Purpose: To validate the prognostic value of interval to biochemical failure (IBF) in patients with high-risk prostate cancer (HiRPCa) treated with combined-modality radiation therapy (CMRT) with or without androgen deprivation therapy (ADT). Methods and Materials: We conducted a retrospective review of HiRPCa (prostate-specific antigen >20 ng/mL, Gleason score [GS] 8-10, or clinical T stage T3-T4) treated with either dose-escalated external beam radiation therapy (EBRT) or CMRT. Interval to biochemical failure was classified as ≤18 or >18 months from the end of all therapy to the date of biochemical failure (BF). Kaplan-Meier methods and Cox proportional hazards regression were used to evaluate the prognostic value of IBF ≤18 months for distant metastasis (DM) and prostate cancer-specific mortality (PCSM). Results: Of 958 patients with a median follow-up of 63.2 months, 175 patients experienced BF. In those with BF, there were no differences in pretreatment clinical characteristics between the EBRT and CMRT groups, except for a higher proportion of patients with GS 8-10 in the CMRT group (70% vs 52%, P=.02). Median IBF after all therapy was 24.0 months (interquartile range 9.6-46.0) in the EBRT group and 18.9 months (interquartile range 9.2-34.5) in the CMRT group (P=.055). On univariate analysis, IBF ≤18 months was associated with increased risk of DM and PCSM in the entire cohort and the individual EBRT and CMRT groups. On multivariate analysis, only GS 9-10 and IBF ≤18 months, but not the radiation therapy regimen or ADT use, predicted DM (hazard ratio [HR] 3.7, P<.01, 95% confidence interval [CI] 1.4-10.3 for GS 9-10; HR 3.9, P<.0001, 95% CI 2.4-6.5 for IBF ≤18 months) and PCSM (HR 14.8, P<.009, 95% CI 2.0-110 for GS 9-10; HR 4.4, P<.0001, 95% CI 2.4-8.1 for IBF ≤18 months). Conclusions: Short IBF was highly prognostic for higher DM and PCSM in patients with HiRPCa. The prognostic value of IBF for DM and PCSM was not affected by the radiation

  6. Do morphological condition indices predict locomotor performance in the lizard Podarcis sicula?

    NASA Astrophysics Data System (ADS)

    Vervust, Bart; Lailvaux, Simon P.; Grbac, Irena; Van Damme, Raoul

    2008-09-01

    Biologists have developed a number of simple metrics to assess the health and energetic status of individual organisms and populations. While these condition indices have been widely used to address questions in evolutionary ecology and conservation biology, the ability of such indices to predict ecologically relevant locomotor performance abilities remains unknown. We show here that the functional links between six commonly used morphological condition indices and locomotor performance in two populations of Adriatic lizards ( Podarcis sicula) are weak at best. Indeed, no indices consistently predict either maximum sprint speed or maximum exertion across sexes, seasons or populations. These results cast doubt on the ecological relevance of morphological condition indices in terms of locomotor performance, measured in laboratory conditions, at least in this species. We urge caution in using condition indices as proxies for individual physiological or phenotypic quality in ecological and evolutionary studies.

  7. Mechanisms of attention to conditioned stimuli predictive of a cigarette outcome.

    PubMed

    Austin, A J; Duka, T

    2012-06-15

    Attention to stimuli associated with a rewarding outcome may be mediated by the incentive motivational properties that the stimulus acquires during conditioning. Other theories of attention state that the prediction error (the discrepancy between the expected and the actual outcome) during conditioning guides attention; once the outcome is fully predicted, attention should be abolished for the conditioned stimulus. The current study examined which of these mechanisms is dominant in conditioning when the outcome is highly rewarding. Allocation of attention to stimuli associated with cigarettes (the rewarding outcome) was tested in 16 smokers, who underwent a classical conditioning paradigm, where abstract visual stimuli were paired with a tobacco outcome. Stimuli were associated with 100% (stimulus A), 50% (stimulus B), or 0% (stimulus C) probability of receiving tobacco. Attention was measured using an eye-tracker device, and the appetitive value of the stimuli was measured with subjective pleasantness ratings during the conditioning process. Dwell time bias (duration of eye gaze) was greatest overall for the A stimulus, and increased over conditioning. Attention to stimulus A was dependent on the ratings of pleasantness that the stimulus evoked, and on the desire to smoke. These findings appear to support the theory that attention for conditioned stimuli is dominated by the incentive motivational qualities of the outcome they predict, and implicate a role for attention in the maintenance of addictive behaviours like smoking. Copyright © 2012 Elsevier B.V. All rights reserved.

  8. Faecal haemoglobin concentration influences risk prediction of interval cancers resulting from inadequate colonoscopy quality: analysis of the Taiwanese Nationwide Colorectal Cancer Screening Program.

    PubMed

    Chiu, Sherry Yueh-Hsia; Chuang, Shu-Ling; Chen, Sam Li-Sheng; Yen, Amy Ming-Fang; Fann, Jean Ching-Yuan; Chang, Dun-Cheng; Lee, Yi-Chia; Wu, Ming-Shiang; Chou, Chu-Kuang; Hsu, Wen-Feng; Chiou, Shu-Ti; Chiu, Han-Mo

    2017-02-01

    Interval colorectal cancer (CRC) after colonoscopy may affect effectiveness and cost-effectiveness of screening programmes. We aimed to investigate whether and how faecal haemoglobin concentration (FHbC) of faecal immunochemical testing (FIT) affected the risk prediction of interval cancer (IC) caused by inadequate colonoscopy quality in a FIT-based population screening programme. From 2004 to 2009, 29 969 subjects underwent complete colonoscopy after positive FIT in the Taiwanese Nationwide CRC Screening Program. The IC rate was traced until the end of 2012. The incidence of IC was calculated in relation to patient characteristics, endoscopy-related factors (such adenoma detection rate (ADR)) and FHbC. Poisson regression analysis was performed to assess the potential risk factors for colonoscopy IC. One hundred and sixty-two ICs developed after an index colonoscopy and the estimated incidence was 1.14 per 1000 person-years of observation for the entire cohort. Increased risk of IC was most remarkable in the uptake of colonoscopy in settings with ADR lower than 15% (adjusted relative risk (aRR)=3.09, 95% CI 1.55 to 6.18) and then higher FHbC (μg Hb/g faeces) (100-149: aRR=2.55, 95% CI 1.52 to 4.29, ≥150: aRR=2.74, 95% CI 1.84 to 4.09) with adjustment for older age and colorectal neoplasm detected at baseline colonoscopy. Similar findings were observed for subjects with negative index colonoscopy. Colonoscopy ICs arising from FIT-based population screening programmes were mainly influenced by inadequate colonoscopy quality and independently predicted by FHbC that is associated with a priori chance of advanced neoplasm. This finding is helpful for future modification of screening logistics based on FHbC. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  9. Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads

    NASA Technical Reports Server (NTRS)

    Parlos, Alexander G.; Toliyat, Hamid A.

    2005-01-01

    An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses.

  10. Predicting fear of heights, snakes, and public speaking from multimodal classical conditioning events.

    PubMed

    Wu, Ning Ying; Conger, Anthony J; Dygdon, Judith A

    2006-04-01

    Two hundred fifty one men and women participated in a study of the prediction of fear of heights, snakes, and public speaking by providing retrospective accounts of multimodal classical conditioning events involving those stimuli. The fears selected for study represent those believed by some to be innate (i.e., heights), prepared (i.e., snakes), and purely experientially learned (i.e., public speaking). This study evaluated the extent to which classical conditioning experiences in direct, observational, and verbal modes contributed to the prediction of the current level of fear severity. Subjects were asked to describe their current level of fear and to estimate their experience with fear response-augmenting events (first- and higher-order aversive pairings) and fear response-moderating events (first- and higher-order appetitive pairings, and pre- and post-conditioning neutral presentations) in direct, observational, and verbal modes. For each stimulus, fear was predictable from direct response-augmenting events and prediction was enhanced by the inclusion of response-moderating events. Furthermore, for each fear, maximum prediction was attained by the addition of variables tapping experiences in the observational and/or verbal modes. Conclusions are offered regarding the importance of including response-augmenting and response-moderating events in all three modes in both research and clinical applications of classical conditioning.

  11. Studies on effects of boundary conditions in confined turbulent flow predictions

    NASA Technical Reports Server (NTRS)

    Nallasamy, M.; Chen, C. P.

    1985-01-01

    The differences in k epsilon model predictions of plane and axisymmetric expansion flows is investigated. The prediction of the coaxial jet for different velocity ratios of the annular to central jet is presented. The effects of inlet kinetic energy and the energy dissipation rate profiles are investigated for swirling and nonswirling flows. The effects of expansion ration and Reynolds number on the reattachment length are also presented. The results show that the inlet k and epsilon profiles have the most significant effect on the reattachment length and flow redevelopment for the case of coaxial jet of high velocity ratio. A comparison of k epsilon model predictions for the pipe expansion flow by the PHOENICS and TEACH codes reveals some discrepancies in the predicted results. TEACH prediction seems to produce unrealistic kinetic energy profiles in some regions of the flow. PHOENICS code produces a long tail in the recirculation region under certain conditions.

  12. Development of a predictive program for Vibrio parahaemolyticus growth under various environmental conditions.

    PubMed

    Fujikawa, Hiroshi; Kimura, Bon; Fujii, Tateo

    2009-09-01

    In this study, we developed a predictive program for Vibrio parahaemolyticus growth under various environmental conditions. Raw growth data was obtained with a V. parahaemolyticus O3:K6 strain cultured at a variety of broth temperatures, pH, and salt concentrations. Data were analyzed with our logistic model and the parameter values of the model were analyzed with polynomial equations. A prediction program consisting of the growth model and the polynomial equations was then developed. After the range of the growth environments was modified, the program successfully predicted the growth for all environments tested. The program could be a useful tool to ensure the bacteriological safety of seafood.

  13. ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction Toolbox

    NASA Technical Reports Server (NTRS)

    Martin, Rodney A.; Santanu, Das; Janakiraman, Vijay Manikandan; Hosein, Stefan

    2015-01-01

    The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we introduce a generic framework developed in MATLAB (sup registered mark) called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox). ACCEPT is an architectural framework designed to compare and contrast the performance of a variety of machine learning and early warning algorithms, and tests the capability of these algorithms to robustly predict the onset of adverse events in any time-series data generating systems or processes.

  14. Acoustical prediction methods for heating, ventilating, and air-conditioning (HVAC) systems

    NASA Astrophysics Data System (ADS)

    Ryherd, S. R.; Wang, L. M.

    2005-09-01

    The goal of this project is to compare and contrast various aspects of acoustical prediction methods for heating, ventilating, and air-conditioning (HVAC) systems. The three methods include two commonly used software programs and a custom spread sheet developed by the authors based on the American's Society of Heating, Refrigeration, and Air-conditioning Engineers (ASHRAE) Applications Handbook. Preliminary results indicate relatively good agreement between the three methods analyzed. The degree of disparity is predominately effected by the assumptions required by the end user. Research methods and results will be presented. This project provides a greater understanding of these acoustical prediction methods and their limitations.

  15. Predictions of Daily Milk and Fat Yields, Major Groups of Fatty Acids, and C18:1 cis-9 from Single Milking Data without a Milking Interval.

    PubMed

    Arnould, Valérie M R; Reding, Romain; Bormann, Jeanne; Gengler, Nicolas; Soyeurt, Hélène

    2015-07-31

    Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem has been investigated in numerous studies. In addition, published equations take into account milking intervals (MI), and these are often not available and/or are unreliable in practice. The first objective of this study was to propose models in which the MI was replaced by a combination of data easily recorded by dairy farmers. The second objective was to further investigate the fatty acids (FA) present in milk. Equations to predict daily yield from AM or PM data were based on a calibration database containing 79,971 records related to 51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)]. These equations were validated using two distinct external datasets. The results obtained from the proposed models were compared to previously published results for models which included a MI effect. The corresponding correlation values ranged from 96.4% to 97.6% when the daily yields were estimated from the AM milkings and ranged from 96.9% to 98.3% when the daily yields were estimated from the PM milkings. The simplicity of these proposed models should facilitate their use by breeding and milk recording organizations.

  16. Individualized conditioning regimes in cord blood transplantation: Towards improved and predictable safety and efficacy.

    PubMed

    Admiraal, R; Boelens, J J

    2016-06-01

    The conditioning regimen used in cord blood transplantation (CBT) may significantly impact the outcomes. Variable pharmacokinetics (PK) of drugs used may further influence outcome. Individualized dosing takes inter-patient differences in PK into account, tailoring drug dose for each individual patient in order to reach optimal exposure. Dose individualization may result in a better predictable regimen in terms of safety and efficacy, including timely T cell reconstitution, which may result in improved survival chances. Conditioning regimens used in CBT varies significantly between and within centres. For busulfan, individualized dosing with therapeutic drug monitoring has resulted in better outcomes. Anti-thymocyte globulin (ATG), used to prevent rejection and GvHD, significantly hampers early T-cell reconstitution (IR). Timely IR is crucial in preventing viral reactivations and relapse. By individudalizing ATG, IR is better predicted and may prevent morbidity and mortality. Individualization of agents used in the conditioning regimen in CBT has proven its added value. Further fine-tuning, including new drugs and/or comprehensive models for all drugs, may result in better predictable conditioning regimens. A predictable conditioning regimen is also of interest/importance when studying adjuvant therapies, including immunotherapies (e.g. cellular vaccines or engineered T-cell) in a harmonized clinical trial design setting.

  17. Do sexual ornaments demonstrate heightened condition-dependent expression as predicted by the handicap hypothesis?

    PubMed Central

    Cotton, Samuel; Fowler, Kevin; Pomiankowski, Andrew

    2004-01-01

    The handicap hypothesis of sexual selection predicts that sexual ornaments have evolved heightened condition-dependent expression. The prediction has only recently been subject to experimental investigation. Many of the experiments are of limited value as they: (i) fail to compare condition dependence in sexual ornaments with suitable non-sexual trait controls; (ii) do not adequately account for body size variation; and (iii) typically consider no stress and extreme stress manipulations rather than a range of stresses similar to those experienced in nature. There is also a dearth of experimental studies investigating the genetic basis of condition dependence. Despite the common claim that sexual ornaments are condition-dependent, the unexpected conclusion from our literature review is that there is little support from well-designed experiments. PMID:15255094

  18. A multivariate conditional model for streamflow prediction and spatial precipitation refinement

    NASA Astrophysics Data System (ADS)

    Liu, Zhiyong; Zhou, Ping; Chen, Xiuzhi; Guan, Yinghui

    2015-10-01

    The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine-based model is compared with that of traditional data-driven models such as the support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.

  19. Restaging oesophageal cancer after neoadjuvant therapy with (18)F-FDG PET-CT: identifying interval metastases and predicting incurable disease at surgery.

    PubMed

    Findlay, John M; Gillies, Richard S; Franklin, James M; Teoh, Eugene J; Jones, Greg E; di Carlo, Sara; Gleeson, Fergus V; Maynard, Nicholas D; Bradley, Kevin M; Middleton, Mark R

    2016-10-01

    It is unknown whether restaging oesophageal cancer after neoadjuvant therapy with positron emission tomography-computed tomography (PET-CT) is more sensitive than contrast-enhanced CT for disease progression. We aimed to determine this and stratify risk. This was a retrospective study of patients staged before neoadjuvant chemotherapy (NAC) by (18)F-FDG PET-CT and restaged with CT or PET-CT in a single centre (2006-2014). Three hundred and eighty-three patients were restaged (103 CT, 280 PET-CT). Incurable disease was detected by CT in 3 (2.91 %) and PET-CT in 17 (6.07 %). Despite restaging unsuspected incurable disease was encountered at surgery in 34/336 patients (10.1 %). PET-CT was more sensitive than CT (p = 0.005, McNemar's test). A new classification of FDG-avid nodal stage (mN) before NAC (plus tumour FDG-avid length) predicted subsequent progression, independent of conventional nodal stage. The presence of FDG-avid nodes after NAC and an impassable tumour stratified risk of incurable disease at surgery into high (75.0 %; both risk factors), medium (22.4 %; either), and low risk (3.87 %; neither) groups (p < 0.001). Decision theory supported restaging PET-CT. PET-CT is more sensitive than CT for detecting interval progression; however, it is insufficient in at least higher risk patients. mN stage and response (mNR) plus primary tumour characteristics can stratify this risk simply. • Restaging (18) F-FDG-PET-CT after neoadjuvant chemotherapy identifies metastases in 6 % of patients • Restaging (18) F-FDG-PET-CT is more sensitive than CT for detecting interval progression • Despite this, at surgery 10 % of patients had unsuspected incurable disease • New concepts (FDG-avid nodal stage and response) plus tumour impassability stratify risk • Higher risk (if not all) patients may benefit from additional restaging modalities.

  20. Predicting failure using conditioning on damage history: Demonstration on percolation and hierarchical fiber bundles

    SciTech Connect

    Andersen, J.V.; Sornette, D.

    2005-11-01

    We formulate the problem of probabilistic predictions of global failure in the simplest possible model based on site percolation and on one of the simplest models of time-dependent rupture, a hierarchical fiber bundle model. We show that conditioning the predictions on the knowledge of the current degree of damage (occupancy density p or number and size of cracks) and on some information on the largest cluster improves significantly the prediction accuracy, in particular by allowing one to identify those realizations which have anomalously low or large clusters (cracks). We quantify the prediction gains using two measures, the relative specific information gain (which is the variation of entropy obtained by adding new information) and the root mean square of the prediction errors over a large ensemble of realizations. The bulk of our simulations have been obtained with the two-dimensional site percolation model on a lattice of size LxL=20x20 and hold true for other lattice sizes. For the hierarchical fiber bundle model, conditioning the measures of damage on the information of the location and size of the largest crack extends significantly the critical region and the prediction skills. These examples illustrate how ongoing damage can be used as a revelation of both the realization-dependent preexisting heterogeneity and the damage scenario undertaken by each specific sample.

  1. Remote sensing for prediction of 1-year post-fire ecosystem condition

    Treesearch

    Leigh B. Lentile; Alistair M. S. Smith; Andrew T. Hudak; Penelope Morgan; Michael J. Bobbitt; Sarah A. Lewis; Peter R. Robichaud

    2009-01-01

    Appropriate use of satellite data in predicting >1 year post-fire effects requires remote measurement of surface properties that can be mechanistically related to ground measures of post-fire condition. The present study of burned ponderosa pine (Pinus ponderosa) forests in the Black Hills of South Dakota evaluates whether immediate fractional cover estimates of...

  2. Prediction of sand transport over immobile gravel from supply limited to capacity conditions

    USDA-ARS?s Scientific Manuscript database

    Prediction of the transport of sand in channels armored with gravel downstream of dams is difficult but necessary for the range of bed conditions from supply limited to capacity transport. Previous work has shown that information on the mean elevation of the sand relative to the gravel and on the s...

  3. Informal Workplace Learning among Nurses: Organisational Learning Conditions and Personal Characteristics That Predict Learning Outcomes

    ERIC Educational Resources Information Center

    Kyndt, Eva; Vermeire, Eva; Cabus, Shana

    2016-01-01

    Purpose: This paper aims to examine which organisational learning conditions and individual characteristics predict the learning outcomes nurses achieve through informal learning activities. There is specific relevance for the nursing profession because of the rapidly changing healthcare systems. Design/Methodology/Approach: In total, 203 nurses…

  4. Informal Workplace Learning among Nurses: Organisational Learning Conditions and Personal Characteristics That Predict Learning Outcomes

    ERIC Educational Resources Information Center

    Kyndt, Eva; Vermeire, Eva; Cabus, Shana

    2016-01-01

    Purpose: This paper aims to examine which organisational learning conditions and individual characteristics predict the learning outcomes nurses achieve through informal learning activities. There is specific relevance for the nursing profession because of the rapidly changing healthcare systems. Design/Methodology/Approach: In total, 203 nurses…

  5. Posterior Predictive Checks for Conditional Independence between Response Time and Accuracy

    ERIC Educational Resources Information Center

    Bolsinova, Maria; Tijmstra, Jesper

    2016-01-01

    Conditional independence (CI) between response time and response accuracy is a fundamental assumption of many joint models for time and accuracy used in educational measurement. In this study, posterior predictive checks (PPCs) are proposed for testing this assumption. These PPCs are based on three discrepancy measures reflecting different…

  6. Literature-based condition-specific miRNA-mRNA target prediction.

    PubMed

    Oh, Minsik; Rhee, Sungmin; Moon, Ji Hwan; Chae, Heejoon; Lee, Sunwon; Kang, Jaewoo; Kim, Sun

    2017-01-01

    miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary

  7. Literature-based condition-specific miRNA-mRNA target prediction

    PubMed Central

    Oh, Minsik; Rhee, Sungmin; Moon, Ji Hwan; Chae, Heejoon; Lee, Sunwon; Kang, Jaewoo; Kim, Sun

    2017-01-01

    miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3′-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In

  8. Appetite and gut hormone responses to moderate-intensity continuous exercise versus high-intensity interval exercise, in normoxic and hypoxic conditions.

    PubMed

    Bailey, Daniel P; Smith, Lindsey R; Chrismas, Bryna C; Taylor, Lee; Stensel, David J; Deighton, Kevin; Douglas, Jessica A; Kerr, Catherine J

    2015-06-01

    This study investigated the effects of continuous moderate-intensity exercise (MIE) and high-intensity interval exercise (HIIE) in combination with short exposure to hypoxia on appetite and plasma concentrations of acylated ghrelin, peptide YY (PYY), and glucagon-like peptide-1 (GLP-1). Twelve healthy males completed four, 2.6 h trials in a random order: (1) MIE-normoxia, (2) MIE-hypoxia, (3) HIIE-normoxia, and (4) HIIE-hypoxia. Exercise took place in an environmental chamber. During MIE, participants ran for 50 min at 70% of altitude-specific maximal oxygen uptake (V˙O2max) and during HIIE performed 6 × 3 min running at 90% V˙O2max interspersed with 6 × 3 min active recovery at 50% V˙O2max with a 7 min warm-up and cool-down at 70% V˙O2max (50 min total). In hypoxic trials, exercise was performed at a simulated altitude of 2980 m (14.5% O2). Exercise was completed after a standardised breakfast. A second meal standardised to 30% of participants' daily energy requirements was provided 45 min after exercise. Appetite was suppressed more in hypoxia than normoxia during exercise, post-exercise, and for the full 2.6 h trial period (linear mixed modelling, p <0.05). Plasma acylated ghrelin concentrations were lower in hypoxia than normoxia post-exercise and for the full 2.6 h trial period (p <0.05). PYY concentrations were higher in HIIE than MIE under hypoxic conditions during exercise (p = 0.042). No differences in GLP-1 were observed between conditions (p > 0.05). These findings demonstrate that short exposure to hypoxia causes suppressions in appetite and plasma acylated ghrelin concentrations. Furthermore, appetite responses to exercise do not appear to be influenced by exercise modality.

  9. Impact of in-consistency between the climate model and its initial conditions on climate prediction

    NASA Astrophysics Data System (ADS)

    Liu, Xueyuan; Köhl, Armin; Stammer, Detlef; Masuda, Shuhei; Ishikawa, Yoichi; Mochizuki, Takashi

    2016-05-01

    We investigated the influence of dynamical in-consistency of initial conditions on the predictive skill of decadal climate predictions. The investigation builds on the fully coupled global model "Coupled GCM for Earth Simulator" (CFES). In two separate experiments, the ocean component of the coupled model is full-field initialized with two different initial fields from either the same coupled model CFES or the GECCO2 Ocean Synthesis while the atmosphere is initialized from CFES in both cases. Differences between both experiments show that higher SST forecast skill is obtained when initializing with coupled data assimilation initial conditions (CIH) instead of those from GECCO2 (GIH), with the most significant difference in skill obtained over the tropical Pacific at lead year one. High predictive skill of SST over the tropical Pacific seen in CIH reflects the good reproduction of El Niño events at lead year one. In contrast, GIH produces additional erroneous El Niño events. The tropical Pacific skill differences between both runs can be rationalized in terms of the zonal momentum balance between the wind stress and pressure gradient force, which characterizes the upper equatorial Pacific. In GIH, the differences between the oceanic and atmospheric state at initial time leads to imbalance between the zonal wind stress and pressure gradient force over the equatorial Pacific, which leads to the additional pseudo El Niño events and explains reduced predictive skill. The balance can be reestablished if anomaly initialization strategy is applied with GECCO2 initial conditions and improved predictive skill in the tropical Pacific is observed at lead year one. However, initializing the coupled model with self-consistent initial conditions leads to the highest skill of climate prediction in the tropical Pacific by preserving the momentum balance between zonal wind stress and pressure gradient force along the equatorial Pacific.

  10. Impact of in-consistency between the climate model and its initial conditions on climate prediction

    NASA Astrophysics Data System (ADS)

    Liu, Xueyuan; Köhl, Armin; Stammer, Detlef; Masuda, Shuhei; Ishikawa, Yoichi; Mochizuki, Takashi

    2017-08-01

    We investigated the influence of dynamical in-consistency of initial conditions on the predictive skill of decadal climate predictions. The investigation builds on the fully coupled global model "Coupled GCM for Earth Simulator" (CFES). In two separate experiments, the ocean component of the coupled model is full-field initialized with two different initial fields from either the same coupled model CFES or the GECCO2 Ocean Synthesis while the atmosphere is initialized from CFES in both cases. Differences between both experiments show that higher SST forecast skill is obtained when initializing with coupled data assimilation initial conditions (CIH) instead of those from GECCO2 (GIH), with the most significant difference in skill obtained over the tropical Pacific at lead year one. High predictive skill of SST over the tropical Pacific seen in CIH reflects the good reproduction of El Niño events at lead year one. In contrast, GIH produces additional erroneous El Niño events. The tropical Pacific skill differences between both runs can be rationalized in terms of the zonal momentum balance between the wind stress and pressure gradient force, which characterizes the upper equatorial Pacific. In GIH, the differences between the oceanic and atmospheric state at initial time leads to imbalance between the zonal wind stress and pressure gradient force over the equatorial Pacific, which leads to the additional pseudo El Niño events and explains reduced predictive skill. The balance can be reestablished if anomaly initialization strategy is applied with GECCO2 initial conditions and improved predictive skill in the tropical Pacific is observed at lead year one. However, initializing the coupled model with self-consistent initial conditions leads to the highest skill of climate prediction in the tropical Pacific by preserving the momentum balance between zonal wind stress and pressure gradient force along the equatorial Pacific.

  11. [Development of a predictive program for microbial growth under various temperature conditions].

    PubMed

    Fujikawa, Hiroshi; Yano, Kazuyoshi; Morozumi, Satoshi; Kimura, Bon; Fujii, Tateo

    2006-12-01

    A predictive program for microbial growth under various temperature conditions was developed with a mathematical model. The model was a new logistic model recently developed by us. The program predicts Escherichia coli growth in broth, Staphylococcus aureus growth and its enterotoxin production in milk, and Vibrio parahaemolyticus growth in broth at various temperature patterns. The program, which was built with Microsoft Excel (Visual Basic Application), is user-friendly; users can easily input the temperature history of a test food and obtain the prediction instantly on the computer screen. The predicted growth and toxin production can be important indices to determine whether a food is microbiologically safe or not. This program should be a useful tool to confirm the microbial safety of commercial foods.

  12. Changes in respiratory quotient elicited in rats by a conditioned stimulus predicting food.

    PubMed

    McGregor, I S; Lee, A M

    1998-01-01

    The present study examined whether changes in energy expenditure and energy substrate utilization occur in rats exposed to a conditioned stimulus that signals food. In a differential conditioning procedure, rats were given conditioning sessions where one of two cues (either a flashing light or buzzer) predicted a carbohydrate-rich meal (CS+) while the other cue predicted no food (CS-). In two subsequent test sessions, indirect calorimetry was used to measure respiratory quotient, energy expenditure, and locomotor activity before, during, and after a 15-min CS+ or CS- presentation. The CS+ was found to significantly increase respiratory quotient, indicating a shift in the energy substrate being utilized toward carbohydrate. The CS+ also increased energy expenditure and locomotor activity, but these effects were more variable across rats. It is concluded that respiratory quotient may rise in anticipation of a carbohydrate-rich meal. Possible mechanisms underlying this effect are discussed.

  13. Predicting conditional means of explanatory variables for energy-demand forecasting

    SciTech Connect

    Parti, M.; Parti, C.B.; Gould, D.M.; Parris, K.M.

    1984-11-01

    The purpose of the research was to explore the feasibility of an economic and statistical approach to the problem of forecasting the conditional means of variables used to predict appliance-specific energy demand. The technique recognizes the necessity of using conditional means of explanatory variables, and it takes into account the changing distributions of these variables over time. Statistical relationships are used to derive the probability distribution of the explanatory variables, given ownership of an appliance such as a pool heater. The conditional means of the explanatory variables can then be calculated on the basis of this conditional probability distribution. The present research has found sizable differences in forecasts of appliance-specific energy demand generated from the conditional means approach and the non-specific approach.

  14. Ovum pick-up interval in buffalo (Bubalus bubalis) managed under wetland conditions in Argentina: Effect on follicular population, oocyte recovery, and in vitro embryo development.

    PubMed

    Konrad, J; Clérico, G; Garrido, M J; Taminelli, G; Yuponi, M; Yuponi, R; Crudeli, G; Sansinena, M

    2017-08-01

    The excellent adaptation of water buffalo (Bubalis bubalis) to swampy environments means that animals are frequently managed in areas with restricted access for reproductive procedures. The objective of this study was to evaluate the effect of the ovum pick-up (OPU) interval on follicular population, oocyte recovery, oocyte quality and in vitro embryo production. Twelve Murrah buffaloes were subjected to two consecutive dominant follicle reductions, and randomly assigned to either 7-day (n=6) or 14-day (n=6) OPU interval groups. Although there was no significant difference in the average number of small (<3mm) and large (>8mm) diameter follicles available per OPU, a higher proportion of medium-sized follicles (3-8mm) were observed in the 14-day interval group (5.129 vs 3.267; p<0.05). The number of recovered oocytes per donor was also significantly higher (4.51 vs. 2.8; p<0.05) in the 14-day interval group, although this was attributed to an increase in the proportion of lower quality oocytes (grades III and IV). After in vitro fertilization, embryo developmental competence from grade I and II oocytes was superior to that from grade III and IV oocytes, irrespective of OPU interval group. There was no significant difference in the proportion of grade I and II oocytes cleaved after sperm co-incubation; however, there was a higher proportion of blastocysts produced in 14-day interval group (28 vs. 6%, p<0.05). No blastocysts were produced from grade III and IV oocytes. This study indicates it is possible to use a 14-day interval for oocyte collection in water buffalo; this approach could be considered as an alternative when access to animals is restricted. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Interval of Uncertainty: An Alternative Approach for the Determination of Decision Thresholds, with an Illustrative Application for the Prediction of Prostate Cancer

    PubMed Central

    2016-01-01

    Often, for medical decisions based on test scores, a single decision threshold is determined and the test results are dichotomized into positive and negative diagnoses. It is therefore important to identify the decision threshold with the least number of misclassifications. The proposed method uses trichotomization: it defines an Uncertain Interval around the point of intersection between the two distributions of individuals with and without the targeted disease. In this Uncertain Interval the diagnoses are intermixed and the numbers of correct and incorrect diagnoses are (almost) equal. This Uncertain Interval is considered to be a range of test scores that is inconclusive and does not warrant a decision. It is expected that defining such an interval with some precision, prevents a relatively large number of false decisions, and therefore results in an increased accuracy or correct classifications rate (CCR) for the test scores outside this Uncertain Interval. Clinical data and simulation results confirm this. The results show that the CCR is systematically higher outside the Uncertain Interval when compared to the CCR of the decision threshold based on the maximized Youden index. For strong tests with a very small overlap between the two distributions, it can be difficult to determine an Uncertain Interval. In simulations, the comparison with an existing method for test-score trichotomization, the Two-graph Receiver Operating Characteristic (TG-ROC), showed smaller differences between the two distributions for the Uncertain Interval than for TG-ROC’s Intermediate Range and consequently a more improved CCR outside the Uncertain Interval. The main conclusion is that the Uncertain Interval method offers two advantages: 1. Identification of patients for whom the test results are inconclusive; 2. A higher estimated rate of correct decisions for the remaining patients. PMID:27829010

  16. Bio-predictive tablet disintegration: effect of water diffusivity, fluid flow, food composition and test conditions.

    PubMed

    Radwan, Asma; Wagner, Manfred; Amidon, Gordon L; Langguth, Peter

    2014-06-16

    Food intake may delay tablet disintegration. Current in vitro methods have little predictive potential to account for such effects. The effect of a variety of factors on the disintegration of immediate release tablets in the gastrointestinal tract has been identified. They include viscosity of the media, precipitation of food constituents on the surface of the tablet and reduction of water diffusivity in the media as well as changes in the hydrodynamics in the surrounding media of the solid dosage form. In order to improve the predictability of food affecting the disintegration of a dosage form, tablet disintegration in various types of a liquefied meal has been studied under static vs. dynamic (agitative) conditions. Viscosity, water diffusivity, osmolality and Reynolds numbers for the different media were characterized. A quantitative model is introduced which predicts the influence of the Reynolds number in the tablet disintegration apparatus on the disintegration time. Viscosity, water diffusivity and media flow velocity are shown to be important factors affecting dosage form disintegration. The results suggest the necessity of considering these parameters when designing a predictive model for simulating the in vivo conditions. Based on these experiments and knowledge on in vivo hydrodynamics in the GI tract, it is concluded that the disintegration tester under current pharmacopoeial conditions is operated in an unphysiological mode and no bioprediction may be derived. Recommendations regarding alternative mode of operation are made. Copyright © 2013 Elsevier B.V. All rights reserved.

  17. Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

    PubMed Central

    Bonino, Angela Yarnell; Leibold, Lori J.

    2017-01-01

    Collecting reliable behavioral data from toddlers and preschoolers is challenging. As a result, there are significant gaps in our understanding of human auditory development for these age groups. This paper describes an observer-based procedure for measuring hearing sensitivity with a two-interval, two-alternative forced-choice paradigm. Young children are trained to perform a play-based, motor response (e.g., putting a block in a bucket) whenever they hear a target signal. An experimenter observes the child's behavior and makes a judgment about whether the signal was presented during the first or second observation interval; the experimenter is blinded to the true signal interval, so this judgment is based solely on the child's behavior. These procedures were used to test 2 to 4 year-olds (n = 33) with no known hearing problems. The signal was a 1,000 Hz warble tone presented in quiet, and the signal level was adjusted to estimate a threshold corresponding to 71%-correct detection. A valid threshold was obtained for 82% of children. These results indicate that the two-interval procedure is both feasible and reliable for use with toddlers and preschoolers. The two-interval, observer-based procedure described in this paper is a powerful tool for evaluating hearing in young children because it guards against response bias on the part of the experimenter. PMID:28190058

  18. Predicting EMIC wave properties from ring current conditions observed by Van Allen Probes

    NASA Astrophysics Data System (ADS)

    Jordanova, V.; Fu, X.; Cowee, M.; Gary, S. P.; Winske, D.; Reeves, G. D.

    2016-12-01

    Recently, we have developed a model to predict amplitude and spectrum of the electromagnetic ion cyclotron (EMIC) wave from the anisotropic ring current ion distributions. The model is derived from linear theory and nonlinear hybrid simulations in a large parameter space for homogeneous plasmas consisting of electrons, protons, and helium ions. In this study we apply our model to selected EMIC events observed by Van Allen Probes satellites. Using the plasma conditions measured by the HOPE instrument, we predict EMIC properties and compare them to wave measurements by the EMFISIS instrument. Detailed analysis of these events will be shown.

  19. Defining boundary conditions for RANS predictions of urban flows using mesoscale simulations

    NASA Astrophysics Data System (ADS)

    Garcia Sanchez, Clara; Gorle, Catherine; van Beeck, Jeroen

    2015-11-01

    Pollutant dispersion and wind flows in urban canopies are major concerns for human health and energy, and the complex nature of the flow and transport processes remains a challenge when using Computational Fluid Dynamics (CFD) to predict wind flows. The definition of the inflow boundary condition in Reynolds-Averaged Navier-Stokes simulations (RANS) is one of the uncertainties that will strongly influence the prediction of the flow field, and thus, the dispersion pattern. The goal of the work presented is to define a methodology that improves the level of realism in the inflow condition for RANS simulations by accounting for larger mesoscale effects. The Weather Research and Forecasting model (WRF) is used to forecast mesoscale flow patterns, and two different approaches are used to define inflow conditions for the RANS simulations performed with OpenFOAM: 1) WRF variables such as local velocity magnitude, ABL height and friction velocity are directly interpolated onto the boundaries of the CFD domain; 2) WRF predictions for the geostrophic wind and friction velocity are applied as a forcing boundary condition. Simulations of the Joint Urban 2003 experimental campaign in Oklahoma City have been performed using both approaches and a comparison of the results will be presented.

  20. DFT-based prediction of fission product sorption on carbon structures under O2 ingress conditions

    NASA Astrophysics Data System (ADS)

    Londono-Hurtado, Alejandro; Szlufarska, Izabela; Morgan, Dane

    2013-06-01

    An isotherm based model for the prediction of Cs sorption on the carbon components of a High Temperature Reactor (HTR) under O2 ingress conditions is presented. Isotherms are derived from a thermodynamic model based on binding energies calculated using Density Functional Theory (DFT). The DFT derived isotherms are compared with isotherms obtained from experimental calculations and sources of discrepancies are discussed. A DFT only model and a second model combining DFT and experimental calculations are used to predict fission product inventories in a HTR vessel during O2 ingress conditions. Results suggest that the carbon type (i.e. graphitic vs. amorphous) plays a central role on fission product sorption and release. During normal reactor conditions (T around 1400 K, low P) graphitic carbon will absorb a small percentage of a monolayer of Cs, while amorphous carbon will be approximately saturated at an entire monolayer of Cs. Results also indicate that, for the case of O2 ingress to the reactor's vessel, the Cs will form Cs2O. In the case of graphitic carbon, the Cs2O will bind more weakly than Cs, leading to Cs release in the form of Cs2O during O ingress. However, the weak binding of Cs to graphite means that only small release is expected. In the case of amorphous carbon, Cs2O binds almost as strongly Cs, and so no significant change in Cs absorbed to the amorphous carbon is predicted, although the form of the absorbed Cs is predicted to be Cs2O. For the case of low release conditions, consistent with modern TRISO fuels, the core will adsorb the entire Cs inventory at normal operating temperatures. However, for high Cs release conditions, consistent with older TRISO fuels, the surface sites on the core will be saturated and most of the Cs will remain in gas form or plate out on other surfaces.

  1. Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslides susceptibility models

    NASA Astrophysics Data System (ADS)

    Pereira, S.; Zêzere, J. L.; Bateira, C.

    2012-04-01

    The aim of this study is to identify the landslide predisposing factors combination, using a bivariate statistical model that best predict landslide susceptibility. The best predictive model should have a good performance in terms of suitability and predictive power, and should be based on landslide predisposing factors that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km2) located in the Northern Portugal. Several destructive landslides occurred in this area in the last decades promoting landscape degradation and other negative human and economic impacts. A landslide inventory was built in 2005-2009 using aerial photo-interpretation (1/5.000 scale) and field work validation. This inventory contains 767 shallow translational slides. The landslide density is 11 events/square kilometre, and each landslide has, on average, 136 m2 and the depth of the slip surface typically ranges from 1 to 1.5 m. The landslide layer was crossed individually with seven landslide predisposing factors (Aspect; Curvature; Slope Angle; Geomorphological Units; Land Use; Inverse Wetness Index; Lithology) and each class within each predisposing theme was weighted using the Information Value Method. In order to identify the best combination of landslide predisposing factors, all possible combinations were tested which resulted in 120 predictive models. The goodness of fit of each landslide susceptibility model was evaluated by constructing the Success Rate Curves and by computing the Area Under the Curve (AUC). The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. Two tests were performed to the entire dataset to assess conditional independence: the Overall Conditional Independence (OCI) and the Agterberg & Cheng Conditional Independence Test (ACCIT) (Agterberg and Cheng, 2002). The best landslide susceptibility model was constructed with only three

  2. Prediction suppression in monkey inferotemporal cortex depends on the conditional probability between images.

    PubMed

    Ramachandran, Suchitra; Meyer, Travis; Olson, Carl R

    2016-01-01

    When monkeys view two images in fixed sequence repeatedly over days and weeks, neurons in area TE of the inferotemporal cortex come to exhibit prediction suppression. The trailing image elicits only a weak response when presented following the leading image that preceded it during training. Induction of prediction suppression might depend either on the contiguity of the images, as determined by their co-occurrence and captured in the measure of joint probability P(A,B), or on their contingency, as determined by their correlation and as captured in the measures of conditional probability P(A|B) and P(B|A). To distinguish between these possibilities, we measured prediction suppression after imposing training regimens that held P(A,B) constant but varied P(A|B) and P(B|A). We found that reducing either P(A|B) or P(B|A) during training attenuated prediction suppression as measured during subsequent testing. We conclude that prediction suppression depends on contingency, as embodied in the predictive relations between the images, and not just on contiguity, as embodied in their co-occurrence.

  3. Testing of the hydromechanical prediction model of soil erosion under the conditions of Georgia

    NASA Astrophysics Data System (ADS)

    Gogichaishvili, G. P.; Kirvalidze, D. R.; Gorjomeladze, O. L.

    2014-09-01

    A hydromechanical model for predicting water (rain-induced) soil erosion was tested on the experimental plots of the Research Institute of Tea and Subtropical Crops in Zendidi village (the Ajara Autonomous Republic) and the Sabashvili Institute of Soil Science, Agrochemistry, and Melioration in Khevi and Kitskhi villages (Upper Imeretia, Western Georgia). A comparison of factual and predicted values of rain-induced erosion for the plots with permanent black fallow showed that the model overestimated the average annual soil loss for the yellow-brown strongly eroded soil in Zendidi village by 23.22 t/ha (133%). This value ranged in different years from 18 to 1052%. For the plots with corn, the predicted value of annual erosion was by 16.94 t/ha higher than the factual value (overestimation of 488%). A comparison of factual and predicted values of rainfall erosion for the plots under sprinkling irrigation also showed that the predicted soil loss was higher than the factual one by 4.14-30.40 t/ha for corn, 6.76-11.14 t/ha for winter wheat, and 15.75-24.12 t/ha for the plots with stubble of winter wheat and barley. Thus, the hydromechanical model for predicting water erosion inadequately describes it under the conditions of Western Georgia and has to be refined.

  4. Standard measures of visual acuity do not predict drivers' recognition performance under day or night conditions.

    PubMed

    Wood, Joanne M; Owens, D Alfred

    2005-08-01

    This study investigated whether visual acuity or contrast sensitivity, measured under a range of luminance conditions, could predict drivers' recognition performance under real-world day and night road conditions. Twenty-four participants, comprising three age groups (younger, mean = 21.5 years; middle-aged, mean = 46.6 years; and older, mean = 71.9 years), drove around a 1.8-km closed road circuit under day and nighttime conditions. At night, headlight intensity was varied over 1.5 log-units by ND filters mounted on the headlights. Participants drove around the circuit under five light conditions (daytime and four at night) and were asked to report relevant targets, including road signs, large low-contrast road obstacles, and pedestrians who wore retroreflective markings on either the torso or the limb joints (creating "biological motion"). Real-world recognition performance was measured as percent correct recognition and, in the case of low-contrast road obstacles, avoided. Clinical vision tests included high-contrast visual acuity and Pelli-Robson letter contrast sensitivity measured at four luminance levels. Real-world recognition performance of all age groups was significantly degraded under low light conditions, and this impairment was greater for the older participants. These changes in drivers' recognition performance were more strongly predicted by contrast sensitivity than visual acuity measured under standard photopic conditions. Interestingly, contrast sensitivity was highly correlated with visual acuity measured under low-luminance conditions. Further analyses showed that recognition performance while driving is better predicted by combinations of two tests: either 1) photopic visual acuity and photopic contrast sensitivity, or 2) photopic and mesopic visual acuity. These findings confirm that visibility is seriously degraded during night driving and that the problem is greater for older drivers. These changes in real-world recognition performance were

  5. Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions

    PubMed Central

    Ma, Jianfen; Hu, Yi; Loizou, Philipos C.

    2009-01-01

    The articulation index (AI), speech-transmission index (STI), and coherence-based intelligibility metrics have been evaluated primarily in steady-state noisy conditions and have not been tested extensively in fluctuating noise conditions. The aim of the present work is to evaluate the performance of new speech-based STI measures, modified coherence-based measures, and AI-based measures operating on short-term (30 ms) intervals in realistic noisy conditions. Much emphasis is placed on the design of new band-importance weighting functions which can be used in situations wherein speech is corrupted by fluctuating maskers. The proposed measures were evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech (consonants and sentences) corrupted by four different maskers (car, babble, train, and street interferences). Of all the measures considered, the modified coherence-based measures and speech-based STI measures incorporating signal-specific band-importance functions yielded the highest correlations (r=0.89–0.94). The modified coherence measure, in particular, that only included vowel∕consonant transitions and weak consonant information yielded the highest correlation (r=0.94) with sentence recognition scores. The results from this study clearly suggest that the traditional AI and STI indices could benefit from the use of the proposed signal- and segment-dependent band-importance functions. PMID:19425678

  6. Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions.

    PubMed

    Ma, Jianfen; Hu, Yi; Loizou, Philipos C

    2009-05-01

    The articulation index (AI), speech-transmission index (STI), and coherence-based intelligibility metrics have been evaluated primarily in steady-state noisy conditions and have not been tested extensively in fluctuating noise conditions. The aim of the present work is to evaluate the performance of new speech-based STI measures, modified coherence-based measures, and AI-based measures operating on short-term (30 ms) intervals in realistic noisy conditions. Much emphasis is placed on the design of new band-importance weighting functions which can be used in situations wherein speech is corrupted by fluctuating maskers. The proposed measures were evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech (consonants and sentences) corrupted by four different maskers (car, babble, train, and street interferences). Of all the measures considered, the modified coherence-based measures and speech-based STI measures incorporating signal-specific band-importance functions yielded the highest correlations (r=0.89-0.94). The modified coherence measure, in particular, that only included vowel/consonant transitions and weak consonant information yielded the highest correlation (r=0.94) with sentence recognition scores. The results from this study clearly suggest that the traditional AI and STI indices could benefit from the use of the proposed signal- and segment-dependent band-importance functions.

  7. Predictions of structural integrity of steam generator tubes under normal operating, accident, and severe accident conditions

    SciTech Connect

    Majumdar, S.

    1996-09-01

    Available models for predicting failure of flawed and unflawed steam generator tubes under normal operating, accident, and severe accident conditions are reviewed. Tests conducted in the past, though limited, tended to show that the earlier flow-stress model for part-through-wall axial cracks overestimated the damaging influence of deep cracks. This observation is confirmed by further tests at high temperatures as well as by finite element analysis. A modified correlation for deep cracks can correct this shortcoming of the model. Recent tests have shown that lateral restraint can significantly increase the failure pressure of tubes with unsymmetrical circumferential cracks. This observation is confirmed by finite element analysis. The rate-independent flow stress models that are successful at low temperatures cannot predict the rate sensitive failure behavior of steam generator tubes at high temperatures. Therefore, a creep rupture model for predicting failure is developed and validated by tests under varying temperature and pressure loading expected during severe accidents.

  8. Correlate Life Predictions and Condition Indicators in Helicopter Tail Gearbox Bearings

    NASA Technical Reports Server (NTRS)

    Dempsey, Paula J.; Bolander, Nathan; Haynes, Chris; Branning, Jeremy; Wade, Daniel R.

    2010-01-01

    Research to correlate bearing remaining useful life (RUL) predictions with Helicopter Health Usage Monitoring Systems (HUMS) condition indicators (CI) to indicate the damage state of a transmission component has been developed. Condition indicators were monitored and recorded on UH-60M (Black Hawk) tail gearbox output shaft thrust bearings, which had been removed from helicopters and installed in a bearing spall propagation test rig. Condition indicators monitoring the tail gearbox output shaft thrust bearings in UH-60M helicopters were also recorded from an on-board HUMS. The spal-lpropagation data collected in the test rig was used to generate condition indicators for bearing fault detection. A damage progression model was also developed from this data. Determining the RUL of this component in a helicopter requires the CI response to be mapped to the damage state. The data from helicopters and a test rig were analyzed to determine if bearing remaining useful life predictions could be correlated with HUMS condition indicators (CI). Results indicate data fusion analysis techniques can be used to map the CI response to the damage levels.

  9. Correlate Life Predictions and Condition Indicators in Helicopter Tail Gearbox Bearings

    NASA Technical Reports Server (NTRS)

    Dempsey, Paula J.; Bolander, Nathan; Haynes, Chris; Branning, Jeremy; Wade, Daniel R.

    2010-01-01

    Research to correlate bearing remaining useful life (RUL) predictions with Helicopter Health Usage Monitoring Systems (HUMS) condition indicators (CI) to indicate the damage state of a transmission component has been developed. Condition indicators were monitored and recorded on UH-60M (Black Hawk) tail gearbox output shaft thrust bearings, which had been removed from helicopters and installed in a bearing spall propagation test rig. Condition indicators monitoring the tail gearbox output shaft thrust bearings in UH-60M helicopters were also recorded from an on-board HUMS. The spal-lpropagation data collected in the test rig was used to generate condition indicators for bearing fault detection. A damage progression model was also developed from this data. Determining the RUL of this component in a helicopter requires the CI response to be mapped to the damage state. The data from helicopters and a test rig were analyzed to determine if bearing remaining useful life predictions could be correlated with HUMS condition indicators (CI). Results indicate data fusion analysis techniques can be used to map the CI response to the damage levels.

  10. Novel mathematical model for predicting the dissolution profile of spherical particles under non-sink conditions.

    PubMed

    Agata, Yasuyoshi; Iwao, Yasunori; Miyagishima, Atsuo; Itai, Shigeru

    2010-04-01

    A mechanistic mathematical model was designed to predict dissolution patterns under non-sink conditions. Sulfamethoxazole was used as a model drug, and its physico-chemical properties such as solubility, density, and intrinsic dissolution rate constant etc., were investigated in order to apply these experimental values to the proposed model. Dissolution tests were employed as a way of validating the mathematical model, and it was found that the predictions given by the model were surprisingly accurate for all particle sizes. In addition, a simulation focused on forecasting the fraction of the drug that was dissolved at a certain time point when various initial particle diameters were used was also particularly valuable. Therefore, these results demonstrated that the model enables dissolution profiles to be analyzed under non-sink conditions.

  11. Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions.

    PubMed

    Kleandrova, Valeria V; Luan, Feng; González-Díaz, Humberto; Ruso, Juan M; Melo, André; Speck-Planche, Alejandro; Cordeiro, M Natália D S

    2014-12-01

    Nanotechnology has brought great advances to many fields of modern science. A manifold of applications of nanoparticles have been found due to their interesting optical, electrical, and biological/chemical properties. However, the potential toxic effects of nanoparticles to different ecosystems are of special concern nowadays. Despite the efforts of the scientific community, the mechanisms of toxicity of nanoparticles are still poorly understood. Quantitative-structure activity/toxicity relationships (QSAR/QSTR) models have just started being useful computational tools for the assessment of toxic effects of nanomaterials. But most QSAR/QSTR models have been applied so far to predict ecotoxicity against only one organism/bio-indicator such as Daphnia magna. This prevents having a deeper knowledge about the real ecotoxic effects of nanoparticles, and consequently, there is no possibility to establish an efficient risk assessment of nanomaterials in the environment. In this work, a perturbation model for nano-QSAR problems is introduced with the aim of simultaneously predicting the ecotoxicity of different nanoparticles against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles. The QSAR-perturbation model was derived from a database containing 5520 cases (nanoparticle-nanoparticle pairs), and it was shown to exhibit accuracies of ca. 99% in both training and prediction sets. In order to demonstrate the practical applicability of our model, three different nickel-based nanoparticles (Ni) with experimental values reported in the literature were predicted. The predictions were found to be in very good agreement with the experimental evidences, confirming that Ni-nanoparticles are not ecotoxic when compared with other nanoparticles. The results

  12. Ongoing development of a computer jobstream to predict helicopter main rotor performance in icing conditions

    NASA Technical Reports Server (NTRS)

    Britton, Randall K.

    1991-01-01

    Work is currently underway at the NASA Lewis Research Center to develop an analytical method for predicting the performance degradation of a helicopter operating in icing conditions. A brief survey is performed of possibilities available to perform such a calculation along with the reasons for choosing the present approach. A complete description of the proposed jobstream is given as well as a discussion of the present state of the development.

  13. Lipari Landfill Piping Network Corrosion Condition Assessment and Service Life Prediction Analysis

    DTIC Science & Technology

    2008-12-01

    crevice or erosion corrosion . 3.4.2 Data The valve body containing the corrosion coupon was placed into the west header on August 4, 2007. Figure...value statistical analysis of maximum pit depths and time to first perforation. Corrosion : 83–87. Houston, TX: NACE International. Gumbel , E.J...ER D C/ CE R L TR -0 8 -2 1 Lipari Landfill Piping Network Corrosion Condition Assessment and Service Life Prediction Analysis Charles

  14. Relation of weather forecasts to the prediction of dangerous forest fire conditions

    Treesearch

    R. H. Weidman

    1923-01-01

    The purpose of predicting dangerous forest-fire conditions, of course, is to reduce the great cost and damage caused by forest fires. In the region of Montana and northern Idaho alone the average cost to the United States Forest Service of fire protection and suppression is over $1,000,000 a year. Although the causes of forest fires will gradually be reduced by...

  15. Prediction of fluid forces acting on a hand model in unsteady flow conditions.

    PubMed

    Kudo, Shigetada; Yanai, Toshimasa; Wilson, Barry; Takagi, Hideki; Vennell, Ross

    2008-01-01

    The aim of this study was to develop a method to predict fluid forces acting on the human hand in unsteady flow swimming conditions. A mechanical system consisting of a pulley and chain mechanism and load cell was constructed to rotate a hand model in fluid flows. To measure the angular displacement of the hand model a potentiometer was attached to the axis of the rotation. The hand model was then fixed at various angles about the longitudinal axis of the hand model and rotated at different flow velocities in a swimming flume for 258 different trials to approximate a swimmer's stroke in unsteady flow conditions. Pressures were taken from 12 transducers embedded in the hand model at a sampling frequency of 200Hz. The resultant fluid force acting on the hand model was then determined on the basis of the kinetic and kinematic data taken from the mechanical system at the frequency of 200Hz. A stepwise regression analysis was applied to acquire higher order polynomial equations that predict the fluid force acting on the accelerating hand model from the 12 pressure values. The root mean square (RMS) difference between the resultant fluid force measured and that predicted from the single best-fit polynomial equation across all trials was 5N. The method developed in the present study accurately predicted the fluid forces acting on the hand model.

  16. Sign-Tracking to an Appetitive Cue Predicts Incubation of Conditioned Fear in Rats

    PubMed Central

    Morrow, Jonathan D.; Saunders, Benjamin T.; Maren, Stephen; Robinson, Terry E.

    2014-01-01

    Although post-traumatic stress disorder (PTSD) and addiction are very different disorders, both are characterized by hyperreactivity to trauma- or drug-related cues, respectively. We investigated whether an appetitive conditioning task, Pavlovian conditioned approach, which predicts vulnerability to reinstatement of cocaine-seeking, also predicts fear incubation, which may be a marker for vulnerability to PTSD. We classified rats based on whether they learned to approach and interact with a food predictive cue (sign-trackers), or, whether upon cue presentation they went to the location of impending food delivery (goal-trackers). Rats were then exposed to extensive Pavlovian tone-shock pairings, which causes the fear response to increase or “incubate” over time. We found that the fear incubation effect was only present in sign-trackers. The behavior of goal-trackers was more consistent with a normal fear response – it was most robust immediately after training and decayed slowly over time. Sign-trackers also had lower levels of brain-derived neurotrophic factor (BDNF) protein in the prefrontal cortex than goal-trackers. These results indicate that, while many factors likely contribute to the disproportionate co-occurrence of PTSD and substance abuse, one such factor may be a core psychological trait that biases some individuals to attribute excessive motivational significance to predictive cues, regardless of the emotional valence of those cues. High levels of BDNF in the prefrontal cortex may be protective against developing excessive emotional and motivational responses to salient cues. PMID:24747659

  17. Predictive statistical models linking antecedent meteorological conditions and waterway bacterial contamination in urban waterways.

    PubMed

    Farnham, David J; Lall, Upmanu

    2015-06-01

    Although the relationships between meteorological conditions and waterway bacterial contamination are being better understood, statistical models capable of fully leveraging these links have not been developed for highly urbanized settings. We present a hierarchical Bayesian regression model for predicting transient fecal indicator bacteria contamination episodes in urban waterways. Canals, creeks, and rivers of the New York City harbor system are used to examine the model. The model configuration facilitates the hierarchical structure of the underlying system with weekly observations nested within sampling sites, which in turn were nested inside of the harbor network. Models are compared using cross-validation and a variety of Bayesian and classical model fit statistics. The uncertainty of predicted enterococci concentration values is reflected by sampling from the posterior predictive distribution. Issuing predictions with the uncertainty reasonably reflected allows a water manager or a monitoring agency to issue warnings that better reflect the underlying risk of exposure. A model using only antecedent meteorological conditions is shown to correctly classify safe and unsafe levels of enterococci with good accuracy. The hierarchical Bayesian regression approach is most valuable where transient fecal indicator bacteria contamination is problematic and drainage network data are scarce.

  18. Impact of soil moisture initial conditions on multi model summer predictions over mid-latitudes

    NASA Astrophysics Data System (ADS)

    Ardilouze, Constantin; Prodhomme, Chloé; Batté, Lauriane; Déqué, Michel

    2016-04-01

    Land surface initial conditions have been recognized as a potential source of predictability at seasonal time scales. As an example, results from GLACE-2 (phase 2 of the Global Land-Atmosphere Coupling Experiment) highlighted the impact of spring soil moisture in summer near-surface air temperature prediction over Europe and Northern America with global long-range forecast systems (Koster et al., 2011, van den Hurk et al.,2012). Yet, few studies have explored such an influence over a sufficient hindcast period to produce a robust quantitative assessment. In the framework of the FP7-SPECS project, dedicated experiments have been carried out with June-August hindcasts from 5 distinct Atmosphere Ocean Global Climate Models initialized either by realistic or climatological soil moisture conditions on May 1st. Realistic initialization leads to an improved 2-meter temperature prediction skill over parts of Europe in the multi model, particularly the Balkans peninsula which had been identified as a hot spot of soil moisture-atmosphere coupling (Seneviratne et al. 2006) However no improvement was found over North-American Great Plains in spite of the high potential of this region. Further analyses suggest that this lack of skill stems from a common shortcoming of the models. All of them tend to overestimate the positive feedback between soil moisture, temperature and precipitation with respect to the observations. Hence, tackling model systematic biases over the US Southern Great Plains appears as a necessary prerequisite for summer predictability enhancement.

  19. Conditional random field approach to prediction of protein-protein interactions using domain information.

    PubMed

    Hayashida, Morihiro; Kamada, Mayumi; Song, Jiangning; Akutsu, Tatsuya

    2011-06-20

    For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions.

  20. Conditioning rainfall-runoff model parameters to reduce prediction uncertainty in ungauged basins

    NASA Astrophysics Data System (ADS)

    Visessri, S.; McIntyre, N.; Maksimovic, C.

    2012-12-01

    Conditioning rainfall-runoff model parameters in ungauged catchments in Thailand presents problems common to ungauged basins involving data availability, data quality, and rainfall-runoff model suitability, which all contribute to prediction uncertainty. This paper attempts to improve the estimation of streamflow in ungauged basins and reduce associated uncertainties using the approaches of conditioning the prior parameter space. 35 catchments from the upper Ping River basin, Thailand are selected as a case study. The catchments have a range of attributes e.g. catchment sizes 20-6350 km2, elevations 632-1529 m above sea level. and annual rainfall 846-1447 mm/year. For each catchment, three indices - rainfall-runoff elasticity, base flow index and runoff coefficient - are calculated using the observed rainfall-runoff data and regression equations relating these indices to the catchment attributes are identified. Uncertainty in expected indices is defined by the regression error distribution, approximated by a Gaussian model. The IHACRES model is applied for simulating streamflow. The IHACRES parameters are randomly sampled from their presumed prior parameter space. For each sampled parameter set, the streamflow and hence the three indices are modelled. The parameter sets are conditioned on the probability distributions of the regionalised indices, allowing ensemble predictions to be made. The objective function, NSE, calculated for daily and weekly time steps from the water years 1995-2000, is used to assess model performance. Ability to capture observed streamflow and the precision of the estimate is evaluated using reliability and sharpness measures. Similarity in modelled and expected indices contributes to good objective function values. Using only the regionalised runoff coefficient to condition the model yields better NSE values compared to using either only the rainfall-runoff elasticity or only the base flow index. Conditioning on the runoff coefficient

  1. Importance of initial conditions in seasonal predictions of Arctic sea ice extent

    NASA Astrophysics Data System (ADS)

    Msadek, R.; Vecchi, G. A.; Winton, M.; Gudgel, R. G.

    2014-07-01

    We present seasonal predictions of Arctic sea ice extent (SIE) over the 1982-2013 period using two suites of retrospective forecasts initialized from a fully coupled ocean-atmosphere-sea ice assimilation system. High skill scores are found in predicting year-to-year fluctuations of Arctic SIE, with significant correlations up to 7 month ahead for September detrended anomalies. Predictions over the recent era, which coincides with an improved observational coverage, outperform the earlier period for most target months. We find, however, a degradation of skill in September during the last decade, a period of sea ice thinning in observations. The two prediction models, Climate Model version 2.1 (CM2.1) and Forecast-oriented Low Ocean Resolution (FLOR), share very similar ocean and ice component and initialization but differ by their atmospheric component. FLOR has improved climatological atmospheric circulation and sea ice mean state, but its skill is overall similar to CM2.1 for most seasons, which suggests a key role for initial conditions in predicting seasonal SIE fluctuations.

  2. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions.

    PubMed

    Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon; Gaydon, Donald; Marcaida, Manuel; Nakagawa, Hiroshi; Oriol, Philippe; Ruane, Alex C; Ruget, Françoise; Singh, Balwinder-; Singh, Upendra; Tang, Liang; Tao, Fulu; Wilkens, Paul; Yoshida, Hiroe; Zhang, Zhao; Bouman, Bas

    2015-03-01

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2 ] and temperature.

  3. Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

    NASA Technical Reports Server (NTRS)

    Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon; hide

    2014-01-01

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.

  4. Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

    NASA Technical Reports Server (NTRS)

    Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon; Gaydon, Donald; Marcaida, Manuel, III; Nakagawa, Hiroshi; Oriol, Philippe; Ruane, Alex C.; Ruget, Francoise; Singh, Balwinder; Singh, Upendra; Tang, Liang; Tao, Fulu; Wilkens, Paul; Yoshida, Hiroe; Zhang, Zhao; Bouman, Bas

    2014-01-01

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.

  5. Assessing the Value of the Zebrafish Conditioned Place Preference Model for Predicting Human Abuse Potential.

    PubMed

    Brock, A J; Goody, S M G; Mead, A N; Sudwarts, A; Parker, M O; Brennan, C H

    2017-10-01

    Regulatory agencies recommend that centrally active drugs are tested for abuse potential before approval. Standard preclinical assessments are conducted in rats or non-human primates (NHPs). This study evaluated the ability of the zebrafish conditioned place preference (CPP) model to predict human abuse outcomes. Twenty-seven compounds from a variety of pharmacological classes were tested in zebrafish CPP, categorized as positive or negative, and analyzed using standard diagnostic tests of binary classification to determine the likelihood that zebrafish correctly predict robust positive signals in human subjective effects studies (+HSE) and/or Drug Enforcement Administration drug scheduling. Results were then compared with those generated for rat self-administration and CPP, as well as NHP self-administration, using this same set of compounds. The findings reveal that zebrafish concordance and sensitivity values were not significantly different from chance for both +HSE and scheduling. Although significant improvements in specificity and negative predictive values were observed for zebrafish relative to +HSE, specificity without sensitivity provides limited predictive value. Moreover, assessments in zebrafish provided no added value for predicting scheduling. By contrast, rat and NHP models generally possessed significantly improved concordance, sensitivity, and positive predictive values for both clinical measures. Although there may be predictive value with compounds from specific pharmacological classes (e.g., µ-opioid receptor agonists, psychostimulants) for zebrafish CPP, altogether these data highlight that using the current methodology, the zebrafish CPP model does not add value to the preclinical assessment of abuse potential. Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

  6. Predicting Inner Heliospheric Solar Wind Conditions in Advance of Solar Probe Plus

    NASA Astrophysics Data System (ADS)

    Case, A. W.; Kasper, J. C.; Korreck, K. E.; Stevens, M. L.; Cohen, O.; Salem, C. S.; Halekas, J. S.; Larson, D. E.; Maruca, B. A.

    2012-12-01

    In advance of the upcoming inner heliospheric missions (Solar Orbiter and Solar Probe Plus) it is vital to have an accurate prediction of the range of solar wind conditions that occur between 9.5Rs and 0.7AU. These conditions will place constraints on instrument design and the operational modes that are used. In this paper, we discuss and compare different methods of predicting the solar wind bulk plasma parameters. One method uses observed 1AU conditions observed with the Wind spacecraft combined with scaling laws derived from Helios observations. We extend this simple model by using a more realistic solar wind velocity profile in addition to the Wind and Helios observations. Another method uses 3D MHD simulations from which solar wind conditions along a spacecraft trajectory can be extracted. We discuss some implications of these models in the design of the Solar Wind Electrons Alphas and Protons investigation, a suite of solar wind instruments being designed to fly on Solar Probe Plus.

  7. Maternal natal environment and breeding territory predict the condition and sex ratio of offspring.

    PubMed

    Bowers, E Keith; Thompson, Charles F; Sakaluk, Scott K

    2017-03-01

    Females in a variety of taxa adjust offspring sex ratios to prevailing ecological conditions. However, little is known about whether conditions experienced during a female's early ontogeny influence the sex ratio of her offspring. We tested for past and present ecological predictors of offspring sex ratios among known-age females that were produced as offspring and bred as adults in a population of house wrens. The body condition of offspring that a female produced and the proportion of her offspring that were male were negatively correlated with the size of the brood in which she herself was reared. The proportion of sons within broods was negatively correlated with maternal hatching date, and varied positively with the quality of a female's current breeding territory as predicted. However, females producing relatively more sons than daughters were less likely to return to breed in the population the following year. Although correlative, our results suggest that the rearing environment can have enduring effects on later maternal investment and sex allocation. Moreover, the overproduction of sons relative to daughters may increase costs to a female's residual reproductive value, constraining the extent to which sons might be produced in high-quality breeding conditions. Sex allocation in birds remains a contentious subject, largely because effects on offspring sex ratios are small. Our results suggest that offspring sex ratios are shaped by various processes and trade-offs that act throughout the female life history and ultimately reduce the extent of sex-ratio adjustment relative to classic theoretical predictions.

  8. Photovoltaic power generation for air-conditioning system based on predictive control

    SciTech Connect

    Kim, S.; Choi, J.; Park, G.; Yoo Jiyoon

    1995-12-31

    In this paper an auxiliary power supply scheme using photovoltaic power generation for air-conditioning system and its novel control strategy are proposed. The proposed auxiliary power supply system employs a boost converter, a bidirectional power converter and photovoltaic arrays. The boost converter controlled by a predictive control strategy provides maximum power track (MPT) state on the photovoltaic (PV) arrays as well as power generation facility function on the ac utility grid. Furthermore the bidirectional power converter controls the power flow balance between the loads and two different power sources according to the condition of the load power and the supplied power from photovoltaic arrays. It is shown that the maximum power tracking of the PV arrays, the unit power factor of ac utility grid and the descent input dc voltage regulation of the air-conditioning system are achieved by the proposed predictive control strategy. The proposed switching strategy for the boost converter and the bidirectional power converter are based on the predictive control with ac line current and output voltage of the PV arrays. The bidirectional power converter is suitably modulation controlled to rectify the ac source during the power shortage under the poor power generation of PV arrays or over load conditions of air conditioner. During the opposite state, the bidirectional power converter is gated to function as a regeneration inverter. Controller design procedure for the proposed approach to achieve near sinusoidal input currents under the inverter mode and the rectifier mode is detailed. Simulation results on a laboratory prototype system are discussed. Experimental results from the laboratory prototype system will be presented in the near future.

  9. Predicting Failure Under Laboratory Conditions: Learning the Physics of Slow Frictional Slip and Dynamic Failure

    NASA Astrophysics Data System (ADS)

    Rouet-Leduc, B.; Hulbert, C.; Riviere, J.; Lubbers, N.; Barros, K.; Marone, C.; Johnson, P. A.

    2016-12-01

    Forecasting failure is a primary goal in diverse domains that include earthquake physics, materials science, nondestructive evaluation of materials and other engineering applications. Due to the highly complex physics of material failure and limitations on gathering data in the failure nucleation zone, this goal has often appeared out of reach; however, recent advances in instrumentation sensitivity, instrument density and data analysis show promise toward forecasting failure times. Here, we show that we can predict frictional failure times of both slow and fast stick slip failure events in the laboratory. This advance is made possible by applying a machine learning approach known as Random Forests1(RF) to the continuous acoustic emission (AE) time series recorded by detectors located on the fault blocks. The RF is trained using a large number of statistical features derived from the AE time series signal. The model is then applied to data not previously analyzed. Remarkably, we find that the RF method predicts upcoming failure time far in advance of a stick slip event, based only on a short time window of data. Further, the algorithm accurately predicts the time of the beginning and end of the next slip event. The predicted time improves as failure is approached, as other data features add to prediction. Our results show robust predictions of slow and dynamic failure based on acoustic emissions from the fault zone throughout the laboratory seismic cycle. The predictions are based on previously unidentified tremor-like acoustic signals that occur during stress build up and the onset of macroscopic frictional weakening. We suggest that the tremor-like signals carry information about fault zone processes and allow precise predictions of failure at any time in the slow slip or stick slip cycle2. If the laboratory experiments represent Earth frictional conditions, it could well be that signals are being missed that contain highly useful predictive information. 1Breiman

  10. Weighted regression analysis and interval estimators

    Treesearch

    Donald W. Seegrist

    1974-01-01

    A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.

  11. Efficient Prediction of Helicopter BVI Noise under Different Conditions of Wake and Blade Deformation

    NASA Astrophysics Data System (ADS)

    Inada, Yoshinobu; Yang, Choongmo; Iwanaga, Noriki; Aoyama, Takashi

    Predictions of helicopter BVI noise using three-dimensional Euler code with a single blade grid are conducted under three different conditions: BVI noise caused by (1) interaction between rotating blades and vortex shed from fixed wing vortex generator, (2) interaction between rotating blades and tip vortices shed from preceding blades, and (3) interaction between rotating blades with elastic deformation and shed tip vortices. In the CFD calculation, the Field Velocity Approach (FVA) and Scully’s vortex model are used to import the wake information into the calculation grid and to determine the induced velocity made by tip vortices, respectively (cases 1 3). Beddoes generalized wake model is used to prescribe the tip vortices position in the wake (cases 2 and 3). Information about blade elastic deformation is imported from HART II project experimental data into the calculation (case 3). Acoustic analyses based on Ffowcs-Williams and Hawkings (FW-H) equation are conducted subsequently in each case. The results from the calculations show good agreement with experiments in all three cases, indicating that application of FVA, Scully’s model, and Beddoes generalized wake model is effective for BVI noise prediction in this study, which is intended for low calculation cost using a single blade grid. Also, use of blade elastic deformation data in the calculation shows marked improvement in calculation precision. Consequently, the method used in this study can predict BVI noise under various conditions of wake or blade deformation with acceptable precision and low calculation cost.

  12. Assessment of Kinematic Brain Injury Metrics for Predicting Strain Responses in Diverse Automotive Impact Conditions.

    PubMed

    Gabler, Lee F; Crandall, Jeff R; Panzer, Matthew B

    2016-12-01

    Numerous injury criteria have been developed to predict brain injury using the kinematic response of the head during impact. Each criterion utilizes a metric that is some mathematical combination of the velocity and/or acceleration components of translational and/or rotational head motion. Early metrics were based on linear acceleration of the head, but recent injury criteria have shifted towards rotational-based metrics. Currently, there is no universally accepted metric that is suitable for a diverse range of head impacts. In this study, we assessed the capability of fifteen existing kinematic-based metrics for predicting strain-based brain response using four different automotive impact conditions. Tissue-level strains were obtained through finite element model simulation of 660 head impacts including occupant and pedestrian crash tests, and pendulum head impacts. Correlations between head kinematic metrics and predicted brain strain-based metrics were evaluated. Correlations between brain strain and metrics based on angular velocity were highest among those evaluated, while metrics based on linear acceleration were least correlative. BrIC and RVCI were the kinematic metrics with the highest overall correlation; however, each metric had limitations in certain impact conditions. The results of this study suggest that rotational head kinematics are the most important parameters for brain injury criteria.

  13. Prediction of Hydrolysis Products of Organic Chemicals under Environmental pH Conditions.

    PubMed

    Tebes-Stevens, Caroline; Patel, Jay M; Jones, W Jack; Weber, Eric J

    2017-05-02

    Cheminformatics-based software tools can predict the molecular structure of transformation products using a library of transformation reaction schemes. This paper presents the development of such a library for abiotic hydrolysis of organic chemicals under environmentally relevant conditions. The hydrolysis reaction schemes in the library encode the process science gathered from peer-reviewed literature and regulatory reports. Each scheme has been ranked on a scale of one to six based on the median half-life in a data set compiled from literature-reported hydrolysis rates. These ranks are used to predict the most likely transformation route when more than one structural fragment susceptible to hydrolysis is present in a molecule of interest. Separate rank assignments are established for pH 5, 7, and 9 to represent standard conditions in hydrolysis studies required for registration of pesticides in Organisation for Economic Co-operation and Development (OECD) member countries. The library is applied to predict the likely hydrolytic transformation products for two lists of chemicals, one representative of chemicals used in commerce and the other specific to pesticides, to evaluate which hydrolysis reaction pathways are most likely to be relevant for organic chemicals found in the natural environment.

  14. Adapting SOYGRO V5.42 for prediction under climate change conditions

    SciTech Connect

    Pickering, N.B.; Jones, J.W.; Boote, K.J.

    1995-12-31

    In some studies of the impacts of climate change on global crop production, crop growth models were empirically adapted to improve their response to increased CO{sub 2} concentration and air temperature. This chapter evaluates the empirical adaptations of the photosynthesis and evapotranspiration (ET) algorithms used in the soybean [Glycine max (L.) Merr.] model, SOYGRO V5.42, by comparing it with a new model that includes mechanistic approaches for these two processes. The new evapotranspiration-photosynthesis sub-model (ETPHOT) uses a hedgerow light interception algorithm, a C{sub 3}-leaf biochemical photosynthesis submodel, and predicts canopy ET and temperatures using a three-zone energy balance. ETPHOT uses daily weather data, has an internal hourly time step, and sums hourly predictions to obtain daily gross photosynthesis and ET. The empirical ET and photosynthesis curves included in SOYGRO V5.42 for climate change prediction were similar to those predicted by the ETPHOT model. Under extreme conditions that promote high leaf temperatures, like in the humid tropics. SOYGRO V5.42 overestimated daily gross photosynthesis response to CO{sub 2} compared with the ETPHOT model. SOYGRO V5.42 also slightly overestimated daily gross photosynthesis at intermediate air temperatures and ambient CO{sub 2} concentrations. 80 refs., 12 figs.

  15. Prediction of optimal conditions for verbal-communication quality in eating establishments.

    PubMed

    Nahid, Musarrat; Hodgson, Murray

    2011-04-01

    This paper discusses the prediction of verbal-communication quality in eating establishments (EEs). EEs contain talkers and listeners who require high speech intelligibility at their tables, and high speech privacy between tables. Using catt-Acoustic, verbal-communication quality--quantified by speech transmission index (STI)--in models of three existing EEs was predicted. Talker voice-output levels were predicted using an existing empirical model accounting for the Lombard effect. With these, catt-Acoustic predicted impulse responses, speech levels and noise levels at primary and secondary listener positions, and the corresponding STIs. The untreated EEs were first modeled for various talker and listener positions, and occupancies. Then various treated configurations, involving reduced volume, increased absorption and barriers were studied to determine the effectiveness of the treatments. The results suggest that placing barriers around tables can be an effective way to achieve good verbal-communication quality. Increasing the absorption of the room surfaces or decreasing the ceiling height to control reverberation may not be effective. However, increasing the surface absorption and putting barriers around tables may achieve optimal speech conditions in EEs. Subdividing large EEs into smaller ones can also be effective.

  16. Predictive study on Tuscan extra virgin olive oil stability under several commercial conditions.

    PubMed

    Pagliarini, E; Zanoni, B; Giovanelli, G

    2000-04-01

    Industries aim to ensure extra virgin olive oil (EVOO) stability especially during commercial activities up to use by end consumers. The objective of this work was to set up predictive models of EVOO stability during commercial activities. Stability was studied on five lots of a batch of Tuscan virgin olive oil to simulate different commercial activities. Chemical, physical, and sensory analyses were carried out on EVOO samples. Experimental data were processed by multivariate analyses to select significant parameters and by regression analyses to set up kinetic models. A few parameters were found to be significant: hydroxytyrosol and tyrosol contents, carotenoid absorbance at 475 and 448 nm, alpha-tocopherol content, Rancimat induction time, and K(232). It was also shown that the stability of this EVOO was not significantly influenced by different uncontrolled bottling, transport, and storage conditions in supermarkets. Empirical models were set up to predict the time to reach a reference value for K(232).

  17. Prediction of air temperature in the aircraft cabin under different operational conditions

    NASA Astrophysics Data System (ADS)

    Volavý, F.; Fišer, J.; Nöske, I.

    2013-04-01

    This paper deals with the prediction of the air temperature in the aircraft cabin by means of Computational Fluid Dynamics. The simulations are performed on the CFD model which is based on geometry and cabin interior arrangement of the Flight Test Facility (FTF) located at Fraunhofer IBP, Germany. The experimental test flights under three different cabin temperatures were done in FTF and the various data were gathered during these flights. Air temperature in the cabin was measured on probes located near feet, torso and head of each passenger and also surface temperature and air temperature distributed from inlets were measured. The data were firstly analysed in order to obtain boundary conditions for cabin surfaces and inlets. Then the results of air temperature from the simulations were compared with measured data. The suitability and accuracy of the CFD approach for temperature prediction is discussed.

  18. On the Impact of Uncertainty in Initial Conditions of Hydrologic Models on Prediction

    NASA Astrophysics Data System (ADS)

    Razavi, S.; Sheikholeslami, R.

    2015-12-01

    Determining the initial conditions for predictive models remains a challenge due to the uncertainty in measurement/identification of the state variables at the scale of interest. However, the characterization of uncertainty in initial conditions has arguably attracted less attention compared with other sources of uncertainty in hydrologic modelling (e.g, parameter, data, and structural uncertainty). This is perhaps because it is commonly believed that: (1) hydrologic systems (relatively rapidly) forget their initial conditions over time, and (2) other sources of uncertainty (e.g., in data) are dominant. This presentation revisits the basic principles of the theory of nonlinear dynamical systems in the context of hydrologic systems. Through simple example case studies, we demonstrate how and under what circumstances different hydrologic processes represent a range of attracting limit sets in their evolution trajectory in state space over time, including fixed points, limit cycles (periodic behaviour), torus (quasi-periodic behaviour), and strange attractors (chaotic behaviour). Furthermore, the propagation (or dissipation) of uncertainty in initial conditions of several hydrologic models through time, under any of the possible attracting limit sets, is investigated. This study highlights that there are definite situations in hydrology where uncertainty in initial conditions remains of significance. The results and insights gained have important implications for hydrologic modelling under non-stationarity in climate and environment.

  19. Prediction of water intake and excretion flows in Holstein dairy cows under thermoneutral conditions.

    PubMed

    Khelil-Arfa, H; Boudon, A; Maxin, G; Faverdin, P

    2012-10-01

    The increase in the worldwide demand for dairy products, associated with global warming, will emphasize the issue of water use efficiency in dairy systems. The evaluation of environmental issues related to the management of animal dejections will also require precise biotechnical models that can predict effluent management in farms. In this study, equations were developed and evaluated for predicting the main water flows at the dairy cow level, based on parameters related to cow productive performance and diet under thermoneutral conditions. Two datasets were gathered. The first one comprised 342 individual measurements of water balance in dairy cows obtained during 18 trials at the experimental farm of Méjussaume (INRA, France). Predictive equations of water intake, urine and fecal water excretion were developed by multiple regression using a stepwise selection of regressors from a list of seven candidate parameters, which were milk yield, dry matter intake (DMI), body weight, diet dry matter content (DM), proportion of concentrate (CONC) and content of crude protein (CP) ingested with forage and concentrate (CPf and CPc, g/kg DM). The second dataset was used for external validation of the developed equations and comprised 196 water flow measurements on experimental lots obtained from 43 published papers related to water balance or digestibility measurements in dairy cows. Although DMI was the first predictor of the total water intake (TWI), with a partial r(2) of 0.51, DM was the first predictive parameter of free water intake (FWI), with a partial r(2) of 0.57, likely due to the large variability of DM in the first dataset (from 11.5 to 91.4 g/100 g). This confirmed the compensation between water drunk and ingested with diet when DM changes. The variability of urine volume was explained mainly by the CPf associated with DMI (r.s.d. 5.4 kg/day for an average flow of 24.0 kg/day) and that of fecal water was explained by the proportion of CONC in the diet and DMI

  20. Conditioned cortical reactivity to cues predicting cigarette-related or pleasant images

    PubMed Central

    Deweese, Menton M.; Robinson, Jason D.; Cinciripini, Paul M.; Versace, Francesco

    2016-01-01

    Through Pavlovian conditioning, reward-associated neutral stimuli can acquire incentive salience and motivate complex behaviors. In smokers, cigarette-associated cues may induce cravings and trigger smoking. Understanding the brain mechanisms underlying conditioned responses to cigarette-associated relative to other inherently pleasant stimuli might contribute to the development of more effective smoking cessation treatments that emphasize the rehabilitation of reward circuitry. Here we measured brain responses to geometric patterns (the conditioned stimuli, CSs) predicting cigarette-related, intrinsically pleasant and neutral images (the unconditioned stimuli, USs) using event-related potentials (ERPs) in 29 never-smokers, 20 nicotine-deprived smokers, and 19 non-deprived smokers. Results showed that during US presentation, cigarette-related and pleasant images prompted higher cortical positivity than neutral images over centro-parietal sensors between 400 and 800 ms post-US onset (late positive potential, LPP). The LPP evoked by pleasant images was significantly larger than the LPP evoked by cigarette images. During CS presentation, ERPs evoked by geometric patterns predicting pleasant and cigarette-related images had significantly larger amplitude than ERPs evoked by CSs predicting neutral images. These effects were maximal over right parietal sites between 220 and 240 ms post-CS onset and over occipital and frontal sites between 308 and 344 ms post-CS onset. Smoking status did not modulate these effects. Our results show that stimuli with no intrinsic reward value (e.g., geometric patterns) may acquire rewarding properties through repeated pairings with established reward cues (i.e., cigarette-related, intrinsically pleasant). PMID:26826400

  1. Numerical prediction of a draft tube flow taking into account uncertain inlet conditions

    NASA Astrophysics Data System (ADS)

    Brugiere, O.; Balarac, G.; Corre, C.; Metais, O.; Flores, E.; Pleroy

    2012-11-01

    The swirling turbulent flow in a hydroturbine draft tube is computed with a non-intrusive uncertainty quantification (UQ) method coupled to Reynolds-Averaged Navier-Stokes (RANS) modelling in order to take into account in the numerical prediction the physical uncertainties existing on the inlet flow conditions. The proposed approach yields not only mean velocity fields to be compared with measured profiles, as is customary in Computational Fluid Dynamics (CFD) practice, but also variance of these quantities from which error bars can be deduced on the computed profiles, thus making more significant the comparison between experiment and computation.

  2. Assessing Long-Term Wind Conditions by Combining Different Measure-Correlate-Predict Algorithms: Preprint

    SciTech Connect

    Zhang, J.; Chowdhury, S.; Messac, A.; Hodge, B. M.

    2013-08-01

    This paper significantly advances the hybrid measure-correlate-predict (MCP) methodology, enabling it to account for variations of both wind speed and direction. The advanced hybrid MCP method uses the recorded data of multiple reference stations to estimate the long-term wind condition at a target wind plant site. The results show that the accuracy of the hybrid MCP method is highly sensitive to the combination of the individual MCP algorithms and reference stations. It was also found that the best combination of MCP algorithms varies based on the length of the correlation period.

  3. Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis.

    PubMed

    Edginton, Andrea N; Willmann, Stefan

    2008-01-01

    Liver cirrhosis is a progressive disease characterized by loss of functional hepatocytes with concomitant connective tissue and nodule formation in the liver. The morphological and physiological changes associated with the disease substantially affect drug pharmacokinetics. Whole-body physiologically based pharmacokinetic (WB-PBPK) modelling is a predictive technique that quantitatively relates the pharmacokinetic parameters of a drug to such (patho-)physiological conditions. To extend an existing WB-PBPK model, based on the physiological changes associated with liver cirrhosis, which allows for prediction of drug pharmacokinetics in patients with liver cirrhosis. The literature was searched for quantitative measures of the physiological changes associated with the presence of Child-Pugh class A through C liver cirrhosis. The parameters that were included were the organ blood flows, cardiac index, plasma binding protein concentrations, haematocrit, functional liver volume, hepatic enzymatic activity and glomerular filtration rate. Predictions of pharmacokinetic profiles and parameters were compared with literature data for the model compounds alfentanil, lidocaine (lignocaine), theophylline and levetiracetam. The predicted versus observed plasma concentration-time profiles for alfentanil and lidocaine were similar, such that the pharmacokinetic changes associated with Child-Pugh class A, B and C liver cirrhosis were adequately described. The theophylline elimination half-life was greatly increased in Child-Pugh class B and C patients compared with controls, as predicted by the model. Levetiracetam urinary excretion was consistently reduced with disease progression and very closely resembled observed values. Consideration of the physiological differences between healthy individuals and patients with liver cirrhosis was important for the simulation of drug pharmacokinetics in this compromised group. The WB-PBPK model was altered to incorporate these physiological

  4. Paternal genetic contribution to offspring condition predicted by size of male secondary sexual character

    PubMed Central

    Sheldon, B. C.; Merila, J.; Qvarnström, A.; Gustafsson, L.; Ellegren, H.

    1997-01-01

    Whether females can obtain genetic benefits from mate choice is contentious, and the main problem faced by previous studies of natural populations is that many factors other than paternal genes contribute to offspring fitness. Here, we use comparisons between sets of naturally occurring maternal half-sibling collared flycatchers, Ficedula albicollis, to control for this problem. We show, first, that there are paternal genetic effects on nestling fledging condition, a character related to fitness in this species. Further, the magnitude of the paternal genetic contribution to this character is related to the size of a condition-dependent male secondary sexual character. Our results demonstrate that genetic benefits from mate choice can be predicted by the size of a secondary sexual character, and therefore provide direct support for indicator models of sexual selection.

  5. Ecological Factors Predict Transition Readiness/Self-Management in Youth With Chronic Conditions.

    PubMed

    Javalkar, Karina; Johnson, Meredith; Kshirsagar, Abhijit V; Ocegueda, Sofia; Detwiler, Randal K; Ferris, Maria

    2016-01-01

    Health care transition readiness or self-management among adolescents and young adults (AYA) with chronic conditions may be influenced by factors related to their surrounding environment. Study participants were AYA diagnosed with a chronic condition and evaluated at pediatric- and adult-focused subspecialty clinics at the University of North Carolina Hospital Systems. All participants were administered a provider-administered self-management/transition-readiness tool, the UNC TRxANSITION Scale. Geographic area and associated characteristics (ecological factors) were identified for each participant's ZIP code using the published U.S. Census data. The Level 1 model of the hierarchical linear regression used individual-level predictors of transition readiness/self-management. The Level 2 model incorporated the ecological factors. We enrolled 511 AYA with different chronic conditions aged 12-31 years with the following characteristics: mean age of 20± 4 years, 45% white, 42% black, and 54% female. Participants represented 214 ZIP codes in or around North Carolina, USA. The Level 1 model showed that age, gender, and race were significant predictors of transition readiness/self-management. On adding the ecological factors in the Level 2 model, race was no longer significant. Participants from a geographic area with a greater percentage of females (β = .114, p = .005) and a higher median income (β = .126, p = .002) had greater overall transition readiness. Ecological factors also predicted subdomains of transition readiness/self-management. In this cohort of adolescents and young adults with different chronic conditions, ecological disparities such as sex composition, median income, and language predict self-management/transition readiness. It is important to take ecological risk factors into consideration when preparing patients for health self-management or transition. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All

  6. Sensitivity of soil moisture initialization for decadal predictions under different regional climatic conditions in Europe

    NASA Astrophysics Data System (ADS)

    Khodayar, S.; Sehlinger, A.; Feldmann, H.; Kottmeier, C.

    2015-12-01

    The impact of soil initialization is investigated through perturbation simulations with the regional climate model COSMO-CLM. The focus of the investigation is to assess the sensitivity of simulated extreme periods, dry and wet, to soil moisture initialization in different climatic regions over Europe and to establish the necessary spin up time within the framework of decadal predictions for these regions. Sensitivity experiments consisted of a reference simulation from 1968 to 1999 and 5 simulations from 1972 to 1983. The Effective Drought Index (EDI) is used to select and quantify drought status in the reference run to establish the simulation time period for the sensitivity experiments. Different soil initialization procedures are investigated. The sensitivity of the decadal predictions to soil moisture initial conditions is investigated through the analysis of water cycle components' (WCC) variability. In an episodic time scale the local effects of soil moisture on the boundary-layer and the propagated effects on the large-scale dynamics are analysed. The results show: (a) COSMO-CLM reproduces the observed features of the drought index. (b) Soil moisture initialization exerts a relevant impact on WCC, e.g., precipitation distribution and intensity. (c) Regional characteristics strongly impact the response of the WCC. Precipitation and evapotranspiration deviations are larger for humid regions. (d) The initial soil conditions (wet/dry), the regional characteristics (humid/dry) and the annual period (wet/dry) play a key role in the time that soil needs to restore quasi-equilibrium and the impact on the atmospheric conditions. Humid areas, and for all regions, a humid initialization, exhibit shorter spin up times, also soil reacts more sensitive when initialised during dry periods. (e) The initial soil perturbation may markedly modify atmospheric pressure field, wind circulation systems and atmospheric water vapour distribution affecting atmospheric stability

  7. Can phenological models predict tree phenology accurately under climate change conditions?

    NASA Astrophysics Data System (ADS)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  8. Predicting a contact's sensitivity to initial conditions using metrics of frictional coupling

    DOE PAGES

    Flicek, Robert C.; Hills, David A.; Brake, Matthew Robert W.

    2016-09-29

    This paper presents a method for predicting how sensitive a frictional contact’s steady-state behavior is to its initial conditions. Previous research has proven that if a contact is uncoupled, i.e. if slip displacements do not influence the contact pressure distribution, then its steady-state response is independent of initial conditions, but if the contact is coupled, the steady-state response depends on initial conditions. In this paper, two metrics for quantifying coupling in discrete frictional systems are examined. These metrics suggest that coupling is dominated by material dissimilarity due to Dundurs’ composite material parameter β when β ≥ 0.2, but geometric mismatchmore » becomes the dominant source of coupling for smaller values of β. Based on a large set of numerical simulations with different contact geometries, material combinations, and friction coefficients, a contact’s sensitivity to initial conditions is found to be correlated with the product of the coupling metric and the friction coefficient. For cyclic shear loading, this correlation is maintained for simulations with different contact geometries, material combinations, and friction coefficients. Furthermore, for cyclic bulk loading, the correlation is only maintained when the contact edge angle is held constant.« less

  9. Predicting a contact's sensitivity to initial conditions using metrics of frictional coupling

    SciTech Connect

    Flicek, Robert C.; Hills, David A.; Brake, Matthew Robert W.

    2016-09-29

    This paper presents a method for predicting how sensitive a frictional contact’s steady-state behavior is to its initial conditions. Previous research has proven that if a contact is uncoupled, i.e. if slip displacements do not influence the contact pressure distribution, then its steady-state response is independent of initial conditions, but if the contact is coupled, the steady-state response depends on initial conditions. In this paper, two metrics for quantifying coupling in discrete frictional systems are examined. These metrics suggest that coupling is dominated by material dissimilarity due to Dundurs’ composite material parameter β when β ≥ 0.2, but geometric mismatch becomes the dominant source of coupling for smaller values of β. Based on a large set of numerical simulations with different contact geometries, material combinations, and friction coefficients, a contact’s sensitivity to initial conditions is found to be correlated with the product of the coupling metric and the friction coefficient. For cyclic shear loading, this correlation is maintained for simulations with different contact geometries, material combinations, and friction coefficients. Furthermore, for cyclic bulk loading, the correlation is only maintained when the contact edge angle is held constant.

  10. Predicting a contact's sensitivity to initial conditions using metrics of frictional coupling

    SciTech Connect

    Flicek, Robert C.; Hills, David A.; Brake, Matthew Robert W.

    2016-09-29

    This paper presents a method for predicting how sensitive a frictional contact’s steady-state behavior is to its initial conditions. Previous research has proven that if a contact is uncoupled, i.e. if slip displacements do not influence the contact pressure distribution, then its steady-state response is independent of initial conditions, but if the contact is coupled, the steady-state response depends on initial conditions. In this paper, two metrics for quantifying coupling in discrete frictional systems are examined. These metrics suggest that coupling is dominated by material dissimilarity due to Dundurs’ composite material parameter β when β ≥ 0.2, but geometric mismatch becomes the dominant source of coupling for smaller values of β. Based on a large set of numerical simulations with different contact geometries, material combinations, and friction coefficients, a contact’s sensitivity to initial conditions is found to be correlated with the product of the coupling metric and the friction coefficient. For cyclic shear loading, this correlation is maintained for simulations with different contact geometries, material combinations, and friction coefficients. Furthermore, for cyclic bulk loading, the correlation is only maintained when the contact edge angle is held constant.

  11. Key Comorbid Conditions that Are Predictive of Survival among Hemodialysis Patients

    PubMed Central

    Bragg-Gresham, Jennifer; Gillespie, Brenda W.; Tentori, Francesca; Pisoni, Ronald L.; Tighiouart, Hocine; Levey, Andrew S.; Port, Friedrich K.

    2009-01-01

    Background and objectives: Abstracting information about comorbid illnesses from the medical record can be time-consuming, particularly when a large number of conditions are under consideration. We sought to determine which conditions are most prognostic and whether comorbidity continues to contribute to a survival model once laboratory and clinical parameters have been accounted for. Design, setting, participants, & measurements: Comorbidity data were abstracted from the medical records of Dialysis Outcomes and Practice Pattern Study (DOPPS) I, II, and III participants using a standardized questionnaire. Models that were composed of different combinations of comorbid conditions and case-mix factors were compared for explained variance (R2) and discrimination (c statistic). Results: Seventeen comorbid conditions account for 96% of the total explained variance that would result if 45 comorbidities that were expected to be predictive of survival were added to a demographics-adjusted survival model. These conditions together had more discriminatory power (c statistic 0.67) than age alone (0.63) or serum albumin (0.60) and were equivalent to a combination of routine laboratory and clinical parameters (0.67). The strength of association of the individual comorbidities lessened when laboratory/clinical parameters were added, but all remained significant. The total R2 of a model adjusted for demographics and laboratory/clinical parameters increased from 0.13 to 0.17 upon addition of comorbidity. Conclusions: A relatively small list of comorbid conditions provides equivalent discrimination and explained variance for survival as a more extensive characterization of comorbidity. Comorbidity adds to the survival model a modest amount of independent prognostic information that cannot be substituted by clinical/laboratory parameters. PMID:19808231

  12. Influence of thermal buoyancy on vertical tube bundle thermal density head predictions under transient conditions. [LMFBR

    SciTech Connect

    Lin, H.C.; Kasza, K.E.

    1984-01-01

    The thermal-hydraulic behavior of an LMFBR system under various types of plant transients is usually studied using one-dimensional (1-D) flow and energy transport models of the system components. Many of the transient events involve the change from a high to a low flow with an accompanying change in temperature of the fluid passing through the components which can be conductive to significant thermal bouyancy forces. Thermal bouyancy can exert its influence on system dynamic energy transport predictions through alterations of flow and thermal distributions which in turn can influence decay heat removal, system-response time constants, heat transport between primary and secondary systems, and thermal energy rejection at the reactor heat sink, i.e., the steam generator. In this paper the results from a comparison of a 1-D model prediction and experimental data for vertical tube bundle overall thermal density head and outlet temperature under transient conditions causing varying degrees of thermal bouyancy are presented. These comparisons are being used to generate insight into how, when, and to what degree thermal buoyancy can cause departures from 1-D model predictions.

  13. Comparison of model predictions for coherence length to in-flight measurements at cruise conditions

    NASA Astrophysics Data System (ADS)

    Haxter, Stefan; Spehr, Carsten

    2017-03-01

    In this paper, we will focus on coherence lengths of pressure fluctuations underneath a turbulent boundary layer on an actual aircraft measured during a flight test. Coherence lengths of pressure fluctuations have already been measured in the past and various models have been set up in order to predict the values. However, most of the underlying data were measured at Mach numbers and pressures different from our region of interest and it is not known if the models are applicable. In some of the investigations also unknown alignment procedures between array and flow were used and it will be shown that this can have a considerable influence on the result. We have performed flight tests at cruising speed and altitude in which we took due account of this alignment by means of an array processing technique which is capable of determining the flow direction for each frequency bin under consideration. In this paper one of the data points will be evaluated and compared to the prediction models. From the differences and subsequently from the adopted run conditions for the measurement of the data of the models, several conclusions are drawn concerning scaling effects and importance of alignment. Also, two of the prediction models are adjusted to our measurements.

  14. Predictions of structural integrity of steam generator tubes under normal operating, accident, an severe accident conditions

    SciTech Connect

    Majumdar, S.

    1997-02-01

    Available models for predicting failure of flawed and unflawed steam generator tubes under normal operating, accident, and severe accident conditions are reviewed. Tests conducted in the past, though limited, tended to show that the earlier flow-stress model for part-through-wall axial cracks overestimated the damaging influence of deep cracks. This observation was confirmed by further tests at high temperatures, as well as by finite-element analysis. A modified correlation for deep cracks can correct this shortcoming of the model. Recent tests have shown that lateral restraint can significantly increase the failure pressure of tubes with unsymmetrical circumferential cracks. This observation was confirmed by finite-element analysis. The rate-independent flow stress models that are successful at low temperatures cannot predict the rate-sensitive failure behavior of steam generator tubes at high temperatures. Therefore, a creep rupture model for predicting failure was developed and validated by tests under various temperature and pressure loadings that can occur during postulated severe accidents.

  15. Hf Radio Propagation At High Latitudes: Observations and Predictions For Quiet and Disturbed Conditions

    NASA Astrophysics Data System (ADS)

    Jacobsen, B.; Jodalen, V.; Cannon, P. S.; Smith, O.; Angling, M. J.

    High-frequency (HF) radio communications at high latitudes are greatly affected by geomagnetic and ionospheric conditions, and both civilian and military users need re- liable forecasts of the propagation environment. In recent years, a network of channel sounders known as DAMSON (Doppler and multipath sounding network) has been operated in Scandinavia (including Svalbard) on a nearly continous basis. We have analysed DAMSON measurements of multipath spread, Doppler shift and spread, and signal-to-noise ratio on four HF paths under both quiet and disturbed geomagnetic conditions. Correlations have been made with data from other ground-based installa- tions in the same geographic region (HF radars, magnetometers, ionosondes). Long signal delays (several ms) are regularly observed during midday at frequencies above the predicted Maximum Usable Frequency (MUF) and could possibly be caused by ground scatter. Large Doppler spreads (tens of Hz) are observed during disturbed con- ditions (substorms), when the ionospheric reflection point is located within the auroral oval. We suggest that forecasts of the HF multipath and Doppler environment based on sounder measurements could be utilized as additions to the regular HF predictions of e.g., the MUF and LUF of a propagation path.

  16. Prediction and visualization of redox conditions in the groundwater of Central Valley, California

    NASA Astrophysics Data System (ADS)

    Rosecrans, Celia Z.; Nolan, Bernard T.; Gronberg, JoAnn M.

    2017-03-01

    Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of

  17. Prediction and visualization of redox conditions in the groundwater of Central Valley, California

    USGS Publications Warehouse

    Rosecrans, Celia Z.; Nolan, Bernard T.; Gronberg, JoAnn M.

    2017-01-01

    Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions.Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of

  18. Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

    PubMed

    Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

    2015-01-01

    This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of

  19. Probing Pluto's Underworld : Predicted Ice Temperatures from Microwave Radiometry Decoupled from Surface Conditions

    NASA Astrophysics Data System (ADS)

    Le Gall, Alice; Lorenz, Ralph; Leyrat, Cedric

    2015-11-01

    The Pluto dwarf planet has been successfully observed in July 2015 by the New Horizons spacecraft (NASA) during a close-targeted flyby which reavealed surprising and fascinating landscapes. While data are still being downlinked on the ground, we propose to present a prediction of the observation of the Radio Science Experiment experiment (REX) that occured on July 14, 2015 and aimed at measuring the microwave brightness temperature of Pluto’s night side.Present models admit a wide range of 2015 surface conditions at Pluto and Charon, where the atmospheric pressure may undergo dramatic seasonal variation and for which measurements have been performed by the New Horizons mission. One anticipated observation is the microwave brightness temperature, heretofore anticipated as indicating surface conditions relevant to surface-atmosphere equilibrium. However, drawing on recent experience with Cassini observations at Iapetus and Titan, we call attention to the large electrical skin depth of outer solar system materials such as methane, nitrogen or water ice, such that this observation may indicate temperatures averaged over depths of several or tens of meters beneath the surface.Using a seasonally-forced thermal model to determine microwave emission we predict that the southern hemisphere observations (in the polar night in July 2015) of New Horizons should display relatively warm effective temperatures of about 40 K. This would reflect the deep heat buried over the last century of summer, even if the atmospheric pressure suggests that the surface nitrogen frost point may be much lower. We will present our predictions and discuss their impact for the interpretation of the REX measurements.

  20. Developing A New Predictive Dispersion Equation Based on Tidal Average (TA) Condition in Alluvial Estuaries

    NASA Astrophysics Data System (ADS)

    Anak Gisen, Jacqueline Isabella; Nijzink, Remko C.; Savenije, Hubert H. G.

    2014-05-01

    Dispersion mathematical representation of tidal mixing between sea water and fresh water in The definition of dispersion somehow remains unclear as it is not directly measurable. The role of dispersion is only meaningful if it is related to the appropriate temporal and spatial scale of mixing, which are identified as the tidal period, tidal excursion (longitudinal), width of estuary (lateral) and mixing depth (vertical). Moreover, the mixing pattern determines the salt intrusion length in an estuary. If a physically based description of the dispersion is defined, this would allow the analytical solution of the salt intrusion problem. The objective of this study is to develop a predictive equation for estimating the dispersion coefficient at tidal average (TA) condition, which can be applied in the salt intrusion model to predict the salinity profile for any estuary during different events. Utilizing available data of 72 measurements in 27 estuaries (including 6 recently studied estuaries in Malaysia), regressions analysis has been performed with various combinations of dimensionless parameters . The predictive dispersion equations have been developed for two different locations, at the mouth D0TA and at the inflection point D1TA (where the convergence length changes). Regressions have been carried out with two separated datasets: 1) more reliable data for calibration; and 2) less reliable data for validation. The combination of dimensionless ratios that give the best performance is selected as the final outcome which indicates that the dispersion coefficient is depending on the tidal excursion, tidal range, tidal velocity amplitude, friction and the Richardson Number. A limitation of the newly developed equation is that the friction is generally unknown. In order to compensate this problem, further analysis has been performed adopting the hydraulic model of Cai et. al. (2012) to estimate the friction and depth. Keywords: dispersion, alluvial estuaries, mixing, salt

  1. Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.

    PubMed

    Kim, Minseung; Zorraquino, Violeta; Tagkopoulos, Ilias

    2015-03-01

    A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (±3.5%) to 98.3% (±2.3%) for the various characteristics. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain) achieved 10.6% (±1.0%) higher performance than any individual models. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

  2. Building spatially-explicit model predictions for ecological condition of streams in the Pacific Northwest: An assessment of landscape variables, models, endpoints and prediction scale

    EPA Science Inventory

    While large-scale, randomized surveys estimate the percentage of a region’s streams in poor ecological condition, identifying particular stream reaches or watersheds in poor condition is an equally important goal for monitoring and management. We built predictive models of strea...

  3. Why is it so difficult to represent stably stratified conditions in numerical weather prediction (NWP) models?

    NASA Astrophysics Data System (ADS)

    Sandu, Irina; Beljaars, Anton; Bechtold, Peter; Mauritsen, Thorsten; Balsamo, Gianpaolo

    2013-06-01

    In the 1990s, scientists at European Centre for Medium-Range Weather Forecasts (ECMWF) suggested that artificially enhancing turbulent diffusion in stable conditions improves the representation of two important aspects of weather forecasts, i.e., near-surface temperatures and synoptic cyclones. Since then, this practice has often been used for tuning the large-scale performance of operational numerical weather prediction (NWP) models, although it is widely recognized to be detrimental for an accurate representation of stable boundary layers. Here we investigate why, 20 years on, such a compromise is still needed in the ECMWF model. We find that reduced turbulent diffusion in stable conditions improves the representation of winds in stable boundary layers, but it deteriorates the large-scale flow and the near-surface temperatures. This suggests that enhanced diffusion is still needed to compensate for errors caused by other poorly represented processes. Among these, we identify the orographic drag, which influences the large-scale flow in a similar way to the turbulence closure for stable conditions, and the strength of the land-atmosphere coupling, which partially controls the near-surface temperatures. We also take a closer look at the relationship between the turbulence closure in stable conditions and the large-scale flow, which was not investigated in detail with a global NWP model. We demonstrate that the turbulent diffusion in stable conditions affects the large-scale flow by modulating not only the strength of synoptic cyclones and anticyclones, but also the amplitude of the planetary-scale standing waves.

  4. Development of an analytical method to predict helicopter main rotor performance in icing conditions

    NASA Technical Reports Server (NTRS)

    Britton, Randall K.

    1992-01-01

    Historically, certification of a helicopter for flight into known icing conditions was a problem. This is because of the current emphasis on flight testing for verification of system performance. Flight testing in icing conditions is difficult because, in addition to being dangerous and expensive, many times conditions which are sought after cannot be readily found in nature. The problem is compounded for helicopters because of their small range in comparison to many fixed wing aircraft. Thus, helicopters are forced to wait for conditions to occur in a certain region rather than seeking them out. These and other drawbacks to flight testing prompted extreme interest in developing validated alternatives to flight testing. One such alternative is theoretical prediction. It is desirable to have the ability to predict how a helicopter will perform when subjected to icing conditions. Herein, calculations are restricted to the main rotor, and are illustrated. The computational tool used to obtain performance is the lifting line analysis of B65. B65 incorporates experimental data into data banks in order to determine the section lift, drag, and moment characteristics of various airfoils at different Mach numbers and angles of attack. The local flow angle is calculated at user specified radial locations. This flow angle, along with the local Mach number is then cross referenced with the airfoil tables to obtain the local section characteristics. The local characteristics are then integrated together to obtain the entire rotor attributes. Once the clean performance is known, characterization of the type and shape of ice which accretes on the rotor blades is obtained using the analysis of LEWICE. The Interactive Boundary Layer (IBL) method then calculates the 2-D characteristics of the iced airfoil for input into the airfoil data bank of B65. Calculations are restricted to natural ice shedding and it is assumed that no de-icing takes place. Once the new lift, drag, and moment

  5. Predicting Ductility and Failure Modes of TRIP Steels under Different Loading Conditions

    SciTech Connect

    Choi, Kyoo Sil; Liu, Wenning N.; Sun, Xin; Khaleel, Mohammad A.

    2010-06-12

    We study the ultimate ductility and failure modes of a TRIP (TRansformation-Induced Plasticity) 800 steel under different loading conditions with an advanced micromechanics-based finite element analysis. The representative volume element (RVE) for the TRIP800 under examination is developed based on an actual microstructure obtained from scanning electron microscopy (SEM). The evolution of retained austenite during deformation process and the mechanical properties of the constituent phases of the TRIP800 steel are obtained from the synchrotron-based in-situ high-energy X-ray diffraction (HEXRD) experiments and a self-consistent (SC) model. The ductile failure of the TRIP800 under different loading conditions is predicted in the form of plastic strain localization without any prescribed failure criteria for the individual phases. Comparisons of the computational results with experimental measurements suggest that the microstructure-based finite element analysis can well capture the overall macroscopic behavior of the TRIP800 steel under different loading conditions. The methodology described in this study may be extended for studying the ultimate ductile failure mechanisms of TRIP steels as well as the effects of the various processing parameters on the macroscopic behaviors of TRIP steels.

  6. A hybrid prognostic model for multistep ahead prediction of machine condition

    NASA Astrophysics Data System (ADS)

    Roulias, D.; Loutas, T. H.; Kostopoulos, V.

    2012-05-01

    Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

  7. ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction

    NASA Astrophysics Data System (ADS)

    Beckers, Joost V. L.; Weerts, Albrecht H.; Tijdeman, Erik; Welles, Edwin

    2016-08-01

    Oceanic-atmospheric climate modes, such as El Niño-Southern Oscillation (ENSO), are known to affect the local streamflow regime in many rivers around the world. A new method is proposed to incorporate climate mode information into the well-known ensemble streamflow prediction (ESP) method for seasonal forecasting. The ESP is conditioned on an ENSO index in two steps. First, a number of original historical ESP traces are selected based on similarity between the index value in the historical year and the index value at the time of forecast. In the second step, additional ensemble traces are generated by a stochastic ENSO-conditioned weather resampler. These resampled traces compensate for the reduction of ensemble size in the first step and prevent degradation of skill at forecasting stations that are less affected by ENSO. The skill of the ENSO-conditioned ESP is evaluated over 50 years of seasonal hindcasts of streamflows at three test stations in the Columbia River basin in the US Pacific Northwest. An improvement in forecast skill of 5 to 10 % is found for two test stations. The streamflows at the third station are less affected by ENSO and no change in forecast skill is found here.

  8. Conditional spectrum computation incorporating multiple causal earthquakes and ground-motion prediction models

    USGS Publications Warehouse

    Lin, Ting; Harmsen, Stephen C.; Baker, Jack W.; Luco, Nicolas

    2013-01-01

    The conditional spectrum (CS) is a target spectrum (with conditional mean and conditional standard deviation) that links seismic hazard information with ground-motion selection for nonlinear dynamic analysis. Probabilistic seismic hazard analysis (PSHA) estimates the ground-motion hazard by incorporating the aleatory uncertainties in all earthquake scenarios and resulting ground motions, as well as the epistemic uncertainties in ground-motion prediction models (GMPMs) and seismic source models. Typical CS calculations to date are produced for a single earthquake scenario using a single GMPM, but more precise use requires consideration of at least multiple causal earthquakes and multiple GMPMs that are often considered in a PSHA computation. This paper presents the mathematics underlying these more precise CS calculations. Despite requiring more effort to compute than approximate calculations using a single causal earthquake and GMPM, the proposed approach produces an exact output that has a theoretical basis. To demonstrate the results of this approach and compare the exact and approximate calculations, several example calculations are performed for real sites in the western United States. The results also provide some insights regarding the circumstances under which approximate results are likely to closely match more exact results. To facilitate these more precise calculations for real applications, the exact CS calculations can now be performed for real sites in the United States using new deaggregation features in the U.S. Geological Survey hazard mapping tools. Details regarding this implementation are discussed in this paper.

  9. Predicting the drying properties of sludge based on hydrothermal treatment under subcritical conditions.

    PubMed

    Mäkelä, Mikko; Fraikin, Laurent; Léonard, Angélique; Benavente, Verónica; Fullana, Andrés

    2016-03-15

    The effects of hydrothermal treatment on the drying properties of sludge were determined. Sludge was hydrothermally treated at 180-260 °C for 0.5-5 h using NaOH and HCl as additives to influence reaction conditions. Untreated sludge and attained hydrochar samples were then dried under identical conditions with a laboratory microdryer and an X-ray microtomograph was used to follow changes in sample dimensions. The effective moisture diffusivities of sludge and hydrochar samples were determined and the effect of process conditions on respective mean diffusivities evaluated using multiple linear regression. Based on the results the drying time of untreated sludge decreased from approximately 80 min to 37-59 min for sludge hydrochar. Drying of untreated sludge was governed by the falling rate period where drying flux decreased continuously as a function of sludge moisture content due to heat and mass transfer limitations and sample shrinkage. Hydrothermal treatment increased the drying flux of sludge hydrochar and decreased the effect of internal heat and mass transfer limitations and sample shrinkage especially at higher treatment temperatures. The determined effective moisture diffusivities of sludge and hydrochar increased as a function of decreasing moisture content and the mean diffusivity of untreated sludge (8.56·10(-9) m(2) s(-1)) and sludge hydrochar (12.7-27.5·10(-9) m(2) s(-1)) were found statistically different. The attained regression model indicated that treatment temperature governed the mean diffusivity of hydrochar, as the effects of NaOH and HCl were statistically insignificant. The attained results enabled prediction of sludge drying properties through mean moisture diffusivity based on hydrothermal treatment conditions.

  10. Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

    NASA Technical Reports Server (NTRS)

    Bolten, John; Crow, Wade

    2012-01-01

    The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.

  11. First-Principles Prediction of the Equilibrium Shape of Nanoparticles Under Realistic Electrochemical Conditions

    NASA Astrophysics Data System (ADS)

    Bonnet, Nicéphore; Marzari, Nicola

    2013-02-01

    A first-principles model of the electrochemical double layer is applied to study surface energies and surface coverage under realistic electrochemical conditions and to determine the equilibrium shape of metal nanoparticles as a function of applied potential. The potential bias is directly controlled by adding electronic charge to the system, while total energy calculations and thermodynamic relations are used to predict electrodeposition curves and changes in surface energies and coverage. This approach is applied to Pt surfaces subject to hydrogen underpotential deposition. The shape of Pt nanoparticles under a cathodic scan is shown to undergo an octahedric-to-cubic transition, which is more pronounced in alkaline media due to the interaction energy of the pH-dependent surface charge with the surface dipole.

  12. First-principles prediction of the equilibrium shape of nanoparticles under realistic electrochemical conditions.

    PubMed

    Bonnet, Nicéphore; Marzari, Nicola

    2013-02-22

    A first-principles model of the electrochemical double layer is applied to study surface energies and surface coverage under realistic electrochemical conditions and to determine the equilibrium shape of metal nanoparticles as a function of applied potential. The potential bias is directly controlled by adding electronic charge to the system, while total energy calculations and thermodynamic relations are used to predict electrodeposition curves and changes in surface energies and coverage. This approach is applied to Pt surfaces subject to hydrogen underpotential deposition. The shape of Pt nanoparticles under a cathodic scan is shown to undergo an octahedric-to-cubic transition, which is more pronounced in alkaline media due to the interaction energy of the pH-dependent surface charge with the surface dipole.

  13. Method for Prediction of the Power Output from Photovoltaic Power Plant under Actual Operating Conditions

    NASA Astrophysics Data System (ADS)

    Obukhov, S. G.; Plotnikov, I. A.; Surzhikova, O. A.; Savkin, K. D.

    2017-04-01

    Solar photovoltaic technology is one of the most rapidly growing renewable sources of electricity that has practical application in various fields of human activity due to its high availability, huge potential and environmental compatibility. The original simulation model of the photovoltaic power plant has been developed to simulate and investigate the plant operating modes under actual operating conditions. The proposed model considers the impact of the external climatic factors on the solar panel energy characteristics that improves accuracy in the power output prediction. The data obtained through the photovoltaic power plant operation simulation enable a well-reasoned choice of the required capacity for storage devices and determination of the rational algorithms to control the energy complex.

  14. Predicting mental conditions based on "history of present illness" in psychiatric notes with deep neural networks.

    PubMed

    Tran, Tung; Kavuluru, Ramakanth

    2017-06-10

    Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task. We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient's history of present illness typically occurring in the beginning of a psychiatric initial evaluation note. We clean and process the 1000 records made available through the N-GRID clinical NLP task into a key-value dictionary and build a dataset of 986 examples for which there is a narrative for history of present illness as well as Yes/No responses with regards to presence of specific mental conditions. We propose two independent deep neural network models: one based on convolutional neural networks (CNN) and another based on recurrent neural networks with hierarchical attention (ReHAN), the latter of which allows for interpretation of model decisions. We conduct experiments to compare these methods to each other and to baselines based on linear models and named entity recognition (NER). Our CNN model with optimized thresholding of output probability estimates achieves best overall mean micro-F score of 63.144% for 11 common mental conditions with statistically significant gains (p<0.05) over all other models. The ReHAN model with interpretable attention mechanism scored 61.904% mean micro-F1 score. Both models' improvements over baseline models (support vector machines and NER) are statistically significant. The ReHAN model additionally aids in interpretation of the results by surfacing important words and sentences that lead to a particular prediction for each

  15. Lack of predictive power of trait fear and anxiety for conditioned pain modulation (CPM).

    PubMed

    Horn-Hofmann, Claudia; Priebe, Janosch A; Schaller, Jörg; Görlitz, Rüdiger; Lautenbacher, Stefan

    2016-12-01

    In recent years the association of conditioned pain modulation (CPM) with trait fear and anxiety has become a hot topic in pain research due to the assumption that such variables may explain the low CPM efficiency in some individuals. However, empirical evidence concerning this association is still equivocal. Our study is the first to investigate the predictive power of fear and anxiety for CPM by using a well-established psycho-physiological measure of trait fear, i.e. startle potentiation, in addition to two self-report measures of pain-related trait anxiety. Forty healthy, pain-free participants (female: N = 20; age: M = 23.62 years) underwent two experimental blocks in counter-balanced order: (1) a startle paradigm with affective picture presentation and (2) a CPM procedure with hot water as conditioning stimulus (CS) and contact heat as test stimulus (TS). At the end of the experimental session, pain catastrophizing (PCS) and pain anxiety (PASS) were assessed. PCS score, PASS score and startle potentiation to threatening pictures were entered as predictors in a linear regression model with CPM magnitude as criterion. We were able to show an inhibitory CPM effect in our sample: pain ratings of the heat stimuli were significantly reduced during hot water immersion. However, CPM was neither predicted by self-report of pain-related anxiety nor by startle potentiation as psycho-physiological measure of trait fear. These results corroborate previous negative findings concerning the association between trait fear/anxiety and CPM efficiency and suggest that shifting the focus from trait to state measures might be promising.

  16. Significance of frailty for predicting adverse clinical outcomes in different patient groups with specific medical conditions.

    PubMed

    Ritt, Martin; Gaßmann, Karl-Günter; Sieber, Cornel Christian

    2016-10-01

    Frailty is a major health burden in an aging society. It constitutes a clinical state of reduced physiological reserves that is associated with a diminished ability to withstand internal and external stressors. Frail patients have an increased risk for adverse clinical outcomes, such as mortality, readmission to hospital, institutionalization and falls. Of further clinical interest, frailty might be at least in part reversible in some patients and subject to preventive strategies. In daily clinical practice older patients with a complex health status, who are mostly frail or at least at risk of developing frailty, are frequently cared for by geriatricians. Recently, clinicians and scientists from other medical disciplines, such as cardiology, pulmonology, gastroenterology, nephrology, endocrinology, rheumatology, surgery and critical care medicine also discovered frailty to be an interesting instrument for risk stratification of patients, including younger patients. In this review we highlight the results of recent studies that demonstrated the significance of frailty to predict adverse clinical outcomes in patients with specific medical conditions, such as cardiac, lung, liver and kidney diseases as well as diabetes mellitus, osteoarthritis, trauma patients, patients undergoing surgery and critically ill patients. Multiple studies in patients with the aforementioned specific medical conditions could be identified demonstrating a predictive role of frailty for several adverse clinical outcomes. The association between frailty and adverse clinical outcomes reported in these studies was in part independent of several major potential confounder factors, such as age, sex, race, comorbidities and disabilities and were also detected in younger patients.

  17. Variably-saturated flow in large weighing lysimeters under dry conditions: inverse and predictive modeling

    NASA Astrophysics Data System (ADS)

    Iden, Sascha; Reineke, Daniela; Koonce, Jeremy; Berli, Markus; Durner, Wolfgang

    2015-04-01

    A reliable quantification of the soil water balance in semi-arid regions requires an accurate determination of bare soil evaporation. Modeling of soil water movement in relatively dry soils and the quantitative prediction of evaporation rates and groundwater recharge pose considerable challenges in these regions. Actual evaporation from dry soil cannot be predicted without detailed knowledge of the complex interplay between liquid, vapor and heat flow and soil hydraulic properties exert a strong influence on evaporation rates during stage-two evaporation. We have analyzed data from the SEPHAS lysimeter facility in Boulder City (NV) which was installed to investigate the near-surface processes of water and energy exchange in desert environments. The scientific instrumentation consists of 152 sensors per Lysimeter which measured soil temperature, soil water content, and soil water potential. Data from three weighing lysimeters (3 m long, surface area 4 m2) were used to identifiy effective soil hydraulic properties of the disturbed soil monoliths by inverse modeling with the Richards equation assuming isothermal flow conditions. Results indicate that the observed soil water content in 8 different soil depths can be well matched for all three lysimeters and that the effective soil hydraulic properties of the three lysimeters agree well. These results could only be obtained with a flexible model of the soil hydraulic properties which guaranteed physical plausibility of water retention towards complete dryness and accounted for capillary, film and isothermal vapor flow. Conversely, flow models using traditional parameterizations of the soil hydraulic properties were not able to match the observed evaporation fluxes and water contents. After identifying the system properties by inverse modeling, we checked the possibility to forecast evaporation rates by running a fully coupled water, heat and vapor flow model which solved the energy balance of the soil surface. In these

  18. Study on multi-scale blending initial condition perturbations for a regional ensemble prediction system

    NASA Astrophysics Data System (ADS)

    Zhang, Hanbin; Chen, Jing; Zhi, Xiefei; Wang, Yi; Wang, Yanan

    2015-08-01

    An initial conditions (ICs) perturbation method was developed with the aim to improve an operational regional ensemble prediction system (REPS). Three issues were identified and investigated: (1) the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; (2) the scale characteristic of the IC perturbations of the REPS; and (3) whether the REPS's skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system (GEPS) were analyzed. A "multi-scale blending" (MSB) IC perturbation scheme was developed, and the main findings can be summarized as follows: The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.

  19. Bundle critical power predictions under normal and abnormal conditions in pressurized water reactors

    SciTech Connect

    Lin, W.S.; Pei, B.S. ); Lee, C.H. )

    1992-06-01

    In this paper a new approach to bundle critical power predictions is presented. In addition to a very accurate critical heat flux (CHF) model, correction factors that account for the effects of grid spacers, heat flux non-uniformities, and cold walls, which are needed for critical power predictions for practical fuel bundles, are developed. By using the subchannel analysis code COBRA IIIC/MIT-1, local flow conditions needed as input to CHF correlations are obtained. Critical power is therefore obtained iteratively to ensure that the bundle power value from the subchannel analysis will cause CHF at only one point in the bundle. Good agreement with the experimental data is obtained. The accuracy is higher than that of the W-3 and EPRI-1 correlations for the limited data base used in this study. The effects of three types of fuel abnormalities, namely, local heat flux spikes, local flow blockages, and rod bowing, on bundle critical power are also analyzed. The local heat flux spikes and flow blockages have no significant influence on critical power. However, rod bowing phenomena have some effect, the severity of which depends on system pressure, the gap closure between adjacent rods, and the presence or absence of thimble tubes (cold walls). A correlation for the influence of various rod bowing phenomena on bundle critical power is developed. Good agreement with experimental data is shown.

  20. Effect of pour-on alphacypermethrin on feed intake, body condition score, milk yield, pregnancy rates, and calving-to-conception interval in buffaloes.

    PubMed

    Bifulco, G; Veneziano, V; Cimmino, R; Esposito, L; Auletta, L; Varricchio, E; Balestrieri, A; Claps, S; Campanile, G; Neglia, G

    2015-04-01

    The aims of this study were to assess the efficacy of alphacypermethrin (ACYP) on pediculosis due to Haematopinus tuberculatus and to evaluate the influence of the treatment on productive and reproductive performance in buffaloes (Bubalus bubalis) reared in an intensive system. The trial was performed on 56 pluriparous buffaloes at 86.8 ± 8.1 d in milk. The animals underwent individual louse count and were divided into 2 homogenous groups according to louse count, age, number of lactations, days in milk, live BW, BCS, pregnancy status, and milk yield. Group A (n = 28) was treated by a pour-on formulation of ACYP, and Group S (n = 28) was treated by pour-on saline solution. Individual louse counts were performed weekly on 10 buffaloes in each group. Feed intake was recorded daily and the total mixed ration, individual ingredients, and orts were analyzed to calculate DM ingestion. Individual milk yield was recorded daily and milk samples were analyzed at the beginning of the trial, after 4 wk, and at the end of the trial to assess milk composition. Individual BCS was also evaluated simultaneously. Finally, the animals underwent synchronization of ovulation starting 4 wk after treatment and the pregnancy rate and the calving-conception interval were evaluated. Data were analyzed by the Mann-Whitney test and ANOVA for repeated measures. The infestation was constant in Group S, whereas no lice were present in Group A throughout the study. Daily DMI was similar in the 2 groups (16.7 ± 0.4 vs. 16.3 ± 0.3 kg/d in Group A vs. Group S, respectively), although buffaloes in Group A showed higher (P < 0.05) BCS score at the end of the trial (7.39 ± 0.1 vs. 7.14 ± 0.1 in Group A vs. Group S, respectively). The average milk yield/buffalo was higher (P < 0.05) in Group A compared to Group S (10.58 ± 0.1 vs. 10.39 ± 0.1 kg in Group A vs. Group S, respectively) and this was mainly due to the higher milk production recorded in buffaloes at less than 75 d in milk (11.81 ± 0

  1. Aeroheating Testing and Predictions for Project Orion CEV at Turbulent Conditions

    NASA Technical Reports Server (NTRS)

    Hollis, Brian R.; Berger, Karen T.; Horvath, Thomas J.; Coblish, Joseph J.; Norris, Joseph D.; Lillard, Randolph P.; Kirk, Benjamin S.

    2009-01-01

    An investigation of the aeroheating environment of the Project Orion Crew Exploration Vehicle was performed in the Arnold Engineering Development Center Hypervelocity Wind Tunnel No. 9 Mach 8 and Mach 10 nozzles and in the NASA Langley Research Center 20 - Inch Mach 6 Air Tunnel. Heating data were obtained using a thermocouple-instrumented approx.0.035-scale model (0.1778-m/7-inch diameter) of the flight vehicle. Runs were performed in the Tunnel 9 Mach 10 nozzle at free stream unit Reynolds numbers of 1x10(exp 6)/ft to 20x10(exp 6)/ft, in the Tunnel 9 Mach 8 nozzle at free stream unit Reynolds numbers of 8 x 10(exp 6)/ft to 48x10(exp 6)/ft, and in the 20-Inch Mach 6 Air Tunnel at free stream unit Reynolds numbers of 1x10(exp 6)/ft to 7x10(exp 6)/ft. In both facilities, enthalpy levels were low and the test gas (N2 in Tunnel 9 and air in the 20-Inch Mach 6) behaved as a perfect-gas. These test conditions produced laminar, transitional and turbulent data in the Tunnel 9 Mach 10 nozzle, transitional and turbulent data in the Tunnel 9 Mach 8 nozzle, and laminar and transitional data in the 20- Inch Mach 6 Air Tunnel. Laminar and turbulent predictions were generated for all wind tunnel test conditions and comparisons were performed with the experimental data to help define the accuracy of computational method. In general, it was found that both laminar data and predictions, and turbulent data and predictions, agreed to within less than the estimated 12% experimental uncertainty estimate. Laminar heating distributions from all three data sets were shown to correlate well and demonstrated Reynolds numbers independence when expressed in terms of the Stanton number based on adiabatic wall-recovery enthalpy. Transition onset locations on the leeside centerline were determined from the data and correlated in terms of boundary-layer parameters. Finally turbulent heating augmentation ratios were determined for several body-point locations and correlated in terms of the

  2. Predicting the solubility of gases in Nitrile Butadiene Rubber in extreme conditions using molecular simulation

    NASA Astrophysics Data System (ADS)

    Khawaja, Musab; Molinari, Nicola; Sutton, Adrian; Mostofi, Arash

    In the oil and gas industry, elastomer seals play an important role in protecting sensitive monitoring equipment from contamination by gases - a problem that is exacerbated by the high pressures and temperatures found down-hole. The ability to predict and prevent such permeative failure has proved elusive to-date. Nitrile butadiene rubber (NBR) is a common choice of elastomer for seals due to its resistance to heat and fuels. In the conditions found in the well it readily absorbs small molecular weight gases. How this behaviour changes quantitatively for different gases as a function of temperature and pressure is not well-understood. In this work a series of fully atomistic simulations are performed to understand the effect of extreme conditions on gas solubility in NBR. Widom particle insertion is used to compute solubilities. The importance of sampling and allowing structural relaxation upon compression are highlighted, and qualitatively reasonable trends reproduced. Finally, while at STP it has previously been shown that the solubility of CO2 is higher than that of He in NBR, we observe that under the right circumstances it is possible to reverse this trend.

  3. Review of Thawing Time Prediction Models Depending
on Process Conditions and Product Characteristics

    PubMed Central

    Kluza, Franciszek; Spiess, Walter E. L.; Kozłowicz, Katarzyna

    2016-01-01

    Summary Determining thawing times of frozen foods is a challenging problem as the thermophysical properties of the product change during thawing. A number of calculation models and solutions have been developed. The proposed solutions range from relatively simple analytical equations based on a number of assumptions to a group of empirical approaches that sometimes require complex calculations. In this paper analytical, empirical and graphical models are presented and critically reviewed. The conditions of solution, limitations and possible applications of the models are discussed. The graphical and semi--graphical models are derived from numerical methods. Using the numerical methods is not always possible as running calculations takes time, whereas the specialized software and equipment are not always cheap. For these reasons, the application of analytical-empirical models is more useful for engineering. It is demonstrated that there is no simple, accurate and feasible analytical method for thawing time prediction. Consequently, simplified methods are needed for thawing time estimation of agricultural and food products. The review reveals the need for further improvement of the existing solutions or development of new ones that will enable accurate determination of thawing time within a wide range of practical conditions of heat transfer during processing. PMID:27904387

  4. Review of Thawing Time Prediction Models Depending
on Process Conditions and Product Characteristics.

    PubMed

    Góral, Dariusz; Kluza, Franciszek; Spiess, Walter E L; Kozłowicz, Katarzyna

    2016-03-01

    Determining thawing times of frozen foods is a challenging problem as the thermophysical properties of the product change during thawing. A number of calculation models and solutions have been developed. The proposed solutions range from relatively simple analytical equations based on a number of assumptions to a group of empirical approaches that sometimes require complex calculations. In this paper analytical, empirical and graphical models are presented and critically reviewed. The conditions of solution, limitations and possible applications of the models are discussed. The graphical and semi--graphical models are derived from numerical methods. Using the numerical methods is not always possible as running calculations takes time, whereas the specialized software and equipment are not always cheap. For these reasons, the application of analytical-empirical models is more useful for engineering. It is demonstrated that there is no simple, accurate and feasible analytical method for thawing time prediction. Consequently, simplified methods are needed for thawing time estimation of agricultural and food products. The review reveals the need for further improvement of the existing solutions or development of new ones that will enable accurate determination of thawing time within a wide range of practical conditions of heat transfer during processing.

  5. Prediction of glass durability as a function of glass composition and test conditions: Thermodynamics and kinetics

    SciTech Connect

    Jantzen, C M

    1988-01-01

    The long-term durability of nuclear waste glasses can be predicted by comparing their performance to natural and ancient glasses. Glass durability is a function of the kinetic and thermodynamic stability of glass in solution. The relationship between the kinetic and thermodynamic aspects of glass durability can be understood when the relative contributions of glass composition and imposed test conditions are delineated. Glass durability has been shown to be a function of the thermodynamic hydration free energy which can be calculated from the glass composition. Hydration thermodynamics also furnishes a quantitative frame of reference to understand how various test parameters affect glass durability. Linear relationships have been determined between the logarithmic extent of hydration and the calculated hydration free energy for several different test geometries. Different test conditions result in different kinetic reactivity parameters such as the exposed glass surface area (SA), the leachant solution volume (V), and the length of time that the glass is in the leachant (t). Leachate concentrations are known to be a function of the kinetic test parameter (SAV)t. The relative durabilities of glasses, including pure silica, obsidians, nuclear waste glasses, medieval window glasses, and frit glasses define a plane in three dimensional ..delta..G/sub hyd/-concentration-(SAV)t space. At constant kinetic conditions, e.g., test geometry and test duration, the three dimensional plane is intersected at constant (SAV)t and the ..delta..G/sub hyd/-concentration plots have similar slopes. The slope represents the natural logarithm of the theoretical slope, (12.303 RT), for the rate of glass dissolution. 53 refs., 4 figs.

  6. Geochemical Predictions of Elemental Compositions using Remote LIBS under Mars Conditions

    NASA Astrophysics Data System (ADS)

    Dyar, M. D.; Tucker, J.; Humphries, S.; Clegg, S. M.; Wiens, R. C.; Carmosino, M. L.

    2010-12-01

    The ChemCam instrument on Mars Science Laboratory will be the first deployment of laser-induced breakdown spectroscopy (LIBS) for remote geochemical analysis. Successful quantitative analyses of those results will use in-situ calibration targets and laboratory calibrations, and employ sophisticated algorithms for data reduction in order to correct for variations in peak intensities and areas caused by interactions in the plasma that are a function of chemical composition. Such chemical matrix effects influence the ratio of each emission line to the abundance of the element that produces it, and are directly related to the elemental composition of the sample. Advances in statistical analysis of LIBS data that mitigate matrix effects and provide for accurate and precise bulk analysis of major, minor, and trace elements are reported here. Our in-house data set currently includes LIBS spectra of >140 rock powders (igneous, metamorphic, and sedimentary) with highly-varying compositions (as determined by XRF) that were acquired at 7-9 m standoff distance under Mars atmospheric conditions using a laboratory instrument [1]. LIBS spectra were modeled using partial least squares analysis (PLS) to predict elemental compositions. Within the igneous suite, 10 repeat measurements of a single sample demonstrates consistency and precision; calculated 1-σ errors were 1.6 wt.%SiO2, 1.5 wt.% Al2O3, 0.4 wt.% TiO2, 1.2 wt.% Fe2O3T, 1.6 wt.% MgO, 0.02 wt.% MnO, 1.1 wt.% CaO, 0.5 wt.% Na2O, 0.2 wt.% P2O5, and 0.4 wt.% K2O. In the overall suite, predictions of all elements, expressed as root mean square errors (RMSEP), are better than ±2.45 for SiO2, ±1.64 for Al2O3, ±0.38 for TiO2, ±1.50 for Fe2O3T, ±1.88 for MgO, ±0.03 for MnO, ±0.82 for CaO, ±0.55 for K2O, ±0.62 for Na2O, and ±0.24 for P2O5 in units of wt.% oxides. On-going work should reduce these values even further. For elements at low concentrations, multivariate analyses must be interpreted with care because their

  7. Probabilistic prediction of hydrologic drought using a conditional probability approach based on the meta-Gaussian model

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.; Sun, Alexander Y.; Xia, Youlong

    2016-11-01

    Prediction of drought plays an important role in drought preparedness and mitigation, especially because of large impacts of drought and increasing demand for water resources. An important aspect for improving drought prediction skills is the identification of drought predictability sources. In general, a drought originates from precipitation deficit and thus the antecedent meteorological drought may provide predictive information for other types of drought. In this study, a hydrological drought (represented by Standardized Runoff Index (SRI)) prediction method is proposed based on the meta-Gaussian model taking into account the persistence and its prior meteorological drought condition (represented by Standardized Precipitation Index (SPI)). Considering the inherent nature of standardized drought indices, the meta-Gaussian model arises as a suitable model for constructing the joint distribution of multiple drought indices. Accordingly, the conditional distribution of hydrological drought can be derived analytically, which enables the probabilistic prediction of hydrological drought in the target period and uncertainty quantifications. Based on monthly precipitation and surface runoff of climate divisions of Texas, U.S., 1-month and 2-month lead predictions of hydrological drought are illustrated and compared to the prediction from Ensemble Streamflow Prediction (ESP). Results, based on 10 climate divisions in Texas, show that the proposed meta-Gaussian model provides useful drought prediction information with performance depending on regions and seasons.

  8. Maturity of judgement in decision making for predictive testing for nontreatable adult-onset neurogenetic conditions: a case against predictive testing of minors.

    PubMed

    Richards, F H

    2006-11-01

    International guidelines developed to minimize harm from predictive testing for adult-onset, nontreatable neurogenetic conditions such as Huntington disease (HD) state that such testing should not be available to minors. Some authors have proposed that predictive testing for these conditions should be available to minors at the request of parents and/or of younger adolescents themselves. They highlight the lack of empirical evidence that predictive testing of minors causes harm and suggest that refusing to test minors may be detrimental. The current study focuses on the context of predictive test requests by adolescents younger than 18 years, and presents arguments and evidence that the risk of potential harm from testing such young people is sufficiently high to justify continued caution in this area. A study based on a model of psychosocial maturity found that the 3 factors involved in maturity of judgement in decision making - responsibility, temperance and perspective - continue to develop into late adolescence. There is also evidence that the prefrontal areas of the brain, which are involved in executive functions such as decision making, are not fully developed until early adulthood. Combined with evidence of adverse long-term effects, from research with adults who have undergone predictive testing, these findings constitute grounds for retaining a minimum age of 18 years for predictive testing for nontreatable conditions. Further research on assessment of maturity will assist with reaching a consensus on this issue.

  9. Cervical insulin-like growth factor binding protein-1 (IGFBP-1) to predict spontaneous onset of labor and induction to delivery interval in post-term pregnancy.

    PubMed

    Dögl, Malin; Skogvoll, Eirik; Heimstad, Runa

    2011-01-01

    To evaluate whether insulin-like growth factor binding protein-1 (IGFBP-1) assessed in cervical secretion can predict successful induction and spontaneous onset of labor in post-term pregnancy, compared to ultrasound measurement of cervical length and Bishop score. Cohort study, originating from a randomized controlled trial. Obstetric department of a university and tertiary referral hospital, Norway. Five hundred and eight post-term women who had been randomized to induction of labor or expectant management 1 week beyond estimated day of delivery (289 [±2] days of gestation). Time to delivery was related to presence of IGFBP-1 in cervical secretion, Bishop score and ultrasound measurement of cervical length recorded at inclusion. Spontaneous onset of labor and delivery within 3 days in the expectant management, and delivery within 24 hours of induction in the induction group. Test characteristics (sensitivity, specificity and negative and positive values and likelihood ratios) for IGFBP-1, Bishop score and cervical length were calculated. Logistic regression and Cox regression were used to account for parity and body mass index. With expectant management, IGFBP-1 predicted spontaneous labor onset and delivery within 72 hours with low sensitivity and high specificity (0.45 and 0.80, respectively), as did Bishop score (0.24, 0.92). Cervical length was more sensitive (0.67, 0.58). IGFBP-1 predicted successful induction within 24 hours with low sensitivity and high specificity (0.30, 0.85), such as Bishop score (0.06, 1.00) and cervical length (0.45, 0.76). Parity enhanced successful induction. IGFBP-1 predicts both spontaneous labor onset and successful induction in post-term pregnancy. Bishop score and cervical length performed equally well. © 2010 The Authors Acta Obstetricia et Gynecologica Scandinavica© 2010 Nordic Federation of Societies of Obstetrics and Gynecology.

  10. Interval arithmetic in calculations

    NASA Astrophysics Data System (ADS)

    Bairbekova, Gaziza; Mazakov, Talgat; Djomartova, Sholpan; Nugmanova, Salima

    2016-10-01

    Interval arithmetic is the mathematical structure, which for real intervals defines operations analogous to ordinary arithmetic ones. This field of mathematics is also called interval analysis or interval calculations. The given math model is convenient for investigating various applied objects: the quantities, the approximate values of which are known; the quantities obtained during calculations, the values of which are not exact because of rounding errors; random quantities. As a whole, the idea of interval calculations is the use of intervals as basic data objects. In this paper, we considered the definition of interval mathematics, investigated its properties, proved a theorem, and showed the efficiency of the new interval arithmetic. Besides, we briefly reviewed the works devoted to interval analysis and observed basic tendencies of development of integral analysis and interval calculations.

  11. Stagnation-point heat-transfer rate predictions at aeroassist flight conditions

    NASA Technical Reports Server (NTRS)

    Gupta, Roop N.; Jones, Jim J.; Rochelle, William C.

    1992-01-01

    The results are presented for the stagnation-point heat-transfer rates used in the design process of the Aeroassist Flight Experiment (AFE) vehicle over its entire aeropass trajectory. The prediction methods used in this investigation demonstrate the application of computational fluid dynamics (CFD) techniques to a wide range of flight conditions and their usefulness in a design process. The heating rates were computed by a viscous-shock-layer (VSL) code at the lower altitudes and by a Navier-Stokes (N-S) code for the higher altitude cases. For both methods, finite-rate chemically reacting gas was considered, and a temperature-dependent wall-catalysis model was used. The wall temperature for each case was assumed to be radiative equilibrium temperature, based on total heating. The radiative heating was estimated by using a correlation equation. Wall slip was included in the N-S calculation method, and this method implicitly accounts for shock slip. The N-S/VSL combination of projection methods was established by comparison with the published benchmark flow-field code LAURA results at lower altitudes, and the direct simulation Monte Carlo results at higher altitude cases. To obtain the design heating rate over the entire forward face of the vehicle, a boundary-layer method (BLIMP code) that employs reacting chemistry and surface catalysis was used. The ratio of the VSL or N-S method prediction to that obtained from the boundary-layer method code at the stagnation point is used to define an adjustment factor, which accounts for the errors involved in using the boundary-layer method.

  12. In vitro simulation of pathological bone conditions to predict clinical outcome of bone tissue engineered materials

    NASA Astrophysics Data System (ADS)

    Nguyen, Duong Thuy Thi

    According to the Centers for Disease Control, the geriatric population of ≥65 years of age will increase to 51.5 million in 2020; 40% of white women and 13% of white men will be at risk for fragility fractures or fractures sustained under normal stress and loading conditions due to bone disease, leading to hospitalization and surgical treatment. Fracture management strategies can be divided into pharmaceutical therapy, surgical intervention, and tissue regeneration for fracture prevention, fracture stabilization, and fracture site regeneration, respectively. However, these strategies fail to accommodate the pathological nature of fragility fractures, leading to unwanted side effects, implant failures, and non-unions. Compromised innate bone healing reactions of patients with bone diseases are exacerbated with protective bone therapy. Once these patients sustain a fracture, bone healing is a challenge, especially when fracture stabilization is unsuccessful. Traditional stabilizing screw and plate systems were designed with emphasis on bone mechanics rather than biology. Bone grafts are often used with fixation devices to provide skeletal continuity at the fracture gap. Current bone grafts include autologous bone tissue and donor bone tissue; however, the quality and quantity demanded by fragility fractures sustained by high-risk geriatric patients and patients with bone diseases are not met. Consequently, bone tissue engineering strategies are advancing towards functionalized bone substitutes to provide fracture reconstruction while effectively mediating bone healing in normal and diseased fracture environments. In order to target fragility fractures, fracture management strategies should be tailored to allow bone regeneration and fracture stabilization with bioactive bone substitutes designed for the pathological environment. The clinical outcome of these materials must be predictable within various disease environments. Initial development of a targeted

  13. A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

    PubMed Central

    Vergara-Díaz, Omar; Zaman-Allah, Mainassara A.; Masuka, Benhildah; Hornero, Alberto; Zarco-Tejada, Pablo; Prasanna, Boddupalli M.; Cairns, Jill E.; Araus, José L.

    2016-01-01

    Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R2~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization. PMID:27242867

  14. Donor Chimerism Early after Reduced-intensity Conditioning Hematopoietic Stem Cell Transplantation Predicts Relapse and Survival

    PubMed Central

    Koreth, John; Kim, Haesook T.; Nikiforow, Sarah; Milford, Edgar L.; Armand, Philippe; Cutler, Corey; Glotzbecker, Brett; Ho, Vincent T.; Antin, Joseph H.; Soiffer, Robert J.; Ritz, Jerome; Alyea, Edwin P.

    2015-01-01

    The impact of early donor cell chimerism on outcomes of T-replete reduced-intensity conditioning (RIC) hematopoietic stem cell transplantation (HSCT) is ill-defined. We evaluated day 30 (D30) and 100 (D100) total donor cell chimerism after RIC HSCT undertaken between 2002 and 2010 at our institution, excluding patients who died or relapsed before D30. When available, donor T-cell chimerism was also assessed. The primary outcome was overall survival (OS). Secondary outcomes included progression-free survival (PFS), relapse and non-relapse mortality (NRM). 688 patients with hematologic malignancies (48% myeloid; 52% lymphoid) and a median age of 57 years (range, 18-74) undergoing RIC HSCT with T-replete donor grafts (97% peripheral blood; 92% HLA-matched) and median follow-up of 58.2 months (range, 12.6-120.7) were evaluated. In multivariable analysis total donor cell and T-cell chimerism at D30 and D100 each predicted RIC HSCT outcomes, with D100 total donor cell chimerism most predictive. D100 total donor cell chimerism <90% was associated with increased relapse (HR 2.54, 95% CI 1.83-3.51, p<0.0001), impaired PFS (HR 2.01, 95% CI 1.53-2.65, p<0.0001) and worse OS (1.50, 95% CI 1.11-2.04, p=0.009), but not NRM (HR 0.76; 95% CI 0.44-2.27, p=0.33). There was no additional utility of incorporating sustained D30-D100 total donor cell chimerism, or T-cell chimerism. Low donor chimerism early after RIC HSCT is an independent risk factor for relapse and impaired survival. Donor chimerism assessment early after RIC HSCT can prognosticate for long-term outcomes and help identify high-risk patient cohorts that may benefit from additional therapeutic interventions. PMID:24907627

  15. An ENSO prediction approach based on ocean conditions and ocean-atmosphere coupling

    NASA Astrophysics Data System (ADS)

    Tseng, Yu-heng; Hu, Zeng-Zhen; Ding, Ruiqiang; Chen, Han-ching

    2017-03-01

    A simple statistical model for the El Niño-Southern Oscillation (ENSO) prediction is derived based on the evolution of the ocean heat condition and the oceanic Kelvin wave propagation associated with westerly wind events (WWEs) and easterly wind surges (EWSs) in the tropical Pacific. The multivariate linear regression model solely relies on the pentad thermocline depth anomaly evolution in 25 days along with the zonal surface wind modulation. It successfully hindcasts all ENSOs except for the 2000/01 La Niña, using the pentad (or monthly) mean tropical atmosphere ocean array data since 1994 with an averaged skill (measured by anomaly correlation) of 0.62 (or 0.67) with a 6-month lead. The exception is mainly due to the long-lasting cold sea surface temperature anomalies in the subtropics resulting from the strong 1998/99 La Niña, even though the tropical warm water volume (WWV) had rebounded and turned phases after 2000. We also note that the hindcast skill is comparable using pentad or monthly mean NCEP global ocean data assimilation system data for the same time period. The hindcast skill of the proposed statistical model is better than that based on the WWV index in terms of the monthly correlation, normalized RMSEs and ENSO occurrences, which suggest that including the evolution of the subsurface ocean temperature anomaly and the WWEs/EWSs in the central tropical Pacific can enhance the ability to predict ENSO. The hindcast skill is also comparable to the predictions using other dynamical and statistical models, indicating that these processes are the keys to ENSO development. The dynamics behind the statistical model are consistent with the physical processes of ENSO development as follows: the tropical WWV resulting from the interannually-varying meridional subtropical cell transport provides a sufficient heat source. When the seasonal phase lock of ocean-atmosphere coupling triggers the positive (negative) zonal wind anomaly in boreal summer and fall, an

  16. From Pavlov to pain: How predictability affects the anticipation and processing of visceral pain in a fear conditioning paradigm.

    PubMed

    Labrenz, Franziska; Icenhour, Adriane; Schlamann, Marc; Forsting, Michael; Bingel, Ulrike; Elsenbruch, Sigrid

    2016-04-15

    Conditioned pain-related fear may contribute to hyperalgesia and central sensitization, but this has not been tested for interoceptive, visceral pain. The underlying ability to accurately predict pain is based on predictive cue properties and may alter the sensory processing and cognitive-emotional modulation of pain thus exacerbating the subjective pain experience. In this functional magnetic resonance imaging study using painful rectal distensions as unconditioned stimuli (US), we addressed changes in the neural processing of pain during the acquisition of pain-related fear and subsequently tested if conditioned stimuli (CS) contribute to hyperalgesia and increased neural responses in pain-encoding regions. N=49 healthy volunteers were assigned to one of two groups and underwent 3T fMRI during acquisition of either differential fear conditioning (predictable) or non-contingent presentation of CS and US (unpredictable). During a subsequent test phase, pain stimuli signaled randomly by the CSs were delivered. For the acquisition, results confirmed differential conditioning in the predictable but not the unpredictable group. With regard to activation in response to painful stimuli, the unpredictable compared to the predictable group revealed greater activation in pain-encoding (somatosensory cortex, insula) and pain-modulatory (prefrontal and cingulate cortices, periaqueductal grey, parahippocampus) regions. In the test phase, no evidence of hyperalgesia or central sensitization was found, but the predictable group demonstrated enhanced caudate nucleus activation in response to CS(-)-signaled pain. These findings support that during fear conditioning, the ability to predict pain affects neural processing of visceral pain and alters the associative learning processes underlying the acquisition of predictive properties of cues signaling pain, but conditioned pain-related fear does not result in visceral hyperalgesia or central sensitization. Copyright © 2016 Elsevier

  17. Towards national mapping of aquatic condition (II): Predicting the probable biological condition of USA streams and rivers

    EPA Science Inventory

    The US EPA’s National River and Stream Assessment (NRSA) uses spatially balanced sampling to estimate the proportion of streams within the conterminous US (CONUS) that deviate from least-disturbed biological condition (BC). These assessments do not infer BC at un-sampled st...

  18. Towards national mapping of aquatic condition (II): Predicting the probable biological condition of USA streams and rivers

    EPA Science Inventory

    The US EPA’s National River and Stream Assessment (NRSA) uses spatially balanced sampling to estimate the proportion of streams within the conterminous US (CONUS) that deviate from least-disturbed biological condition (BC). These assessments do not infer BC at un-sampled st...

  19. Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions

    NASA Astrophysics Data System (ADS)

    Fiala, D.; Lomas, K. J.; Stohrer, M.

    A mathematical model for predicting human thermal and regulatory responses in cold, cool, neutral, warm, and hot environments has been developed and validated. The multi-segmental passive system, which models the dynamic heat transport within the body and the heat exchange between body parts and the environment, is discussed elsewhere. This paper is concerned with the development of the active system, which simulates the regulatory responses of shivering, sweating, and peripheral vasomotion of unacclimatised subjects. Following a comprehensive literature review, 26 independent experiments were selected that were designed to provoke each of these responses in different circumstances. Regression analysis revealed that skin and head core temperature affect regulatory responses in a non-linear fashion. A further signal, i.e. the rate of change of the mean skin temperature weighted by the skin temperature error signal, was identified as governing the dynamics of thermoregulatory processes in the cold. Verification and validation work was carried out using experimental data obtained from 90 exposures covering a range of steady and transient ambient temperatures between 5°C and 50°C and exercise intensities between 46 W/m2 and 600 W/m2. Good general agreement with measured data was obtained for regulatory responses, internal temperatures, and the mean and local skin temperatures of unacclimatised humans for the whole spectrum of climatic conditions and for different activity levels.

  20. Predicting aversive events and terminating fear in the mouse anterior cingulate cortex during trace fear conditioning.

    PubMed

    Steenland, Hendrik W; Li, Xiang-Yao; Zhuo, Min

    2012-01-18

    A variety of studies have implicated the anterior cingulate cortex (ACC) in fear, including permanent storage of fear memory. Recent pharmacological and genetic studies indicate that early synaptic plasticity in the ACC may also contribute to certain forms of fear memory at early time points. However, no study has directly examined the possible changes in neuronal activity of ACC neurons in freely behaving mice during early learning. In the present study, we examined the neural responses of the ACC during trace fear conditioning. We found that ACC putative pyramidal and nonpyramidal neurons were involved in the termination of fear behavior ("un-freezing"), and the spike activity of these neurons was reduced during freezing. Some of the neurons were also found to acquire un-freezing locked activity and change their tuning. The results implicate the ACC neurons in fear learning and controlling the abolition of fear behavior. We also show that the ACC is important for making cue-related fear memory associations in the trace fear paradigm as measured with tone-evoked potentials and single-unit activity. Collectively, our findings indicate that the ACC is involved in predicting future aversive events and terminating fear during trace fear.

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

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  2. Prediction of temperature distribution in sericite mica drying with variable temperature and airflow condition

    NASA Astrophysics Data System (ADS)

    Noh, A. Mohd; Mat, S.; Roslan, M. H.; Salleh, E.

    2017-06-01

    To develop new drying facilities, it was important to know the impact of every input parameter to the drying process. Using a real prototype to carry out the experiment required high cost and time consuming especially for large scale drying. CFD simulation approached was one of the solution. Previous study of drying simulation only focuses on the fix value of the input parameter. This paper presents the result of CFD simulation to predict the heat distribution in sericite mica drying with variable temperature and airflow condition. Variable temperature and airflow was used because the only heat source for the dryer was from the solar energy therefore it’s only available in the day time. The analysis was carried out for 24 hours of drying time. The simulation result shows that the temperature inside the sericite mica increase 8 to 10°C when the solar energy is available and it is still increasing about 4 to 7°C for 5 hours after the solar energy is absent. The result also shows that during the drying time the temperature of sericite mica that is closer to the heat source was higher compared to the one that is further away with the maximum difference of 3.8°C.

  3. Predictive gaze during observation of irrational actions in adults with autism spectrum conditions.

    PubMed

    Marsh, L E; Pearson, A; Ropar, D; Hamilton, A F de C

    2015-01-01

    Understanding irrational actions may require the observer to make mental state inferences about why an action was performed. Individuals with autism spectrum conditions (ASC) have well documented difficulties with mentalizing; however, the degree to which rationality understanding is impaired in autism is not yet clear. The present study uses eye-tracking to measure online understanding of action rationality in individuals with ASC. Twenty adults with ASC and 20 typically developing controls, matched for age and IQ watched movies of rational and irrational actions while their eye movements were recorded. Measures of looking time, scan path and saccade latency were calculated. Results from looking time and scan path analyses demonstrate that participants with ASC have reduced visual attention to salient action features such as the action goal and the hand performing the action, regardless of action rationality. However, when participants with ASC do attend to these features, they are able to make anticipatory goal saccades as quickly as typically developing controls. Taken together these results indicate that individuals with autism have reduced attention to observed actions, but when attention is maintained, goal prediction is typical. We conclude that the basic mechanisms of action understanding are intact in individuals with ASC although there may be impairment in the top-down, social modulation of eye movements.

  4. Prediction of atmospheric conditions at ExoMars 2016 landing site with global circulation model simulations

    NASA Astrophysics Data System (ADS)

    Temel, Orkun; Karatekin, Ozgur; van Beeck, Jeroen

    2017-04-01

    On the framework of the ExoMars programme, Schiaparelli lander was planned to land on Meridiani Planum during the dust storm season corresponding to the solar longitude of 244.7. The lander crashed at 2.1 degrees south and 6.2 degrees west. The images taken from NASA orbiter has shown that the parachute is located at 1 km south of the impact point of Schiaparelli lander, indicating the fact that wind direction was from north to south. Due to the flat topography of Meridiani Planum region, global circulation model simulations might provide reasonable estimations of atmospheric conditions without performing nested mesoscale simulations. In this present study, simulations have been performed with planetWRF, the extended version of theWeather Research and Forecasting model (WRF) for the extraterrestrial atmospheres. Diurnal variation of wind speed and wind direction along with other flow quantities have been investigated for the corresponding Martian day (sol 476). The sensitivity of predictions to modelling parameters have also been evaluated.

  5. Experimental study of tyre/road contact forces in rolling conditions for noise prediction

    NASA Astrophysics Data System (ADS)

    Cesbron, Julien; Anfosso-Lédée, Fabienne; Duhamel, Denis; Ping Yin, Hai; Le Houédec, Donatien

    2009-02-01

    This paper deals with the experimental study of dynamical tyre/road contact for noise prediction. In situ measurements of contact forces and close proximity noise levels were carried out for a slick tyre rolling on six different road surfaces between 30 and 50 km/h. Additional texture profiles of the tested surfaces were taken on the wheel track. Normal contact stresses were measured at a sampling frequency of 10752 Hz using a line of pressure sensitive cells placed both along and perpendicular to the rolling direction. The contact areas obtained during rolling were smaller than in static conditions. This is mainly explained by the dynamical properties of tyre compounds, like the viscoelastic behaviour of the rubber. Additionally the root-mean-square of the resultant contact forces at various speeds was in the same order for a given road surface, while their spectra were quite different. This is certainly due to a spectral influence of bending waves propagating in the tyre during rolling, especially when the wavelength is small in comparison with the size of the contact patch. Finally, the levels of contact forces and close proximity noise measured at 30 km/h were correlated. Additional correlations with texture levels were performed. The results show that the macro-texture generates contact forces linearly around 800 Hz and consequently noise levels between 500 and 1000 Hz via the vibrations transmitted to the tyre.

  6. Predictive analysis of landslide susceptibility in the Kao-Ping watershed, Taiwan under climate change conditions

    NASA Astrophysics Data System (ADS)

    Shou, K. J.; Wu, C. C.; Lin, J. F.

    2015-01-01

    Among the most critical issues, climatic abnormalities caused by global warming also affect Taiwan significantly for the past decade. The increasing frequency of extreme rainfall events, in which concentrated and intensive rainfalls generally cause geohazards including landslides and debris flows. The extraordinary Typhoon Morakot hit Southern Taiwan on 8 August 2009 and induced serious flooding and landslides. In this study, the Kao-Ping River watershed was adopted as the study area, and the typical events 2007 Krosa Typhoon and 2009 Morakot Typhoon were adopted to train the susceptibility model. This study employs rainfall frequency analysis together with the atmospheric general circulation model (AGCM) downscaling estimation to understand the temporal rainfall trends, distributions, and intensities in the Kao-Ping River watershed. The rainfall estimates were introduced in the landslide susceptibility model to produce the predictive landslide susceptibility for various rainfall scenarios, including abnormal climate conditions. These results can be used for hazard remediation, mitigation, and prevention plans for the Kao-Ping River watershed.

  7. Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions.

    PubMed

    Fiala, D; Lomas, K J; Stohrer, M

    2001-09-01

    A mathematical model for predicting human thermal and regulatory responses in cold, cool, neutral, warm, and hot environments has been developed and validated. The multi-segmental passive system, which models the dynamic heat transport within the body and the heat exchange between body parts and the environment, is discussed elsewhere. This paper is concerned with the development of the active system, which simulates the regulatory responses of shivering, sweating, and peripheral vasomotion of unacclimatised subjects. Following a comprehensive literature review, 26 independent experiments were selected that were designed to provoke each of these responses in different circumstances. Regression analysis revealed that skin and head core temperature affect regulatory responses in a nonlinear fashion. A further signal, i.e. the rate of change of the mean skin temperature weighted by the skin temperature error signal, was identified as governing the dynamics of thermoregulatory processes in the cold. Verification and validation work was carried out using experimental data obtained from 90 exposures covering a range of steady and transient ambient temperatures between 5 degrees C and 50 degrees C and exercise intensities between 46 W/m2 and 600 W/m2. Good general agreement with measured data was obtained for regulatory responses, internal temperatures, and the mean and local skin temperatures of unacclimatised humans for the whole spectrum of climatic conditions and for different activity levels.

  8. Interval probabilistic neural network.

    PubMed

    Kowalski, Piotr A; Kulczycki, Piotr

    2017-01-01

    Automated classification systems have allowed for the rapid development of exploratory data analysis. Such systems increase the independence of human intervention in obtaining the analysis results, especially when inaccurate information is under consideration. The aim of this paper is to present a novel approach, a neural networking, for use in classifying interval information. As presented, neural methodology is a generalization of probabilistic neural network for interval data processing. The simple structure of this neural classification algorithm makes it applicable for research purposes. The procedure is based on the Bayes approach, ensuring minimal potential losses with regard to that which comes about through classification errors. In this article, the topological structure of the network and the learning process are described in detail. Of note, the correctness of the procedure proposed here has been verified by way of numerical tests. These tests include examples of both synthetic data, as well as benchmark instances. The results of numerical verification, carried out for different shapes of data sets, as well as a comparative analysis with other methods of similar conditioning, have validated both the concept presented here and its positive features.

  9. Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

    NASA Astrophysics Data System (ADS)

    Rey, M.; Nikitin, A. V.; Tyuterev, V.

    2014-06-01

    Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

  10. Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

    USGS Publications Warehouse

    Carlisle, D.M.; Falcone, J.; Meador, M.R.

    2009-01-01

    We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

  11. Modeling long period swell in Southern California: Practical boundary conditions from buoy observations and global wave model predictions

    NASA Astrophysics Data System (ADS)

    Crosby, S. C.; O'Reilly, W. C.; Guza, R. T.

    2016-02-01

    Accurate, unbiased, high-resolution (in space and time) nearshore wave predictions are needed to drive models of beach erosion, coastal flooding, and alongshore transport of sediment, biota and pollutants. On highly sheltered shorelines, wave predictions are sensitive to the directions of onshore propagating waves, and nearshore model prediction error is often dominated by uncertainty in offshore boundary conditions. Offshore islands and shoals, and coastline curvature, create complex sheltering patterns over the 250km span of southern California (SC) shoreline. Here, regional wave model skill in SC was compared for different offshore boundary conditions created using offshore buoy observations and global wave model hindcasts (National Oceanographic and Atmospheric Administration Wave Watch 3, WW3). Spectral ray-tracing methods were used to transform incident offshore swell (0.04-0.09Hz) energy at high directional resolution (1-deg). Model skill is assessed for predictions (wave height, direction, and alongshore radiation stress) at 16 nearshore buoy sites between 2000 and 2009. Model skill using buoy-derived boundary conditions is higher than with WW3-derived boundary conditions. Buoy-driven nearshore model results are similar with various assumptions about the true offshore directional distribution (maximum entropy, Bayesian direct, and 2nd derivative smoothness). Two methods combining offshore buoy observations with WW3 predictions in the offshore boundary condition did not improve nearshore skill above buoy-only methods. A case example at Oceanside harbor shows strong sensitivity of alongshore sediment transport predictions to different offshore boundary conditions. Despite this uncertainty in alongshore transport magnitude, alongshore gradients in transport (e.g. the location of model accretion and erosion zones) are determined by the local bathymetry, and are similar for all predictions.

  12. Predicting falls in elderly patients with chronic pain and other chronic conditions.

    PubMed

    Lazkani, Aida; Delespierre, Tiba; Bauduceau, Bernard; Benattar-Zibi, Linda; Bertin, Philippe; Berrut, Gilles; Corruble, Emmanuelle; Danchin, Nicolas; Derumeaux, Geneviève; Doucet, Jean; Falissard, Bruno; Forette, Francoise; Hanon, Olivier; Pasquier, Florence; Pinget, Michel; Ourabah, Rissane; Piedvache, Celine; Becquemont, Laurent

    2015-10-01

    The aim was to identify fall predictors in elderly suffering from chronic pain (CP) and to test their applicability among patients with other chronic conditions. 1,379 non-institutionalized patients aged 65 years and older who were suffering from CP (S.AGE CP sub-cohort) were monitored every 6 months for 1 year. Socio-demographic, clinical and pain data and medication use were assessed at baseline for the association with falls in the following year. Falls were assessed retrospectively at each study visit. Logistic regression analyses were performed to identify fall predictors. The derived model was applied to two additional S.AGE sub-cohorts: atrial fibrillation (AF) (n = 1,072) and type-2 diabetes mellitus (T2DM) (n = 983). Four factors predicted falls in the CP sub-cohort: fall history (OR: 4.03, 95 % CI 2.79-5.82), dependency in daily activities (OR: 1.81, 95 % CI 1.27-2.59), age ≥75 (OR: 1.53, 95 % CI 1.04-2.25) and living alone (OR: 1.73, 95 % CI 1.24-2.41) (Area Under the Curve: AUC = 0.71, 95 % CI 0.67-0.75). These factors were relevant in AF (AUC = 0.71, 95 % CI 0.66-0.75) and T2DM (AUC = 0.67, 95 % CI 0.59-0.73) sub-cohorts. Fall predicted probability in CP, AF and T2DM sub-cohorts increased from 7, 7 and 6 % in patients with no risk factors to 59, 66 and 45 % respectively, in those with the four predictors. Fall history was the strongest predictor in the three sub-cohorts. Fall history, dependency in daily activities, age ≥75 and living alone are independent fall predictors in CP, AF and T2DM patients.

  13. CADDIS Volume 4. Data Analysis: Predicting Environmental Conditions from Biological Observations (PECBO Appendix)

    EPA Pesticide Factsheets

    Overview of PECBO Module, using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, methods for inferring environmental conditions, statistical scripts in module.

  14. Modelling volatility recurrence intervals in the Chinese commodity futures market

    NASA Astrophysics Data System (ADS)

    Zhou, Weijie; Wang, Zhengxin; Guo, Haiming

    2016-09-01

    The law of extreme event occurrence attracts much research. The volatility recurrence intervals of Chinese commodity futures market prices are studied: the results show that the probability distributions of the scaled volatility recurrence intervals have a uniform scaling curve for different thresholds q. So we can deduce the probability distribution of extreme events from normal events. The tail of a scaling curve can be well fitted by a Weibull form, which is significance-tested by KS measures. Both short-term and long-term memories are present in the recurrence intervals with different thresholds q, which denotes that the recurrence intervals can be predicted. In addition, similar to volatility, volatility recurrence intervals also have clustering features. Through Monte Carlo simulation, we artificially synthesise ARMA, GARCH-class sequences similar to the original data, and find out the reason behind the clustering. The larger the parameter d of the FIGARCH model, the stronger the clustering effect is. Finally, we use the Fractionally Integrated Autoregressive Conditional Duration model (FIACD) to analyse the recurrence interval characteristics. The results indicated that the FIACD model may provide a method to analyse volatility recurrence intervals.

  15. Wave-current interaction in the bottom boundary layer during storm and non-storm conditions: observations and model predictions

    USGS Publications Warehouse

    Drake, D.E.; Cacchione, D.A.

    1992-01-01

    Bottom boundary layer measurements of current velocity profiles and bed response under combined wave and current conditions were obtained at a water depth of 145 m on the shelf off central California during December 1988. High quality logarithmic current profiles, excellent time-series bottom photographs, and a large variation in the relative strengths of the wave-induced oscillatory currents and the quasi-steady low frequency currents provided a dataset that is ideal for examining the effects of wave-current interaction near a rough boundary. During one period of 3 days that included a brief storm event, the wave-induced bottom currents (Ub 1 10) ranged from 2.3 to 22 cm s-1 and the steady currents (Ur) ranged from 1.8 to 28.1 cm s-1 at 0.18 m above the bottom; the ratio Ub U18 varied from below 0.2 to more than 7. Velocity profiles were highly logarithmic (R2 > 0.95) 60% of the time and 27 profiles collected at 2-h intervals had R2 {slanted equal to or greater-than} 0.994 which allowed reliable estimates of the current shear velocity (U*c) and roughness length (zoc). Mean U*c values had magnitudes of 0.3-2.4 cm s-1 and zoc, which ranged from 0.04 to 3.5 cm, was strongly correlated to the Ub U18 ratio. Drag coefficients (CD = ??c/??U1002) ranged from about 2.5 ?? 10-3-12 ?? 10-3 in direct response to the wave-current variation; the use of a constant CD of 3 ?? 10-3 for steady flow over a rough bed would have underpredicted the shear stress by up to four times during the storm event. The large zoc and U*c values cannot be explained by changes in the carefully-observed, small (<1 cm) physical bed roughness elements that covered the mud-rich study site. A side-scan sonar site survey also eliminated the possibility of flow disturbance by larger upstream topography. The observations clearly demonstrate the importance of wave-current interaction near a rough boundary. Comparison of the observations with results of the combined flow models of Grant and Madsen and Glenn

  16. Once is too much: Conditioned aversion develops immediately and predicts future cocaine self-administration behavior in rats

    PubMed Central

    Colechio, Elizabeth M.; Imperio, Caesar G.; Grigson, Patricia S.

    2014-01-01

    Rats emit aversive taste reactivity (TR) behavior (i.e., gapes) following intraoral delivery of a cocaine-paired taste cue and greater conditioned aversive TR at the end of training predicts greater drug-seeking and taking. Here, we examined the development of this conditioned aversive TR behavior on a trial by trial basis in an effort to determine when the change in behavior occurs and whether early changes in this behavior can be used to predict later drug-taking. The results show that conditioned aversive TR to a cocaine-paired cue occurs very early in training (i.e., following as few as 1 – 2 taste-drug pairings) and, importantly, that it can be used to predict later drug-seeking and drug-taking in rats. PMID:24773440

  17. Once is too much: conditioned aversion develops immediately and predicts future cocaine self-administration behavior in rats.

    PubMed

    Colechio, Elizabeth M; Imperio, Caesar G; Grigson, Patricia S

    2014-04-01

    Rats emit aversive taste reactivity (TR) behavior (i.e., gapes) following intraoral delivery of a cocaine-paired taste cue and greater conditioned aversive TR at the end of training predicts greater drug-seeking and taking. Here, we examined the development of this conditioned aversive TR behavior on a trial-by-trial basis in an effort to determine when the change in behavior occurs and whether early changes in this behavior can be used to predict later drug taking. The results show that conditioned aversive TR to a cocaine-paired cue occurs very early in training (i.e., following as few as 1-2 taste-drug pairings) and, importantly, that it can be used to predict later drug seeking and drug taking in rats.

  18. Development of a noise prediction model under interrupted traffic flow conditions: a case study for Jaipur city.

    PubMed

    Agarwal, Sheetal; Swami, Bajrang L; Gupta, Akhilendra Bhushan

    2009-01-01

    The objective of this study is to develop an empirical noise prediction model for the evaluation of equivalent noise levels (Leq) under interrupted traffic flow conditions. A new factor tendency to blow horn (AH) was introduced in the conventional federal highway administrative noise prediction (FHWA) model and a comparative study was made between FHWA and modified FHWA models to evaluate the best suitability of the model. Monitoring and modeling of Leq were carried out at four selected intersections of Jaipur city. After comparison of the results, it was found that the modified FHWA model could be satisfactorily applied for Indian conditions as it gives acceptable results with a deviation of +/-3 dB (A). In addition, statistical analysis of the data comprising measured and estimated values shows a good agreement. Hence, the modified FHWA traffic noise prediction model can be applied to the cities having similar traffic conditions as in Jaipur city.

  19. Effects of lateral boundary condition resolution and update frequency on regional climate model predictions

    NASA Astrophysics Data System (ADS)

    Pankatz, Klaus; Kerkweg, Astrid

    2015-04-01

    The work presented is part of the joint project "DecReg" ("Regional decadal predictability") which is in turn part of the project "MiKlip" ("Decadal predictions"), an effort funded by the German Federal Ministry of Education and Research to improve decadal predictions on a global and regional scale. In MiKlip, one big question is if regional climate modeling shows "added value", i.e. to evaluate, if regional climate models (RCM) produce better results than the driving models. However, the scope of this study is to look more closely at the setup specific details of regional climate modeling. As regional models only simulate a small domain, they have to inherit information about the state of the atmosphere at their lateral boundaries from external data sets. There are many unresolved questions concerning the setup of lateral boundary conditions (LBC). External data sets come from global models or from global reanalysis data-sets. A temporal resolution of six hours is common for this kind of data. This is mainly due to the fact, that storage space is a limiting factor, especially for climate simulations. However, theoretically, the coupling frequency could be as high as the time step of the driving model. Meanwhile, it is unclear if a more frequent update of the LBCs has a significant effect on the climate in the domain of the RCM. The first study examines how the RCM reacts to a higher update frequency. The study is based on a 30 year time slice experiment for three update frequencies of the LBC, namely six hours, one hour and six minutes. The evaluation of means, standard deviations and statistics of the climate in the regional domain shows only small deviations, some statistically significant though, of 2m temperature, sea level pressure and precipitation. The second part of the first study assesses parameters linked to cyclone activity, which is affected by the LBC update frequency. Differences in track density and strength are found when comparing the simulations

  20. Full-field predictions of ice dynamic recrystallisation under simple shear conditions

    NASA Astrophysics Data System (ADS)

    Llorens, Maria-Gema; Griera, Albert; Bons, Paul D.; Lebensohn, Ricardo A.; Evans, Lynn A.; Jansen, Daniela; Weikusat, Ilka

    2016-09-01

    Understanding the flow of ice on the microstructural scale is essential for improving our knowledge of large-scale ice dynamics, and thus our ability to predict future changes of ice sheets. Polar ice behaves anisotropically during flow, which can lead to strain localisation. In order to study how dynamic recrystallisation affects to strain localisation in deep levels of polar ice sheets, we present a series of numerical simulations of ice polycrystals deformed under simple-shear conditions. The models explicitly simulate the evolution of microstructures using a full-field approach, based on the coupling of a viscoplastic deformation code (VPFFT) with dynamic recrystallisation codes. The simulations provide new insights into the distribution of stress, strain rate and lattice orientation fields with progressive strain, up to a shear strain of three. Our simulations show how the recrystallisation processes have a strong influence on the resulting microstructure (grain size and shape), while the development of lattice preferred orientations (LPO) appears to be less affected. Activation of non-basal slip systems is enhanced by recrystallisation and induces a strain hardening behaviour up to the onset of strain localisation and strain weakening behaviour. Simulations demonstrate that the strong intrinsic anisotropy of ice crystals is transferred to the polycrystalline scale and results in the development of strain localisation bands than can be masked by grain boundary migration. Therefore, the finite-strain history is non-directly reflected by the final microstructure. Masked strain localisation can be recognised in ice cores, such as the EDML, from the presence of stepped boundaries, microshear and grains with zig-zag geometries.

  1. National Economic Conditions and Patient Insurance Status Predict Prostate Cancer Diagnosis Rates and Management Decisions.

    PubMed

    Weiner, Adam B; Conti, Rena M; Eggener, Scott E

    2016-05-01

    The recent Great Recession from December 2007 to June 2009 presents a unique opportunity to examine whether the incidence of nonpalpable prostate cancer decreases while conservative management for nonpalpable prostate cancer increases during periods of national economic hardship. We derived rates of national monthly diagnosis and conservative management for screen detected, nonpalpable prostate cancer and patient level insurance status from the SEER (Surveillance, Epidemiology and End Results) database from 2004 to 2011. We derived monthly statistics on national unemployment rates, inflation, median household income and S&P 500® closing values from government sources. Using linear and logistic multivariable regression we measured the correlation of national macroeconomic conditions with prostate cancer diagnosis and treatment patterns. We evaluated patient level predictors of conservative management to determine whether being insured by Medicaid or uninsured increased the use of conservative management. Diagnosis rates correlated positively with the S&P 500 monthly close (coefficient 24.90, 95% CI 6.29-43.50, p = 0.009). Conservative management correlated negatively with median household income (coefficient -49.13, 95% CI -69.29--28.98, p <0.001). In a nonMedicare eligible population having Medicaid (OR 1.51, 95% CI 1.32-1.73, p <0.001) or no insurance (OR 2.27, 95% CI 1.93-2.67, p <0.001) increased the use of conservative management compared to that in men with private insurance. As indicated by a significant interaction term being diagnosed during the Great Recession increased the Medicaid insurance predictive value of conservative management (OR 1.30, 95% CI 1.02-1.68, p = 0.037). National economic hardship was associated with decreased diagnosis rates of nonpalpable prostate cancer and increased conservative management. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  2. Analysis and prediction of the critical regions of antimicrobial peptides based on conditional random fields.

    PubMed

    Chang, Kuan Y; Lin, Tung-pei; Shih, Ling-Yi; Wang, Chien-Kuo

    2015-01-01

    Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew's correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion.

  3. Use of Linear Prediction Uncertainty Analysis to Guide Conditioning of Models Simulating Surface-Water/Groundwater Interactions

    NASA Astrophysics Data System (ADS)

    Hughes, J. D.; White, J.; Doherty, J.

    2011-12-01

    Linear prediction uncertainty analysis in a Bayesian framework was applied to guide the conditioning of an integrated surface water/groundwater model that will be used to predict the effects of groundwater withdrawals on surface-water and groundwater flows. Linear prediction uncertainty analysis is an effective approach for identifying (1) raw and processed data most effective for model conditioning prior to inversion, (2) specific observations and periods of time critically sensitive to specific predictions, and (3) additional observation data that would reduce model uncertainty relative to specific predictions. We present results for a two-dimensional groundwater model of a 2,186 km2 area of the Biscayne aquifer in south Florida implicitly coupled to a surface-water routing model of the actively managed canal system. The model domain includes 5 municipal well fields withdrawing more than 1 Mm3/day and 17 operable surface-water control structures that control freshwater releases from the Everglades and freshwater discharges to Biscayne Bay. More than 10 years of daily observation data from 35 groundwater wells and 24 surface water gages are available to condition model parameters. A dense parameterization was used to fully characterize the contribution of the inversion null space to predictive uncertainty and included bias-correction parameters. This approach allows better resolution of the boundary between the inversion null space and solution space. Bias-correction parameters (e.g., rainfall, potential evapotranspiration, and structure flow multipliers) absorb information that is present in structural noise that may otherwise contaminate the estimation of more physically-based model parameters. This allows greater precision in predictions that are entirely solution-space dependent, and reduces the propensity for bias in predictions that are not. Results show that application of this analysis is an effective means of identifying those surface-water and

  4. Predicting water quality at Santa Monica Beach: evaluation of five different models for public notification of unsafe swimming conditions.

    PubMed

    Thoe, W; Gold, M; Griesbach, A; Grimmer, M; Taggart, M L; Boehm, A B

    2014-12-15

    Bathing beaches are monitored for fecal indicator bacteria (FIB) to protect swimmers from unsafe conditions. However, FIB assays take ∼24 h and water quality conditions can change dramatically in that time, so unsafe conditions cannot presently be identified in a timely manner. Statistical, data-driven predictive models use information on environmental conditions (i.e., rainfall, turbidity) to provide nowcasts of FIB concentrations. Their ability to predict real time FIB concentrations can make them more accurate at identifying unsafe conditions than the current method of using day or older FIB measurements. Predictive models are used in the Great Lakes, Hong Kong, and Scotland for beach management, but they are presently not used in California - the location of some of the world's most popular beaches. California beaches are unique as point source pollution has generally been mitigated, the summer bathing season receives little to no rainfall, and in situ measurements of turbidity and salinity are not readily available. These characteristics may make modeling FIB difficult, as many current FIB models rely heavily on rainfall or salinity. The current study investigates the potential for FIB models to predict water quality at a quintessential California Beach: Santa Monica Beach. This study compares the performance of five predictive models, multiple linear regression model, binary logistic regression model, partial least square regression model, artificial neural network, and classification tree, to predict concentrations of summertime fecal coliform and enterococci concentrations. Past measurements of bacterial concentration, storm drain condition, and tide level are found to be critical factors in the predictive models. The models perform better than the current beach management method. The classification tree models perform the best; for example they correctly predict 42% of beach postings due to fecal coliform exceedances during model validation, as compared

  5. The feedback-related negativity reflects “more or less” prediction error in appetitive and aversive conditions

    PubMed Central

    Huang, Yi; Yu, Rongjun

    2014-01-01

    Humans make predictions and use feedback to update their subsequent predictions. The feedback-related negativity (FRN) has been found to be sensitive to negative feedback as well as negative prediction error, such that the FRN is larger for outcomes that are worse than expected. The present study examined prediction errors in both appetitive and aversive conditions. We found that the FRN was more negative for reward omission vs. wins and for loss omission vs. losses, suggesting that the FRN might classify outcomes in a “more-or-less than expected” fashion rather than in the “better-or-worse than expected” dimension. Our findings challenge the previous notion that the FRN only encodes negative feedback and “worse than expected” negative prediction error. PMID:24904254

  6. Disentangling the control of tectonics, eustasy, trophic conditions and climate on shallow-marine carbonate production during the Aalenian-Oxfordian interval: From the western France platform to the western Tethyan domain

    NASA Astrophysics Data System (ADS)

    Andrieu, Simon; Brigaud, Benjamin; Barbarand, Jocelyn; Lasseur, Eric; Saucède, Thomas

    2016-11-01

    The objective of this work is to improve our understanding of the processes controlling changes in the architecture and facies of intracontinental carbonate platforms. We examined the facies and sequence stratigraphy of Aalenian to Oxfordian limestones of western France. Seventy-seven outcrop sections were studied and thirty-one sedimentary facies identified in five depositional environments ranging from lower offshore to backshore. Platform evolution was reconstructed along a 500 km cross-section. Twenty-two depositional sequences were identified on the entire western France platform and correlated with European third-order sequences at the biozone level, demonstrating that eustasy was the major factor controlling the cyclic trend of accommodation. The tectonic subsidence rate was computed from accommodation measurements from the Aalenian to the Oxfordian in key localities. Tectonism controlled the sedimentation rate and platform architecture at a longer time scale. Tectonic subsidence triggered the demise of carbonate production at the Bathonian/Callovian boundary while the uplift made possible the recovery of carbonate platform from Caen to Le Mans during the mid Oxfordian. Topography of the Paleozoic basement mainly controlled lateral variations of paleodepth within the western France platform until the mid Bathonian. A synthesis of carbonate production in the western Tethyan domain at that time was conducted. Stages of high carbonate production during the Bajocian/Bathonian and the middle to late Oxfordian are synchronous with low δ13C, high eccentricity intervals, and rather dry climate promoting (1) evaporation and carbonate supersaturation, and (2) oligotrophic conditions. Periods of low carbonate production during the Aalenian and from the middle Callovian to early Oxfordian correlate with high δ13C and low eccentricity intervals, characterized by wet climate and less oligotrophic conditions. Such conditions tend to diminish growth potential of carbonate

  7. Slip conditions with wall catalysis and radiation for multicomponent, nonequilibrium gas flow. [for predicting heat transfer to the space shuttle

    NASA Technical Reports Server (NTRS)

    Hendricks, W. L.

    1974-01-01

    The slip conditions for a multicomponent mixture with diffusion, wall-catalyzed atom recombination and thermal radiation are derived, and simplified expressions for engineering applications are presented. The gas mixture may be in chemical nonequilibrium with finite-rate catalytic recombination occurring on the wall. These boundary conditions, which are used for rarefied flow regime flow field calculations, are shown to be necessary for accurate predictions of skin friction and heat transfer coefficients in the rarefied portion of the space shuttle trajectory.

  8. Interval hypoxic training.

    PubMed

    Bernardi, L

    2001-01-01

    Interval hypoxic training (IHT) is a technique developed in the former Soviet Union, that consists of repeated exposures to 5-7 minutes of steady or progressive hypoxia, interrupted by equal periods of recovery. It has been proposed for training in sports, to acclimatize to high altitude, and to treat a variety of clinical conditions, spanning from coronary heart disease to Cesarean delivery. Some of these results may originate by the different effects of continuous vs. intermittent hypoxia (IH), which can be obtained by manipulating the repetition rate, the duration and the intensity of the hypoxic stimulus. The present article will attempt to examine some of the effects of IH, and, whenever possible, compare them to those of typical IHT. IH can modify oxygen transport and energy utilization, alter respiratory and blood pressure control mechanisms, induce permanent modifications in the cardiovascular system. IHT increases the hypoxic ventilatory response, increase red blood cell count and increase aerobic capacity. Some of these effects might be potentially beneficial in specific physiologic or pathologic conditions. At this stage, this technique appears interesting for its possible applications, but still largely to be explored for its mechanisms, potentials and limitations.

  9. Low and High Locomotor Responsiveness to Cocaine Predicts Intravenous Cocaine Conditioned Place Preference in Male Sprague-Dawley Rats

    PubMed Central

    Allen, Richard M.; Everett, Carson V.; Nelson, Anna M.; Gulley, Joshua M.; Zahniser, Nancy R.

    2009-01-01

    Outbred, male Sprague-Dawley rats can be classified as either low or high cocaine responders (LCRs or HCRs, respectively) based on cocaine-induced locomotor activity in an open-field arena. This difference reflects cocaine’s ability to inhibit the striatal dopamine transporter and predicts development of sensitization. To investigate the relationship between initial cocaine locomotor responsiveness and cocaine reward, here we first classified rats as either LCRs or HCRs in a conditioned place preference (CPP) apparatus. Subsequently, we conducted cocaine conditioning trials, twice daily over four days with vehicle and cocaine (10 mg/kg, i.p. or 1 mg/kg, i.v.). When cocaine was administered by the i.p. route, similar to previous findings in the open-field, LCRs and HCRs were readily classified and locomotor sensitization developed in LCRs, but not HCRs. However, cocaine CPP was not observed. In contrast, when cocaine was administered by the i.v. route, the LCR/HCR classification not only predicted sensitization, but also CPP, with only LCR rats exhibiting sensitization and cocaine conditioning. Our findings show that the initial locomotor response to cocaine can predict CPP in male Sprague-Dawley rats under conditions when place conditioning develops, and that LCRs may be more prone to develop conditioning in the context of cocaine reward. PMID:17250883

  10. PREDICTING ESTUARINE SEDIMENT METAL CONCENTRATIONS AND INFERRED ECOLOGICAL CONDITIONS: AN INFORMATION THEORETIC APPROACH

    EPA Science Inventory

    Empirically derived values associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. However, resources limit the number, magnitude, and frequency of monitoring programs to gather these data. As su...

  11. PREDICTING ESTUARINE SEDIMENT METAL CONCENTRATIONS AND INFERRED ECOLOGICAL CONDITIONS: AN INFORMATION THEORETIC APPROACH

    EPA Science Inventory

    Empirically derived values associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. However, resources limit the number, magnitude, and frequency of monitoring programs to gather these data. As su...

  12. Sensitivity of damage predictions to tissue level yield properties and apparent loading conditions.

    PubMed

    Niebur, G L; Yuen, J C; Burghardt, A J; Keaveny, T M

    2001-05-01

    High-resolution finite element models of trabecular bone failure could be used to augment current techniques for measuring damage in trabecular bone. However, the sensitivity of such models to the assumed tissue yield properties and apparent loading conditions is unknown. The goal of this study was to assess the sensitivity of the amount and mode (tension vs. compression) of tissue level yielding in trabecular bone to these factors. Linear elastic, high-resolution finite element models of nine bovine tibial trabecular bone specimens were used to calculate the fraction of the total tissue volume that exceeded each criterion for apparent level loading to the reported elastic limit in both on-axis and transverse compression and tension, and in shear. Four candidate yield criteria were studied, based on values suggested in the literature. Both the amount and the failure mode of yielded tissue were sensitive to the magnitudes of the tissue yield strains, the degree of tension-compression asymmetry of the yield criterion, and the applied apparent loads. The amount of yielded tissue was most sensitive to the orientation of the applied apparent loading, with the most tissue yielding for loading along the principal trabecular orientation and the least for loading perpendicular to it, regardless of the assumed tissue level yield criterion. Small changes in the magnitudes and the degree of asymmetry of the tissue yield criterion resulted in much larger changes in the amount of yielded tissue in the model. The results indicate that damage predictions based on high-resolution finite element models are highly sensitive to the assumed tissue yield properties. As such, good estimates of these values are needed before high-resolution finite element models can be applied to the study of trabecular bone damage. Regardless of the assumed tissue yield properties, the amount and type of damage that occurs in trabecular bone depends on the relative orientations of the applied apparent

  13. Aural-Nondetectability Model Predictions for Night-Vision Goggles across Ambient Lighting Conditions

    DTIC Science & Technology

    2015-12-01

    The present work by the US Army Research Laboratory (ARL) measures the acoustic output of 6 NVGs under 3 different lighting conditions that...Ambient Lighting Conditions by Jeremy Gaston, Ashley Foots, Christopher Stachowiak, and Samantha Chambers Approved for...Lighting Conditions Jeremy Gaston, Ashley Foots, Christopher Stachowiak, and Samantha Chambers Human Research and Engineering Directorate, ARL

  14. Prediction of Air Conditioning Load Response for Providing Spinning Reserve - ORNL Report

    SciTech Connect

    Kueck, John D; Kirby, Brendan J; Ally, Moonis Raza; Rice, C Keith

    2009-02-01

    This report assesses the use of air conditioning load for providing spinning reserve and discusses the barriers and opportunities. Air conditioning load is well suited for this service because it often increases during heavy load periods and can be curtailed for short periods with little impact to the customer. The report also provides an appendix describing the ambient temperature effect on air conditioning load.

  15. A Deep Conditional Random Field Approach to Transmembrane Topology Prediction and Application to GPCR Three-Dimensional Structure Modeling.

    PubMed

    Wu, Hongjie; Wang, Kun; Lu, Liyao; Xue, Yu; Lyu, Qiang; Jiang, Min

    2016-08-25

    Transmembrane proteins play important roles in cellular energy production, signal transmission, and metabolism. Many shallow machine learning methods have been applied to transmembrane topology prediction, but the performance was limited by the large size of membrane proteins and the complex biological evolution information behind the sequence. In this paper, we proposed a novel deep approach based on conditional random fields named as dCRF-TM for predicting the topology of transmembrane proteins. Conditional random fields take into account more complicated interrelation between residue labels in full-length sequence than HMM and SVM-based methods. Three widely-used datasets were employed in the benchmark. DCRF-TM had the accuracy 95% over helix location prediction and the accuracy 78% over helix number prediction. DCRF-TM demonstrated a more robust performance on large size proteins (>350 residues) against 11 state-of-the-art predictors. Further dCRF-TM was applied to ab initio modeling three-dimensional structures of seven-transmembrane receptors, also known as G protein-coupled receptors. The predictions on 24 solved G protein-coupled receptors and unsolved vasopressin V2 receptor illustrated that dCRF-TM helped abGPCR-I-TASSER to improve TM-score 34.3% rather than using the random transmembrane definition. 2 out of 5 predicted models caught the experimental verified disulfide bond in vasopressin V2 receptor.

  16. Prediction of Hydrolysis Products of Organic Chemicals under Environmental pH Conditions

    EPA Science Inventory

    Cheminformatics-based software tools can predict the molecular structure of transformation products using a library of transformation reaction schemes. This paper presents the development of such a library for abiotic hydrolysis of organic chemicals under environmentally relevant...

  17. Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

    PubMed Central

    Hsu, Ling-Yuan; Chen, Tsung-Lin

    2012-01-01

    This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event. PMID:23202231

  18. Vehicle dynamic prediction systems with on-line identification of vehicle parameters and road conditions.

    PubMed

    Hsu, Ling-Yuan; Chen, Tsung-Lin

    2012-11-13

    This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.

  19. On the importance of modelling organ geometry and boundary conditions for predicting three-dimensional prostate deformation.

    PubMed

    Jahya, Alex; Schouten, Martijn G; Fütterer, Jurgen J; Misra, Sarthak

    2014-04-01

    The use of an ultrasound probe or a needle guide during biopsy deforms both the rectal wall and the prostate, resulting in lesion motion. An accurate patient-specific finite element (FE)-based biomechanical model can be used to predict prostate deformations. In this study, an FE model of a prostate phantom is developed using magnetic resonance images, while soft-tissue elasticity is estimated in vivo using an ultrasound-based acoustic radiation force impulse imaging technique. This study confirms that three-dimensional FE-predicted prostate deformation is predominantly dependent on accurate modelling of prostate geometry and boundary conditions. Upon application of various compressive displacements, our results show that a linear elastic FE model can accurately predict prostate deformations. The maximum global error between FE-predicted simulations and experimental results is 0.76 mm. Moreover, the effect of including the urethra, puboprostatic ligament and urinary bladder on prostate deformations is investigated by a sensitivity study.

  20. Musical intervals in speech

    PubMed Central

    Ross, Deborah; Choi, Jonathan; Purves, Dale

    2007-01-01

    Throughout history and across cultures, humans have created music using pitch intervals that divide octaves into the 12 tones of the chromatic scale. Why these specific intervals in music are preferred, however, is not known. In the present study, we analyzed a database of individually spoken English vowel phones to examine the hypothesis that musical intervals arise from the relationships of the formants in speech spectra that determine the perceptions of distinct vowels. Expressed as ratios, the frequency relationships of the first two formants in vowel phones represent all 12 intervals of the chromatic scale. Were the formants to fall outside the ranges found in the human voice, their relationships would generate either a less complete or a more dilute representation of these specific intervals. These results imply that human preference for the intervals of the chromatic scale arises from experience with the way speech formants modulate laryngeal harmonics to create different phonemes. PMID:17525146

  1. Musical intervals in speech.

    PubMed

    Ross, Deborah; Choi, Jonathan; Purves, Dale

    2007-06-05

    Throughout history and across cultures, humans have created music using pitch intervals that divide octaves into the 12 tones of the chromatic scale. Why these specific intervals in music are preferred, however, is not known. In the present study, we analyzed a database of individually spoken English vowel phones to examine the hypothesis that musical intervals arise from the relationships of the formants in speech spectra that determine the perceptions of distinct vowels. Expressed as ratios, the frequency relationships of the first two formants in vowel phones represent all 12 intervals of the chromatic scale. Were the formants to fall outside the ranges found in the human voice, their relationships would generate either a less complete or a more dilute representation of these specific intervals. These results imply that human preference for the intervals of the chromatic scale arises from experience with the way speech formants modulate laryngeal harmonics to create different phonemes.

  2. Condition, not eyespan, predicts contest outcome in female stalk-eyed flies, Teleopsis dalmanni

    PubMed Central

    Bath, Eleanor; Wigby, Stuart; Vincent, Claire; Tobias, Joseph A; Seddon, Nathalie

    2015-01-01

    In contests among males, body condition is often the key determinant of a successful outcome, with fighting ability signaled by so-called armaments, that is, exaggerated, condition-dependent traits. However, it is not known whether condition and exaggerated traits function in the same way in females. Here, we manipulated adult condition by varying larval nutrition in the stalk-eyed fly, Teleopsis dalmanni, a species in which eyespan is exaggerated in both sexes, and we measured the outcome of contests between females of similar or different body condition and relative eyespan. We found that females in higher condition, with both larger bodies and eyespan, won a higher proportion of encounters when competing against rivals of lower condition. However, when females were of equal condition, neither eyespan nor body length had an effect on the outcome of a contest. An analysis of previously published data revealed a similar pattern in males: individuals with large relative eyespan did not win significantly more encounters when competing with individuals of a similar body size. Contrary to expectations, and to previous findings in males, there was no clear effect of differences in body size or eyespan affecting contest duration in females. Taken together, our findings suggest that although eyespan can provide an honest indicator of condition, large eyespans provide no additional benefit to either sex in intrasexual aggressive encounters; body size is instead the most important factor. PMID:26140199

  3. Programming with Intervals

    NASA Astrophysics Data System (ADS)

    Matsakis, Nicholas D.; Gross, Thomas R.

    Intervals are a new, higher-level primitive for parallel programming with which programmers directly construct the program schedule. Programs using intervals can be statically analyzed to ensure that they do not deadlock or contain data races. In this paper, we demonstrate the flexibility of intervals by showing how to use them to emulate common parallel control-flow constructs like barriers and signals, as well as higher-level patterns such as bounded-buffer producer-consumer. We have implemented intervals as a publicly available library for Java and Scala.

  4. Interval Graph Limits

    PubMed Central

    Diaconis, Persi; Holmes, Susan; Janson, Svante

    2015-01-01

    We work out a graph limit theory for dense interval graphs. The theory developed departs from the usual description of a graph limit as a symmetric function W (x, y) on the unit square, with x and y uniform on the interval (0, 1). Instead, we fix a W and change the underlying distribution of the coordinates x and y. We find choices such that our limits are continuous. Connections to random interval graphs are given, including some examples. We also show a continuity result for the chromatic number and clique number of interval graphs. Some results on uniqueness of the limit description are given for general graph limits. PMID:26405368

  5. Development of a threshold model to predict germination of Populus tomentosa seeds after harvest and storage under ambient condition.

    PubMed

    Wang, Wei-Qing; Cheng, Hong-Yan; Song, Song-Quan

    2013-01-01

    Effects of temperature, storage time and their combination on germination of aspen (Populus tomentosa) seeds were investigated. Aspen seeds were germinated at 5 to 30°C at 5°C intervals after storage for a period of time under 28°C and 75% relative humidity. The effect of temperature on aspen seed germination could not be effectively described by the thermal time (TT) model, which underestimated the germination rate at 5°C and poorly predicted the time courses of germination at 10, 20, 25 and 30°C. A modified TT model (MTT) which assumed a two-phased linear relationship between germination rate and temperature was more accurate in predicting the germination rate and percentage and had a higher likelihood of being correct than the TT model. The maximum lifetime threshold (MLT) model accurately described the effect of storage time on seed germination across all the germination temperatures. An aging thermal time (ATT) model combining both the TT and MLT models was developed to describe the effect of both temperature and storage time on seed germination. When the ATT model was applied to germination data across all the temperatures and storage times, it produced a relatively poor fit. Adjusting the ATT model to separately fit germination data at low and high temperatures in the suboptimal range increased the models accuracy for predicting seed germination. Both the MLT and ATT models indicate that germination of aspen seeds have distinct physiological responses to temperature within a suboptimal range.

  6. Effect size measures in genetic association studies and age-conditional risk prediction.

    PubMed

    So, Hon-Cheong; Sham, Pak C

    2010-01-01

    The interest in risk prediction using genomic profiles has surged recently. A proper interpretation of effect size measures in association studies is crucial to accurate risk prediction. In this study, we clarified the relationship between the odds ratio (OR), relative risk and incidence rate ratios in the context of genetic association studies. We demonstrated that under the common practice of sampling prevalent cases and controls, the resulting ORs approximate the incidence rate ratios. Based on this result, we presented a framework to compute the disease risk given the current age and follow-up period (including lifetime risk), with consideration of competing risks of mortality. We considered two extensions. One is correcting the incidence rate to reflect the person-years alive and disease-free, the other is converting prevalence to incidence estimates. The methodology was applied to an example of breast cancer prediction. We observed that simply multiplying the OR by the average lifetime risk estimates yielded a final estimate >100% (101%), while using our method that accounts for competing risks produces an estimate of 63% only. We also applied the method to risk prediction of Alzheimer's disease in Hong Kong. We recommend that companies offering direct-to-consumer genetic testing employ more rigorous prediction algorithms considering competing risks. Copyright © 2010 S. Karger AG, Basel.

  7. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli

    PubMed Central

    Kim, Minseung; Rai, Navneet; Zorraquino, Violeta; Tagkopoulos, Ilias

    2016-01-01

    A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery. PMID:27713404

  8. Landslide occurrences and recurrence intervals of heavy rainfalls in Japan

    NASA Astrophysics Data System (ADS)

    Saito, H.; Uchida, T.; Matsuyama, H.; Korup, O.

    2015-12-01

    Dealing with predicted increases in extreme weather conditions due to climate change requires robust knowledge about controls on rainfall-triggered landslides. This study developed the probable rainfall database from weather radar data, and analyzed the potential correlation between the landslide magnitude-frequency and the recurrence interval of the heavy rainfall across Japan. We analyzed 4,744 rainfall-induced landslides (Saito et al., 2014, Geology), 1 to 72 h rainfalls, and soil water index (SWI). We then estimated recurrence intervals for these rainfall parameters using a Gumbel distribution with jackknife fitting. Results showed that the recurrence intervals of rainfall events which caused landslides (<10^3 m^3) were less than 10 yr across Japan. The recurrence intervals increased with increases in landslide volumes. With regard to the landslides larger than 10^5 m^3, recurrence intervals of the rainfall events were more than 100 yr. These results suggest that recurrence intervals of heavy rainfalls are important for assessing regional landslide hazard in Japan.

  9. Stable isotope models to predict geographic origin and cultivation conditions of marijuana.

    PubMed

    Hurley, Janet M; West, Jason B; Ehleringer, James R

    2010-06-01

    Here we describe stable isotope based models using hydrogen and carbon isotope ratios to predict geographic region-of-origin and growth environment for marijuana, with the intent of applying these models to analyses of marijuana trafficking in the USA. The models were developed on the basis of eradication specimens and border specimens seized throughout the USA. We tested reliability of the geographic region-of-origin and growth environment models with a "blind" set of 60 marijuana eradication specimens obtained from counties throughout the USA. The two geographic region-of-origin model predictions were 60-67% reliable and cultivation environment model predictions were 86% accurate for the blind specimens. We demonstrate here that stable isotope ratio analysis of marijuana seizures can significantly improve our understanding of marijuana distribution networks and it is for that purpose that these models were developed.

  10. Predicting solvation free energies and thermodynamics in polar solvents and mixtures using a solvation-layer interface condition

    NASA Astrophysics Data System (ADS)

    Molavi Tabrizi, Amirhossein; Goossens, Spencer; Mehdizadeh Rahimi, Ali; Knepley, Matthew; Bardhan, Jaydeep P.

    2017-03-01

    We demonstrate that with two small modifications, the popular dielectric continuum model is capable of predicting, with high accuracy, ion solvation thermodynamics (Gibbs free energies, entropies, and heat capacities) in numerous polar solvents. We are also able to predict ion solvation free energies in water-co-solvent mixtures over available concentration series. The first modification to the classical dielectric Poisson model is a perturbation of the macroscopic dielectric-flux interface condition at the solute-solvent interface: we add a nonlinear function of the local electric field, giving what we have called a solvation-layer interface condition (SLIC). The second modification is including the microscopic interface potential (static potential) in our model. We show that the resulting model exhibits high accuracy without the need for fitting solute atom radii in a state-dependent fashion. Compared to experimental results in nine water-co-solvent mixtures, SLIC predicts transfer free energies to within 2.5 kJ/mol. The co-solvents include both protic and aprotic species, as well as biologically relevant denaturants such as urea and dimethylformamide. Furthermore, our results indicate that the interface potential is essential to reproduce entropies and heat capacities. These and previous tests of the SLIC model indicate that it is a promising dielectric continuum model for accurate predictions in a wide range of conditions.

  11. Predictions of Separated and Transitional Boundary Layers Under Low-Pressure Turbine Airfoil Conditions Using an Intermittency Transport Equation

    NASA Technical Reports Server (NTRS)

    Suzen, Y. Bora; Huang, P. G.; Hultgren, Lennart S.; Ashpis, David E.

    2001-01-01

    A new transport equation for the intermittency factor was proposed to predict separated and transitional boundary layers under low-pressure turbine airfoil conditions. The intermittent behavior of the transitional flows is taken into account and incorporated into computations by modifying the eddy viscosity, mu(sub t), with the intermittency factor, gamma. Turbulent quantities are predicted by using Menter's two-equation turbulence model (SST). The intermittency factor is obtained from a transport equation model, which not only can reproduce the experimentally observed streamwise variation of the intermittency in the transition zone, but also can provide a realistic cross-stream variation of the intermittency profile. In this paper, the intermittency model is used to predict a recent separated and transitional boundary layer experiment under low pressure turbine airfoil conditions. The experiment provides detailed measurements of velocity, turbulent kinetic energy and intermittency profiles for a number of Reynolds numbers and freestream turbulent intensity conditions and is suitable for validation purposes. Detailed comparisons of computational results with experimental data are presented and good agreements between the experiments and predictions are obtained.

  12. Predictions of Separated and Transitional Boundary Layers Under Low-Pressure Turbine Airfoil Conditions Using an Intermittency Transport Equation

    NASA Technical Reports Server (NTRS)

    Suzen, Y. B.; Huang, P. G.; Hultgren, Lennart S.; Ashpis, David E.

    2003-01-01

    A new transport equation for the intermittency factor was proposed to predict separated and transitional boundary layers under low-pressure turbine airfoil conditions. The intermittent behavior of the transitional flows is taken into account and incorporated into computations by modifying the eddy viscosity, t , with the intermittency factor, y. Turbulent quantities are predicted by using Menter s two-equation turbulence model (SST). The intermittency factor is obtained from a transport equation model, which not only can reproduce the experimentally observed streamwise variation of the intermittency in the transition zone, but also can provide a realistic cross-stream variation of the intermittency profile. In this paper, the intermittency model is used to predict a recent separated and transitional boundary layer experiment under low pressure turbine airfoil conditions. The experiment provides detailed measurements of velocity, turbulent kinetic energy and intermittency profiles for a number of Reynolds numbers and freestream turbulent intensity conditions and is suitable for validation purposes. Detailed comparisons of computational results with experimental data are presented and good agreements between the experiments and predictions are obtained.

  13. Can microscale meteorological conditions predict the impact of white pine blister rust in Colorado and Wyoming?

    Treesearch

    William R. Jacobi; Betsy A. Goodrich; Holly S. J. Kearns; Kelly S. Burns; Brian W. Geils

    2011-01-01

    White pine blister rust occurs when there are compatible interactions between susceptible hosts (white pines and Ribes spp.), inoculum (Cronartium ribicola spores), and local weather conditions during infection. The five spore stages of the white pine blister rust (WPBR) fungus have specific temperature and moisture conditions necessary for production, germination, and...

  14. Teachers' Perceptions of Their Working Conditions: How Predictive of Planned and Actual Teacher Movement?

    ERIC Educational Resources Information Center

    Ladd, Helen F.

    2011-01-01

    This quantitative study examines the relationship between teachers' perceptions of their working conditions and their intended and actual departures from schools. Based on rich administrative data for North Carolina combined with a 2006 statewide survey administered to all teachers in the state, the study documents that working conditions are…

  15. The Central Amygdala Projection to the Substantia Nigra Reflects Prediction Error Information in Appetitive Conditioning

    ERIC Educational Resources Information Center

    Lee, Hongjoo J.; Gallagher, Michela; Holland, Peter C.

    2010-01-01

    The central amygdala nucleus (CeA) plays a critical role in cognitive processes beyond fear conditioning. For example, intact CeA function is essential for enhancing attention to conditioned stimuli (CSs). Furthermore, this enhanced attention depends on the CeA's connections to the nigrostriatal system. In the current study, we examined the role…

  16. The Central Amygdala Projection to the Substantia Nigra Reflects Prediction Error Information in Appetitive Conditioning

    ERIC Educational Resources Information Center

    Lee, Hongjoo J.; Gallagher, Michela; Holland, Peter C.

    2010-01-01

    The central amygdala nucleus (CeA) plays a critical role in cognitive processes beyond fear conditioning. For example, intact CeA function is essential for enhancing attention to conditioned stimuli (CSs). Furthermore, this enhanced attention depends on the CeA's connections to the nigrostriatal system. In the current study, we examined the role…

  17. Prediction of hydrolysis pathways and kinetics for antibiotics under environmental pH conditions: a quantum chemical study on cephradine.

    PubMed

    Zhang, Haiqin; Xie, Hongbin; Chen, Jingwen; Zhang, Shushen

    2015-02-03

    Understanding hydrolysis pathways and kinetics of many antibiotics that have multiple hydrolyzable functional groups is important for their fate assessment. However, experimental determination of hydrolysis encounters difficulties due to time and cost restraint. We employed the density functional theory and transition state theory to predict the hydrolysis pathways and kinetics of cephradine, a model of cephalosporin with two hydrolyzable groups, two ionization states, two isomers and two nucleophilic attack directions. Results showed that the hydrolysis of cephradine at pH = 8.0 proceeds via opening of the β-lactam ring followed by intramolecular amidation. The predicted rate constants at different pH conditions are of the same order of magnitude as the experimental values, and the predicted products are confirmed by experiment. This study identified a catalytic role of the carboxyl group in the hydrolysis, and implies that the carboxyl group also plays a catalytic role in the hydrolysis of other cephalosporin and penicillin antibiotics. This is a first attempt to quantum chemically predict hydrolysis of an antibiotic with complex pathways, and indicates that to predict hydrolysis products under the environmental pH conditions, the variation of the rate constants for different pathways with pH should be evaluated.

  18. MERGER RATES OF DOUBLE NEUTRON STARS AND STELLAR ORIGIN BLACK HOLES: THE IMPACT OF INITIAL CONDITIONS ON BINARY EVOLUTION PREDICTIONS

    SciTech Connect

    Mink, S. E. de; Belczynski, K. E-mail: kbelczyn@astrouw.edu.pl

    2015-11-20

    The initial mass function (IMF), binary fraction, and distributions of binary parameters (mass ratios, separations, and eccentricities) are indispensable inputs for simulations of stellar populations. It is often claimed that these are poorly constrained, significantly affecting evolutionary predictions. Recently, dedicated observing campaigns have provided new constraints on the initial conditions for massive stars. Findings include a larger close binary fraction and a stronger preference for very tight systems. We investigate the impact on the predicted merger rates of neutron stars and black holes. Despite the changes with previous assumptions, we only find an increase of less than a factor of 2 (insignificant compared with evolutionary uncertainties of typically a factor of 10–100). We further show that the uncertainties in the new initial binary properties do not significantly affect (within a factor of 2) our predictions of double compact object merger rates. An exception is the uncertainty in IMF (variations by a factor of 6 up and down). No significant changes in the distributions of final component masses, mass ratios, chirp masses, and delay times are found. We conclude that the predictions are, for practical purposes, robust against uncertainties in the initial conditions concerning binary parameters, with the exception of the IMF. This eliminates an important layer of the many uncertain assumptions affecting the predictions of merger detection rates with the gravitational wave detectors aLIGO/aVirgo.

  19. Empirical prediction intervals improve energy forecasting.

    PubMed

    Kaack, Lynn H; Apt, Jay; Morgan, M Granger; McSharry, Patrick

    2017-08-15

    Hundreds of organizations and analysts use energy projections, such as those contained in the US Energy Information Administration (EIA)'s Annual Energy Outlook (AEO), for investment and policy decisions. Retrospective analyses of past AEO projections have shown that observed values can differ from the projection by several hundred percent, and thus a thorough treatment of uncertainty is essential. We evaluate the out-of-sample forecasting performance of several empirical density forecasting methods, using the continuous ranked probability score (CRPS). The analysis confirms that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncertainty estimates over a variety of energy quantities in the AEO, in particular outperforming scenario projections provided in the AEO. We report probabilistic uncertainties for 18 core quantities of the AEO 2016 projections. Our work frames how to produce, evaluate, and rank probabilistic forecasts in this setting. We propose a log transformation of forecast errors for price projections and a modified nonparametric empirical density forecasting method. Our findings give guidance on how to evaluate and communicate uncertainty in future energy outlooks.

  20. Conditions for Effective Application of Analysis of Symmetrically-Predicted Endogenous Subgroups

    ERIC Educational Resources Information Center

    Peck, Laura R.

    2015-01-01

    Several analytic strategies exist for opening up the "black box" to reveal more about what drives policy and program impacts. This article focuses on one of these strategies: the Analysis of Symmetrically-Predicted Endogenous Subgroups (ASPES). ASPES uses exogenous baseline data to identify endogenously-defined subgroups, keeping the…

  1. Analytical Predictions of Thermal Stress in the Stardust PICA Heatshield Under Reentry Flight Conditions

    NASA Technical Reports Server (NTRS)

    Squire, Thomas; Milos, Frank; Agrawal, Parul

    2009-01-01

    We performed finite element analyses on a model of the Phenolic Impregnated Carbon Ablator (PICA) heatshield from the Stardust sample return capsule (SRC) to predict the thermal stresses in the PICA material during reentry. The heatshield on the Stardust SRC was a 0.83 m sphere cone, fabricated from a single piece of 5.82 cm-thick PICA. The heatshield performed successfully during Earth reentry of the SRC in January 2006. Material response analyses of the full, axisymmetric PICA heatshield were run using the Two-Dimensional Implicit Ablation, Pyrolysis, and Thermal Response Program (TITAN). Peak surface temperatures were predicted to be 3385K, while the temperature at the PICA backface remained at the estimated initial cold-soak temperature of 278K. Surface recession and temperature distribution results from TITAN, at several points in the reentry trajectory, were mapped onto an axisymmetric finite element model of the heatshield. We used the finite element model to predict the thermal stresses in the PICA from differential thermal expansion. The predicted peak compressive stress in the PICA heatshield was 1.38 MPa. Although this level of stress exceeded the chosen design limit for compressive stresses in PICA tiles for the design of the Orion crew exploration vehicle heatshield, the Stardust heatshield exhibited no obvious mechanical failures from thermal stress. The analyses of the Stardust heatshield were used to assess and adjust the level of conservatism in the finite element analyses in support of the Orion heatshield design.

  2. Prediction of lung function response for populations exposed to a wide range of ozone conditions

    EPA Science Inventory

    Abstract Context: A human exposure-response (E-R) model that has previously been demonstrated to accurately predict population mean FEV1 response to ozone exposure has been proposed as the foundation for future risk assessments for ambient ozone. Objective: Fit the origi...

  3. Analytical Predictions of Thermal Stress in the Stardust PICA Heatshield Under Reentry Flight Conditions

    NASA Technical Reports Server (NTRS)

    Squire, Thomas; Milos, Frank; Agrawal, Parul

    2009-01-01

    We performed finite element analyses on a model of the Phenolic Impregnated Carbon Ablator (PICA) heatshield from the Stardust sample return capsule (SRC) to predict the thermal stresses in the PICA material during reentry. The heatshield on the Stardust SRC was a 0.83 m sphere cone, fabricated from a single piece of 5.82 cm-thick PICA. The heatshield performed successfully during Earth reentry of the SRC in January 2006. Material response analyses of the full, axisymmetric PICA heatshield were run using the Two-Dimensional Implicit Ablation, Pyrolysis, and Thermal Response Program (TITAN). Peak surface temperatures were predicted to be 3385K, while the temperature at the PICA backface remained at the estimated initial cold-soak temperature of 278K. Surface recession and temperature distribution results from TITAN, at several points in the reentry trajectory, were mapped onto an axisymmetric finite element model of the heatshield. We used the finite element model to predict the thermal stresses in the PICA from differential thermal expansion. The predicted peak compressive stress in the PICA heatshield was 1.38 MPa. Although this level of stress exceeded the chosen design limit for compressive stresses in PICA tiles for the design of the Orion crew exploration vehicle heatshield, the Stardust heatshield exhibited no obvious mechanical failures from thermal stress. The analyses of the Stardust heatshield were used to assess and adjust the level of conservatism in the finite element analyses in support of the Orion heatshield design.

  4. Predicting Pilot Performance in Off-Nominal Conditions: A Meta-Analysis and Model Validation

    NASA Technical Reports Server (NTRS)

    Wickens, C.D.; Hooey, B.L.; Gore, B.F.; Sebok, A.; Koenecke, C.; Salud, E.

    2009-01-01

    Pilot response to off-nominal (very rare) events represents a critical component to understanding the safety of next generation airspace technology and procedures. We describe a meta-analysis designed to integrate the existing data regarding pilot accuracy of detecting rare, unexpected events such as runway incursions in realistic flight simulations. Thirty-five studies were identified and pilot responses were categorized by expectancy, event location, and whether the pilot was flying with a highway-in-the-sky display. All three dichotomies produced large, significant effects on event miss rate. A model of human attention and noticing, N-SEEV, was then used to predict event noticing performance as a function of event salience and expectancy, and retinal eccentricity. Eccentricity is predicted from steady state scanning by the SEEV model of attention allocation. The model was used to predict miss rates for the expectancy, location and highway-in-the-sky (HITS) effects identified in the meta-analysis. The correlation between model-predicted results and data from the meta-analysis was 0.72.

  5. Prediction of lung function response for populations exposed to a wide range of ozone conditions

    EPA Science Inventory

    Abstract Context: A human exposure-response (E-R) model that has previously been demonstrated to accurately predict population mean FEV1 response to ozone exposure has been proposed as the foundation for future risk assessments for ambient ozone. Objective: Fit the origi...

  6. Conditions for Effective Application of Analysis of Symmetrically-Predicted Endogenous Subgroups

    ERIC Educational Resources Information Center

    Peck, Laura R.

    2015-01-01

    Several analytic strategies exist for opening up the "black box" to reveal more about what drives policy and program impacts. This article focuses on one of these strategies: the Analysis of Symmetrically-Predicted Endogenous Subgroups (ASPES). ASPES uses exogenous baseline data to identify endogenously-defined subgroups, keeping the…

  7. Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander

    NASA Astrophysics Data System (ADS)

    Wang, Qiang; Mu, Mu; Dijkstra, Henk A.

    2012-01-01

    A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method. Because of their relatively large uncertainties, three model parameters were considered: the interfacial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.

  8. Interval estimations in metrology

    NASA Astrophysics Data System (ADS)

    Mana, G.; Palmisano, C.

    2014-06-01

    This paper investigates interval estimation for a measurand that is known to be positive. Both the Neyman and Bayesian procedures are considered and the difference between the two, not always perceived, is discussed in detail. A solution is proposed to a paradox originated by the frequentist assessment of the long-run success rate of Bayesian intervals.

  9. Elements of a pragmatic approach for dealing with bias and uncertainty in experiments through predictions : experiment design and data conditioning; %22real space%22 model validation and conditioning; hierarchical modeling and extrapolative prediction.

    SciTech Connect

    Romero, Vicente Jose

    2011-11-01

    This report explores some important considerations in devising a practical and consistent framework and methodology for utilizing experiments and experimental data to support modeling and prediction. A pragmatic and versatile 'Real Space' approach is outlined for confronting experimental and modeling bias and uncertainty to mitigate risk in modeling and prediction. The elements of experiment design and data analysis, data conditioning, model conditioning, model validation, hierarchical modeling, and extrapolative prediction under uncertainty are examined. An appreciation can be gained for the constraints and difficulties at play in devising a viable end-to-end methodology. Rationale is given for the various choices underlying the Real Space end-to-end approach. The approach adopts and refines some elements and constructs from the literature and adds pivotal new elements and constructs. Crucially, the approach reflects a pragmatism and versatility derived from working many industrial-scale problems involving complex physics and constitutive models, steady-state and time-varying nonlinear behavior and boundary conditions, and various types of uncertainty in experiments and models. The framework benefits from a broad exposure to integrated experimental and modeling activities in the areas of heat transfer, solid and structural mechanics, irradiated electronics, and combustion in fluids and solids.

  10. Overconfidence in interval estimates.

    PubMed

    Soll, Jack B; Klayman, Joshua

    2004-03-01

    Judges were asked to make numerical estimates (e.g., "In what year was the first flight of a hot air balloon?"). Judges provided high and low estimates such that they were X% sure that the correct answer lay between them. They exhibited substantial overconfidence: The correct answer fell inside their intervals much less than X% of the time. This contrasts with choices between 2 possible answers to a question, which showed much less overconfidence. The authors show that overconfidence in interval estimates can result from variability in setting interval widths. However, the main cause is that subjective intervals are systematically too narrow given the accuracy of one's information-sometimes only 40% as large as necessary to be well calibrated. The degree of overconfidence varies greatly depending on how intervals are elicited. There are also substantial differences among domains and between male and female judges. The authors discuss the possible psychological mechanisms underlying this pattern of findings.

  11. Direct interval volume visualization.

    PubMed

    Ament, Marco; Weiskopf, Daniel; Carr, Hamish

    2010-01-01

    We extend direct volume rendering with a unified model for generalized isosurfaces, also called interval volumes, allowing a wider spectrum of visual classification. We generalize the concept of scale-invariant opacity—typical for isosurface rendering—to semi-transparent interval volumes. Scale-invariant rendering is independent of physical space dimensions and therefore directly facilitates the analysis of data characteristics. Our model represents sharp isosurfaces as limits of interval volumes and combines them with features of direct volume rendering. Our objective is accurate rendering, guaranteeing that all isosurfaces and interval volumes are visualized in a crack-free way with correct spatial ordering. We achieve simultaneous direct and interval volume rendering by extending preintegration and explicit peak finding with data-driven splitting of ray integration and hybrid computation in physical and data domains. Our algorithm is suitable for efficient parallel processing for interactive applications as demonstrated by our CUDA implementation.

  12. Importance of Watershed Land Use in Predicting Benthic Invertebrate Condition in the Virginian Biogeographic Province, USA.

    EPA Science Inventory

    Estuaries are dynamic transition zones linking freshwater and oceanic habitats. These productive ecosystems are threatened by a variety of stressors including human modification of coastal watersheds. In this study we examined potential linkages between estuarine condition and...

  13. Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China

    PubMed Central

    Lv, Yang; Hu, Guangyao; Wang, Chunyang; Yuan, Wenjie; Wei, Shanshan; Gao, Jiaoqi; Wang, Boyuan; Song, Fangchao

    2017-01-01

    The microbial contamination of central air conditioning system is one of the important factors that affect the indoor air quality. Actual measurement and analysis were carried out on microbial contamination in central air conditioning system at a venue in Dalian, China. Illumina miseq method was used and three fungal samples of two units were analysed by high throughput sequencing. Results showed that the predominant fungus in air conditioning unit A and B were Candida spp. and Cladosporium spp., and two fungus were further used in the hygrothermal response experiment. Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. In addition, the models were carried on the analysis to verify the accuracy and feasibility of the established model equation. PMID:28367963

  14. Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China.

    PubMed

    Lv, Yang; Hu, Guangyao; Wang, Chunyang; Yuan, Wenjie; Wei, Shanshan; Gao, Jiaoqi; Wang, Boyuan; Song, Fangchao

    2017-04-03

    The microbial contamination of central air conditioning system is one of the important factors that affect the indoor air quality. Actual measurement and analysis were carried out on microbial contamination in central air conditioning system at a venue in Dalian, China. Illumina miseq method was used and three fungal samples of two units were analysed by high throughput sequencing. Results showed that the predominant fungus in air conditioning unit A and B were Candida spp. and Cladosporium spp., and two fungus were further used in the hygrothermal response experiment. Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. In addition, the models were carried on the analysis to verify the accuracy and feasibility of the established model equation.

  15. Quantitative Prediction of Beef Quality Using Visible and NIR Spectroscopy with Large Data Samples Under Industry Conditions

    NASA Astrophysics Data System (ADS)

    Qiao, T.; Ren, J.; Craigie, C.; Zabalza, J.; Maltin, Ch.; Marshall, S.

    2015-03-01

    It is well known that the eating quality of beef has a significant influence on the repurchase behavior of consumers. There are several key factors that affect the perception of quality, including color, tenderness, juiciness, and flavor. To support consumer repurchase choices, there is a need for an objective measurement of quality that could be applied to meat prior to its sale. Objective approaches such as offered by spectral technologies may be useful, but the analytical algorithms used remain to be optimized. For visible and near infrared (VISNIR) spectroscopy, Partial Least Squares Regression (PLSR) is a widely used technique for meat related quality modeling and prediction. In this paper, a Support Vector Machine (SVM) based machine learning approach is presented to predict beef eating quality traits. Although SVM has been successfully used in various disciplines, it has not been applied extensively to the analysis of meat quality parameters. To this end, the performance of PLSR and SVM as tools for the analysis of meat tenderness is evaluated, using a large dataset acquired under industrial conditions. The spectral dataset was collected using VISNIR spectroscopy with the wavelength ranging from 350 to 1800 nm on 234 beef M. longissimus thoracis steaks from heifers, steers, and young bulls. As the dimensionality with the VISNIR data is very high (over 1600 spectral bands), the Principal Component Analysis (PCA) technique was applied for feature extraction and data reduction. The extracted principal components (less than 100) were then used for data modeling and prediction. The prediction results showed that SVM has a greater potential to predict beef eating quality than PLSR, especially for the prediction of tenderness. The infl uence of animal gender on beef quality prediction was also investigated, and it was found that beef quality traits were predicted most accurately in beef from young bulls.

  16. Predicting Use of Nurse Care Coordination by Older Adults With Chronic Conditions.

    PubMed

    Vanderboom, Catherine E; Holland, Diane E; Mandrekar, Jay; Lohse, Christine M; Witwer, Stephanie G; Hunt, Vicki L

    2017-07-01

    To be effective, nurse care coordination must be targeted at individuals who will use the service. The purpose of this study was to identify variables that predicted use of care coordination by primary care patients. Data on the potential predictor variables were obtained from patient interviews, the electronic health record, and an administrative database of 178 adults eligible for care coordination. Use of care coordination was obtained from an administrative database. A multivariable logistic regression model was developed using a bootstrap sampling approach. Variables predicting use of care coordination were dependence in both activities of daily living (ADL) and instrumental activities of daily living (IADL; odds ratio [OR] = 5.30, p = .002), independent for ADL but dependent for IADL (OR = 2.68, p = .01), and number of prescription medications (OR = 1.12, p = .002). Consideration of these variables may improve identification of patients to target for care coordination.

  17. Prediction of “Fear” Acquisition in Healthy Control Participants in a De Novo Fear-Conditioning Paradigm

    PubMed Central

    Otto, Michael W.; Leyro, Teresa M.; Christian, Kelly; Deveney, Christen M.; Reese, Hannah; Pollack, Mark H.; Orr, Scott P.

    2006-01-01

    Studies using fear-conditioning paradigms have found that anxiety patients are more conditionable than individuals without these disorders, but these effects have been demonstrated inconsistently. It is unclear whether these findings have etiological significance, or whether enhanced conditionability is linked only to certain anxiety characteristics. To further examine these issues, we assessed the predictive significance of relevant subsyndromal characteristics in 72 healthy adults, including measures of worry, avoidance, anxious mood, depressed mood, and fears of anxiety symptoms (anxiety sensitivity), as well as the dimensions of neuroticism and extraversion. Of these variables, we found that the combination of higher levels of subsyndromal worry and lower levels of behavioral avoidance predicted heightened conditionability, raising questions about the etiological significance of these variables in the acquisition or maintenance of anxiety disorders. In contrast, we found that anxiety sensitivity was more linked to individual differences in orienting response than differences in conditioning per se. PMID:17179530

  18. Prediction of velocities for a range of streamflow conditions in Pennsylvania

    USGS Publications Warehouse

    Reed, L.A.; Stuckey, M.H.

    2002-01-01

    A regression equation that is used nationwide to predict traveltime in streams during periods of low and moderate flow was developed by H.E. Jobson in 1996. Because none of the data used in the development of the equation were from streams in Pennsylvania, velocities for low and moderate flows predicted by the equation were compared to velocities measured during time-of-travel studies on the Susquehanna, Delaware, and Lehigh Rivers. Although these comparisons showed good agreement, a similar comparison using velocities for higher flows indicated an overestimate by this regression equation. Because of the need for a method of computing traveltimes for periods of high flows, a new regression equation was developed using data from three sources: (1) time-of-travel studies conducted at low and moderate flow, (2) slopearea measurements of flood flows, and (3) velocities of the 100-year floodway as reported in various flood-insurance studies. The new regression equation can be used for predicting velocities associated with flows up to the 100-year flood for Pennsylvania streams. It has standard errors of estimate of 0.18 feet per second, 0.37 feet per second; and 0.31 feet per second, for time-of-travel studies in the Susquehanna, Delaware, and Lehigh Rivers, respectively. The standard error of estimate is 1.71 feet per second for velocities determined from the slope-area measurements and 1.22 feet per second for velocities determined from the flood-insurance studies.

  19. Application of Neural Networks to Predict UH-60L Electrical Generator Condition using (IMD-HUMS) Data

    DTIC Science & Technology

    2006-12-01

    their success rate for this faulting diagnosis . As in any prediction/forecasting model, the selection of appropriate model inputs is extremely...UH-60L helicopter generator. Many different neural networks will be evaluated for their success rate on this faulting diagnosis . A. CONDITION BASED...or alerts • Fault or failure diagnosis and health evaluation • Prognostics: projection of health profiles to future health or estimation of

  20. Reliability of Degree-Day Models to Predict the Development Time of Plutella xylostella (L.) under Field Conditions.

    PubMed

    Marchioro, C A; Krechemer, F S; de Moraes, C P; Foerster, L A

    2015-12-01

    The diamondback moth, Plutella xylostella (L.), is a cosmopolitan pest of brassicaceous crops occurring in regions with highly distinct climate conditions. Several studies have investigated the relationship between temperature and P. xylostella development rate, providing degree-day models for populations from different geographical regions. However, there are no data available to date to demonstrate the suitability of such models to make reliable projections on the development time for this species in field conditions. In the present study, 19 models available in the literature were tested regarding their ability to accurately predict the development time of two cohorts of P. xylostella under field conditions. Only 11 out of the 19 models tested accurately predicted the development time for the first cohort of P. xylostella, but only seven for the second cohort. Five models correctly predicted the development time for both cohorts evaluated. Our data demonstrate that the accuracy of the models available for P. xylostella varies widely and therefore should be used with caution for pest management purposes.

  1. Predicting the onset of transformation under noncontinuous cooling conditions. Part 2: Application to the austenite pearlite transformation

    SciTech Connect

    Pham, T.T.; Hawbolt, E.B.; Brimacombe, J.K.

    1995-08-01

    A detailed review of the additivity principle with respect to the incubation of the austenite decomposition was summarized in Part 1 of this two-part series and led to the concept of an ideal time-temperature-transformation (TTT) diagram. This curve is characteristic of the chemistry and austenite grain size in the steel and allows nonisothermal behavior to be described assuming additivity holds. The derivation of mathematical relationships between the ideal and experimental cooling data was presented in the first article. In this second article, an ideal curve for the austenite-to-pearlite transformation was derived from cooling data. The applicability of the ideal TTT curve for predicting the start of transformation under continuous cooling conditions was assessed for a range of cooling rates. Experiments were conducted under both isothermal and varying temperature conditions, including an industrial cooling schedule, using a Gleeble Thermal Simulator. Reasonable agreement was found between the predictions and the observed transformation start temperatures; predictions were consistent and compared favorably against other methods which have been frequently used to estimate the transformation start temperature for nonisothermal conditions.

  2. Evaluating Productivity Predictions Under Elevated CO2 Conditions: Multi-Model Benchmarking Across FACE Experiments

    NASA Astrophysics Data System (ADS)

    Cowdery, E.; Dietze, M.

    2016-12-01

    As atmospheric levels of carbon dioxide levels continue to increase, it is critical that terrestrial ecosystem models can accurately predict ecological responses to the changing environment. Current predictions of net primary productivity (NPP) in response to elevated atmospheric CO2 concentration are highly variable and contain a considerable amount of uncertainty.The Predictive Ecosystem Analyzer (PEcAn) is an informatics toolbox that wraps around an ecosystem model and can be used to help identify which factors drive uncertainty. We tested a suite of models (LPJ-GUESS, MAESPA, GDAY, CLM5, DALEC, ED2), which represent a range from low to high structural complexity, across a range of Free-Air CO2 Enrichment (FACE) experiments: the Kennedy Space Center Open Top Chamber Experiment, the Rhinelander FACE experiment, the Duke Forest FACE experiment and the Oak Ridge Experiment on CO2 Enrichment. These tests were implemented in a novel benchmarking workflow that is automated, repeatable, and generalized to incorporate different sites and ecological models. Observational data from the FACE experiments represent a first test of this flexible, extensible approach aimed at providing repeatable tests of model process representation.To identify and evaluate the assumptions causing inter-model differences we used PEcAn to perform model sensitivity and uncertainty analysis, not only to assess the components of NPP, but also to examine system processes such nutrient uptake and and water use. Combining the observed patterns of uncertainty between multiple models with results of the recent FACE-model data synthesis project (FACE-MDS) can help identify which processes need further study and additional data constraints. These findings can be used to inform future experimental design and in turn can provide informative starting point for data assimilation.

  3. Predicting maize yield in Zimbabwe using dry dekads derived from remotely sensed Vegetation Condition Index

    NASA Astrophysics Data System (ADS)

    Kuri, Farai; Murwira, Amon; Murwira, Karin S.; Masocha, Mhosisi

    2014-12-01

    Maize is a key crop contributing to food security in Southern Africa yet accurate estimates of maize yield prior to harvesting are scarce. Timely and accurate estimates of maize production are essential for ensuring food security by enabling actionable mitigation strategies and policies for prevention of food shortages. In this study, we regressed the number of dry dekads derived from VCI against official ground-based maize yield estimates to generate simple linear regression models for predicting maize yield throughout Zimbabwe over four seasons (2009-10, 2010-11, 2011-12, and 2012-13). The VCI was computed using Normalized Difference Vegetation Index (NDVI) time series dataset from the SPOT VEGETATION sensor for the period 1998-2013. A significant negative linear relationship between number of dry dekads and maize yield was observed in each season. The variation in yield explained by the models ranged from 75% to 90%. The models were evaluated with official ground-based yield data that was not used to generate the models. There is a close match between the predicted yield and the official yield statistics with an error of 33%. The observed consistency in the negative relationship between number of dry dekads and ground-based estimates of maize yield as well as the high explanatory power of the regression models suggest that VCI-derived dry dekads could be used to predict maize yield before the end of the season thereby making it possible to plan strategies for dealing with food deficits or surpluses on time.

  4. Perceived Working Conditions and Personal Resources Predicting Mental Health Counselor Well-Being

    ERIC Educational Resources Information Center

    Thompson, Isabel A.

    2012-01-01

    This study examined the influence of counselor perceived working conditions, length of time in field, counselor gender, mindfulness attitudes, compassion satisfaction, emotion-focused coping, problem focused coping, and maladaptive coping on levels of burnout and compassion fatigue in a sample of 213 mental health counselors. Cross-sectional…

  5. Laser conditioning characterization and damage threshold prediction of hafnia/silica multilayer mirrors by photothermal microscopy

    SciTech Connect

    Papandrew, A B; Stolz, C J; Wu, Z L; Loomis, G E; Falabella, S

    2000-12-11

    Laser conditioning has been shown to improve the laser damage threshold of some optical coatings by greater than 2x. Debate continues within the damage community regarding laser-conditioning mechanisms, but it is clear that nodular ejection is one of the byproducts of the laser conditioning process. To better understand why laser conditioning is so effective, photothermal microscopy was used to measure absorption of coating defects before and after laser exposure. Although a modest absorption reduction was expected due to the lower electric field peaks within a pit and the absence of potentially absorbing nodular seeds, surprisingly, absorption reductions up to 150x were observed. Photothermal microscopy has also been successfully used to correlate laser-induced damage threshold and absorption of defects in hafnia/silica multilayer optical coatings. Defects with high absorption, as indicated by high photothermal signal, have low damage thresholds. Previously a linear correlation of damage threshold and defect photothermal signal was established with films designed and damage tested at 1{omega} (1053 nm) and Brewster's angle (56.4{sup o}), but characterized by photothermal microscopy at 514.5 nm and near-normal angle of incidence (10{sup o}). In this study coatings designed, characterized by photothermal microscopy, and damage tested at the same wavelength, incident angle, and polarization did not have a correlation between defect photothermal signal and absorption.

  6. Spatial prediction models for the probable biological condition of streams and rivers in the USA

    EPA Science Inventory

    The National Rivers and Streams Assessment (NRSA) is a probability-based survey conducted by the US Environmental Protection Agency and its state and tribal partners. It provides information on the ecological condition of the rivers and streams in the conterminous USA, and the ex...

  7. SMALL AREA ESTIMATION OF INDICATORS OF STREAM CONDITION FOR MAIA USING HIERARCHICAL BAYES PREDICTION MODELS

    EPA Science Inventory

    Probability surveys of stream and river resources (hereafter referred to as streams) provide reliable estimates of stream condition when the areas for the estimates have sufficient number of sample sites. Monitoring programs are frequently asked to provide estimates for areas th...

  8. Prediction of "Fear" Acquisition in Healthy Control Participants in a De Novo Fear Conditioning Paradigm

    ERIC Educational Resources Information Center

    Otto, Michael W.; Leyro, Teresa M.; Christian, Kelly; Deveney, Christen M.; Reese, Hannah; Pollack, Mark H.; Orr, Scott P.

    2007-01-01

    Studies using fear-conditioning paradigms have found that anxiety patients are more conditionable than individuals without these disorders, but these effects have been demonstrated inconsistently. It is unclear whether these findings have etiological significance or whether enhanced conditionability is linked only to certain anxiety…

  9. Childhood Nocturnal Enuresis: The Prediction of Premature Withdrawal from Behavioral Conditioning.

    ERIC Educational Resources Information Center

    Wagner, William G.; Johnson, J. T.

    1988-01-01

    Predictors of premature withdrawal from a 12-week program of behavioral conditioning for childhood nocturnal enuresis were examined for 47 children (ages 5-14). The function containing number of previous techniques used, presence of child behavior problems, and parent tolerance of enuresis was a significant predictor of early termination of…

  10. Laser conditioning characterization and damage threshold prediction of hafnia/silica multilayer mirrors by photothermal microscopy

    NASA Astrophysics Data System (ADS)

    Papandrew, A. B.; Stolz, Christopher J.; Wu, Zhouling; Loomis, Gary E.; Falabella, Steven

    2001-04-01

    Laser conditioning has been shown to improve the laser damage threshold of some optical coatings by greater than 2x. Debate continues within the damage community regarding laser-conditioning mechanisms, but it is clear that nodular ejection is one of the byproducts of the laser conditioning process. To better understand why laser conditioning is so effective, photothermal microscopy was used to measure absorption of coating defects before and after laser exposure. Although a modest absorption reduction was expected due to the lower electric field peaks within a pit and the absence of potentially absorbing nodular seeds, surprisingly, absorption reductions up to 150x were observed. Photothermal microscopy has also been successfully used to correlate laser-induced damage threshold and absorption of defects in hafnia/silica multilayer optical coatings. Defects with high absorption, as indicated by high photothermal signal, have low damage thresholds. Previously a linear correlation of damage threshold and defect photothermal signal was established with films designed and damage tested at 1(omega) (1053 nm) and Brewster's angle (56.4 degree(s)), but characterized by photothermal microscopy at 514.5 nm and near-normal angle of incidence (10 degree(s)). In this study coatings designed, characterized by photothermal microscopy, and damage tested at the same wavelength, incident angle, and polarization did not have a correlation between defect photothermal signal and absorption.

  11. Predictive Gaze during Observation of Irrational Actions in Adults with Autism Spectrum Conditions

    ERIC Educational Resources Information Center

    Marsh, L. E.; Pearson, A.; Ropar, D.; Hamilton, A. F. de C.

    2015-01-01

    Understanding irrational actions may require the observer to make mental state inferences about why an action was performed. Individuals with autism spectrum conditions (ASC) have well documented difficulties with mentalizing; however, the degree to which rationality understanding is impaired in autism is not yet clear. The present study uses…

  12. Spatial prediction models for the probable biological condition of streams and rivers in the USA

    EPA Science Inventory

    The National Rivers and Streams Assessment (NRSA) is a probability-based survey conducted by the US Environmental Protection Agency and its state and tribal partners. It provides information on the ecological condition of the rivers and streams in the conterminous USA, and the ex...

  13. Predictive Gaze during Observation of Irrational Actions in Adults with Autism Spectrum Conditions

    ERIC Educational Resources Information Center

    Marsh, L. E.; Pearson, A.; Ropar, D.; Hamilton, A. F. de C.

    2015-01-01

    Understanding irrational actions may require the observer to make mental state inferences about why an action was performed. Individuals with autism spectrum conditions (ASC) have well documented difficulties with mentalizing; however, the degree to which rationality understanding is impaired in autism is not yet clear. The present study uses…

  14. Perceived Working Conditions and Personal Resources Predicting Mental Health Counselor Well-Being

    ERIC Educational Resources Information Center

    Thompson, Isabel A.

    2012-01-01

    This study examined the influence of counselor perceived working conditions, length of time in field, counselor gender, mindfulness attitudes, compassion satisfaction, emotion-focused coping, problem focused coping, and maladaptive coping on levels of burnout and compassion fatigue in a sample of 213 mental health counselors. Cross-sectional…

  15. Prediction of Fatigue Life for CFRP/Metal Bolted Joint under Temperature Conditions

    NASA Astrophysics Data System (ADS)

    Sekine, Naoyuki; Nakada, Masayuki; Miyano, Yasushi; Kuraishi, Akira; Tsai, Stephen W.

    In 1997, we proposed a prediction method of fatigue failure load for polymer composite structures under an arbitrary frequency, load ratio (minimum load/maximum load), and temperature from the data measured by constant elongation-rate (CER) tests under various temperatures and loading rates, and by fatigue tests at a single frequency under various temperatures. In this paper, tensile CER and fatigue tests of CFRP/metal bolted joint were conducted for various temperatures and loading rates. The applicability of the proposed method was experimentally proven for the tensile fatigue failure load for this CFRP/metal bolted joint.

  16. Predicting Vegetation Condition from ASCAT Soil Water Index over Southwest India

    NASA Astrophysics Data System (ADS)

    Pfeil, Isabella Maria; Hochstöger, Simon; Amarnath, Giriraj; Pani, Peejush; Enenkel, Markus; Wagner, Wolfgang

    2017-04-01

    In India, extreme water scarcity events are expected to occur on average every five years. Record-breaking droughts affecting millions of human beings and livestock are common. If the south-west monsoon (summer monsoon) is delayed or brings less rainfall than expected, a season's harvest can be destroyed despite optimal farm management, leading to, in the worst case, life-threatening circumstances for a large number of farmers. Therefore, the monitoring of key drought indicators, such as the healthiness of the vegetation, and subsequent early warning is crucial. The aim of this work is to predict vegetation state from earth observation data instead of relying on models which need a lot of input data, increasing the complexity of error propagation, or seasonal forecasts, that are often too uncertain to be used as a regression component for a vegetation parameter. While precipitation is the main water supply for large parts of India's agricultural areas, vegetation datasets such as the Normalized Difference Vegetation Index (NDVI) provide reliable estimates of vegetation greenness that can be related to vegetation health. Satellite-derived soil moisture represents the missing link between a deficit in rainfall and the response of vegetation. In particular the water available in the root zone plays an important role for near-future vegetation health. Exploiting the added-value of root zone soil moisture is therefore crucial, and its use in vegetation studies presents an added value for drought analyses and decision-support. The soil water index (SWI) dataset derived from the Advanced Scatterometer (ASCAT) on board the Metop satellites represents the water content that is available in the root zone. This dataset shows a strong correlation with NDVI data obtained from measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS), which is exploited in this study. A linear regression function is fit to the multi-year SWI and NDVI dataset with a temporal

  17. Development of a Threshold Model to Predict Germination of Populus tomentosa Seeds after Harvest and Storage under Ambient Condition

    PubMed Central

    Wang, Wei-Qing; Cheng, Hong-Yan; Song, Song-Quan

    2013-01-01

    Effects of temperature, storage time and their combination on germination of aspen (Populus tomentosa) seeds were investigated. Aspen seeds were germinated at 5 to 30°C at 5°C intervals after storage for a period of time under 28°C and 75% relative humidity. The effect of temperature on aspen seed germination could not be effectively described by the thermal time (TT) model, which underestimated the germination rate at 5°C and poorly predicted the time courses of germination at 10, 20, 25 and 30°C. A modified TT model (MTT) which assumed a two-phased linear relationship between germination rate and temperature was more accurate in predicting the germination rate and percentage and had a higher likelihood of being correct than the TT model. The maximum lifetime threshold (MLT) model accurately described the effect of storage time on seed germination across all the germination temperatures. An aging thermal time (ATT) model combining both the TT and MLT models was developed to describe the effect of both temperature and storage time on seed germination. When the ATT model was applied to germination data across all the temperatures and storage times, it produced a relatively poor fit. Adjusting the ATT model to separately fit germination data at low and high temperatures in the suboptimal range increased the models accuracy for predicting seed germination. Both the MLT and ATT models indicate that germination of aspen seeds have distinct physiological responses to temperature within a suboptimal range. PMID:23658654

  18. A Preliminary Evaluation of Season-ahead Flood Prediction Conditioned on Large-scale Climate Drivers

    NASA Astrophysics Data System (ADS)

    Lee, Donghoon; Ward, Philip; Block, Paul

    2016-04-01

    Globally, flood disasters lead all natural hazards in terms of impacts on society, causing billions of dollars of damages each year. Typically, short-term forecasts emphasize immediate emergency actions, longer-range forecasts, on the order of months to seasons, however, can compliment short-term forecasts by focusing on disaster preparedness. In this study, the inter-annual variability of large-scale climate drivers (e.g. ENSO) is investigated to understand the prospects for skillful season-ahead flood prediction globally using PCR-GLOBWB modeled simulations. For example, global gridded correlations between discharge and Nino 3.4 are calculated, with notably strong correlations in the northwestern (-0.4~-0.6) and the southeastern (0.4~0.6) United States, and the Amazon river basin (-0.6~-0.8). Coupled interactions from multiple, simultaneous climate drivers are also evaluated. Skillful prediction has the potential to estimate season-ahead flood probabilities, flood extent, damages, and eventually integrate into early warning systems. This global approach is especially attractive for areas with limited observations and/or little capacity to develop early warning flood systems.

  19. Predicting electromyographic signals under realistic conditions using a multiscale chemo–electro–mechanical finite element model

    PubMed Central

    Mordhorst, Mylena; Heidlauf, Thomas; Röhrle, Oliver

    2015-01-01

    This paper presents a novel multiscale finite element-based framework for modelling electromyographic (EMG) signals. The framework combines (i) a biophysical description of the excitation–contraction coupling at the half-sarcomere level, (ii) a model of the action potential (AP) propagation along muscle fibres, (iii) a continuum-mechanical formulation of force generation and deformation of the muscle, and (iv) a model for predicting the intramuscular and surface EMG. Owing to the biophysical description of the half-sarcomere, the model inherently accounts for physiological properties of skeletal muscle. To demonstrate this, the influence of membrane fatigue on the EMG signal during sustained contractions is investigated. During a stimulation period of 500 ms at 100 Hz, the predicted EMG amplitude decreases by 40% and the AP propagation velocity decreases by 15%. Further, the model can take into account contraction-induced deformations of the muscle. This is demonstrated by simulating fixed-length contractions of an idealized geometry and a model of the human tibialis anterior muscle (TA). The model of the TA furthermore demonstrates that the proposed finite element model is capable of simulating realistic geometries, complex fibre architectures, and can include different types of heterogeneities. In addition, the TA model accounts for a distributed innervation zone, different fibre types and appeals to motor unit discharge times that are based on a biophysical description of the α motor neurons. PMID:25844148

  20. Predicting Aspergillus fumigatus exposure from composting facilities using a dispersion model: A conditional calibration and validation.

    PubMed

    Douglas, Philippa; Tyrrel, Sean F; Kinnersley, Robert P; Whelan, Michael; Longhurst, Philip J; Hansell, Anna L; Walsh, Kerry; Pollard, Simon J T; Drew, Gillian H

    2017-01-01

    Bioaerosols are released in elevated quantities from composting facilities and are associated with negative health effects, although dose-response relationships are unclear. Exposure levels are difficult to quantify as established sampling methods are costly, time-consuming and current data provide limited temporal and spatial information. Confidence in dispersion model outputs in this context would be advantageous to provide a more detailed exposure assessment. We present the calibration and validation of a recognised atmospheric dispersion model (ADMS) for bioaerosol exposure assessments. The model was calibrated by a trial and error optimisation of observed Aspergillus fumigatus concentrations at different locations around a composting site. Validation was performed using a second dataset of measured concentrations for a different site. The best fit between modelled and measured data was achieved when emissions were represented as a single area source, with a temperature of 29°C. Predicted bioaerosol concentrations were within an order of magnitude of measured values (1000-10,000CFU/m(3)) at the validation site, once minor adjustments were made to reflect local differences between the sites (r(2)>0.7 at 150, 300, 500 and 600m downwind of source). Results suggest that calibrated dispersion modelling can be applied to make reasonable predictions of bioaerosol exposures at multiple sites and may be used to inform site regulation and operational management. Copyright © 2016 The Authors. Published by Elsevier GmbH.. All rights reserved.

  1. Thermal conductivity of abnormally behaving liquids: Prediction methods and their applicability in microgravity conditions

    NASA Astrophysics Data System (ADS)

    Latini, G.; Passerini, G.

    1999-01-01

    Most organic and inorganic liquids show a general decrease of the thermal conductivity but very few compounds show an increase of thermal conductivity with temperature. Hydrogen and Water show an even more abnormal behavior since their thermal conductivity increases from the melting point to a reduced temperature of about 0.65-0.70 then decreases at higher temperatures. Due to their peculiar behavior, none of the general prediction methods developed for organic and inorganic liquids are effective for such substances in their saturated liquid state over the whole temperature range, from melting point to near the critical point. In this paper we present an estimation method able to evaluate thermal conductivity of Hydrogen and Water in their saturated liquid state from the melting point near to the critical point. The equation we present, as a new result of a previously introduced prediction method, links the thermal conductivity of water and Hydrogen with the reduced temperature. Tests, performed against experimental data, show a good accuracy of the method being the deviations generally less than 3% with peak deviations less than 10%.

  2. Internet-based monitoring and prediction system of coal stockpile behaviors under atmospheric conditions.

    PubMed

    Yilmaz, Nihat; Ozdeniz, A Hadi

    2010-03-01

    Spontaneous combustion on industrial-scale stockpiles causes environmental problems and economic losses for the companies consuming large amounts of coal. In this study, an effective monitoring and prediction system based on internet was developed and implemented to prevent losses and environmental problems. The system was performed in a coal stockpile with 5 m width, 10 m length, 3 m height, and having 120 t of weight. The inner temperature data of the stockpile was recorded by 17 temperature sensors placed inside the stockpile at certain points. Additionally, the data relating to the air temperature, air humidity, atmospheric pressure, wind velocity, and wind direction that are the parameters affecting the coal stockpile were also recorded. The recorded values were analyzed with artificial neural network and Statistical modeling methods for prediction of spontaneous combustion. Real-time measurement values and model outputs were published with a web page on internet. The internet-based system can also provide real-time monitoring (combustion alarms, system status) and tele-controlling (Parameter adjusting, system control) through internet exclusively with a standard web browser without the need of any additional software.

  3. A Local Forecast of Land Surface Wetness Conditions, Drought, and St. Louis Encephalitis Virus Transmission Derived from Seasonal Climate Predictions

    NASA Astrophysics Data System (ADS)

    Shaman, J.; Stieglitz, M.; Zebiak, S.; Cane, M.; Day, J. F.

    2002-12-01

    We present an ensemble local hydrologic forecast derived from the seasonal forecasts of the International Research Institute (IRI) for Climate Prediction. Three- month seasonal forecasts were used to resample historical meteorological conditions and generate ensemble forcing datasets for a TOPMODEL-based hydrology model. Eleven retrospective forecasts were run at a Florida and New York site. Forecast skill was assessed for mean area modeled water table depth (WTD), i.e. near surface soil wetness conditions, and compared with WTD simulated with observed data. Hydrology model forecast skill was evident at the Florida site but not at the New York site. At the Florida site, persistence of hydrologic conditions and local skill of the IRI seasonal forecast contributed to the local hydrologic forecast skill. This forecast will permit probabilistic prediction of future hydrologic conditions. At the Florida site, we have also quantified the link between modeled WTD (i.e. drought) and the amplification and transmission of St. Louis Encephalitis virus (SLEV). We derive an empirical relationship between modeled land surface wetness and levels of SLEV transmission associated with human clinical cases. We then combine the seasonal forecasts of local, modeled WTD with this empirical relationship and produce retrospective probabilistic seasonal forecasts of epidemic SLEV transmission in Florida. Epidemic SLEV transmission forecast skill is demonstrated. These findings will permit real-time forecast of drought and resultant SLEV transmission in Florida.

  4. Metabarcoding of benthic eukaryote communities predicts the ecological condition of estuaries.

    PubMed

    Chariton, Anthony A; Stephenson, Sarah; Morgan, Matthew J; Steven, Andrew D L; Colloff, Matthew J; Court, Leon N; Hardy, Christopher M

    2015-08-01

    DNA-derived measurements of biological composition have the potential to produce data covering all of life, and provide a tantalizing proposition for researchers and managers. We used metabarcoding to compare benthic eukaryote composition from five estuaries of varying condition. In contrast to traditional studies, we found biotic richness was greatest in the most disturbed estuary, with this being due to the large volume of extraneous material (i.e. run-off from aquaculture, agriculture and other catchment activities) being deposited in the system. In addition, we found strong correlations between composition and a number of environmental variables, including nutrients, pH and turbidity. A wide range of taxa responded to these environmental gradients, providing new insights into their sensitivities to natural and anthropogenic stressors. Metabarcoding has the capacity to bolster current monitoring techniques, enabling the decisions regarding ecological condition to be based on a more holistic view of biodiversity.

  5. Voxel-based morphometry predicts shifts in dendritic spine density and morphology with auditory fear conditioning.

    PubMed

    Keifer, O P; Hurt, R C; Gutman, D A; Keilholz, S D; Gourley, S L; Ressler, K J

    2015-07-07

    Neuroimaging has provided compelling data about the brain. Yet the underlying mechanisms of many neuroimaging techniques have not been elucidated. Here we report a voxel-based morphometry (VBM) study of Thy1-YFP mice following auditory fear conditioning complemented by confocal microscopy analysis of cortical thickness, neuronal morphometric features and nuclei size/density. Significant VBM results included the nuclei of the amygdala, the insula and the auditory cortex. There were no significant VBM changes in a control brain area. Focusing on the auditory cortex, confocal analysis showed that fear conditioning led to a significantly increased density of shorter and wider dendritic spines, while there were no spine differences in the control area. Of all the morphology metrics studied, the spine density was the only one to show significant correlation with the VBM signal. These data demonstrate that learning-induced structural changes detected by VBM may be partially explained by increases in dendritic spine density.

  6. An Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageous

    PubMed Central

    Matsumoto, Tomotaka; Mineta, Katsuhiko; Osada, Naoki; Araki, Hitoshi

    2015-01-01

    Recent studies suggest the existence of a stochasticity in gene expression (SGE) in many organisms, and its non-negligible effect on their phenotype and fitness. To date, however, how SGE affects the key parameters of population genetics are not well understood. SGE can increase the phenotypic variation and act as a load for individuals, if they are at the adaptive optimum in a stable environment. On the other hand, part of the phenotypic variation caused by SGE might become advantageous if individuals at the adaptive optimum become genetically less-adaptive, for example due to an environmental change. Furthermore, SGE of unimportant genes might have little or no fitness consequences. Thus, SGE can be advantageous, disadvantageous, or selectively neutral depending on its context. In addition, there might be a genetic basis that regulates magnitude of SGE, which is often referred to as “modifier genes,” but little is known about the conditions under which such an SGE-modifier gene evolves. In the present study, we conducted individual-based computer simulations to examine these conditions in a diploid model. In the simulations, we considered a single locus that determines organismal fitness for simplicity, and that SGE on the locus creates fitness variation in a stochastic manner. We also considered another locus that modifies the magnitude of SGE. Our results suggested that SGE was always deleterious in stable environments and increased the fixation probability of deleterious mutations in this model. Even under frequently changing environmental conditions, only very strong natural selection made SGE adaptive. These results suggest that the evolution of SGE-modifier genes requires strict balance among the strength of natural selection, magnitude of SGE, and frequency of environmental changes. However, the degree of dominance affected the condition under which SGE becomes advantageous, indicating a better opportunity for the evolution of SGE in different genetic

  7. Urinary Metabolite Profiles May be Predictive of Cognitive Performance Under Conditions of Acute Sleep Deprivation

    DTIC Science & Technology

    2016-01-01

    A.I. Cumulative sleepiness, mood disturbance , and psychomotor vigilance performance decrements during a week of sleep restricted to 4-5 hours per...Cognitive Performance Under Conditions of Acute Sleep Deprivation 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER Nicholas J...and sustained actions in military and civilian operational environments typically lead to reduced sleep normally required to perform optimally

  8. An Evaluation of the Pavement Condition Index Prediction Model for Flexible Airfield Pavements.

    DTIC Science & Technology

    1983-09-01

    NO. 3 . RECiPIENT’S CATALOG NUMBER LSSR 11-83 LID,_ 13 -______9_ 4. TITLE (end Subtitle) S. TYPE OF REPORT & PERIOD COVERED AN EVALUATION OF THE...Flexible Models. ......... 42 Current Model Development ...........54 Dronen Model................67 Conclusion ................... 71 iv CHAPTER Page 3 ...13 2-2 General Guide for Establishing Rigid Pavement Condition................16 2- 3 Types of Distress in Airfield Pavement

  9. Learning to predict and control harmful events: chronic pain and conditioning.

    PubMed

    Vlaeyen, Johan W S

    2015-04-01

    Pain is a biologically relevant signal and response to bodily threat, associated with the urge to restore the integrity of the body. Immediate protective responses include increased arousal, selective attention, escape, and facial expressions, followed by recuperative avoidance and safety-seeking behaviors. To facilitate early and effective protection against future bodily threat or injury, learning takes place rapidly. Learning is the observable change in behavior due to events in the internal and external environmental and includes nonassociative (habituation and sensitization) and associative learning (Pavlovian and operant conditioning). Once acquired, these knowledge representations remain stored in memory and may generalize to perceptually or functionally similar events. Moreover, these processes are not just a consequence of pain; they may directly influence pain perception. In contrast to the rapid acquisition of learned responses, their extinction is slow, fragile, context dependent and only occurs through inhibitory processes. Here, we review features of associative forms of learning in humans that contribute to pain, pain-related distress, and disability and discuss promising future directions. Although conditioning has a long and honorable history, a conditioning perspective still might open new windows on novel treatment modalities that facilitate the well-being of individuals with chronic pain.

  10. Bioenergetics of Nutrient Reserves and Metabolism in Spiny Lobster Juveniles Sagmariasus verreauxi: Predicting Nutritional Condition from Hemolymph Biochemistry.

    PubMed

    Simon, C J; Fitzgibbon, Q P; Battison, A; Carter, C G; Battaglene, S C

    2015-01-01

    The nutritional condition of cultured Sagmariasus verreauxi juveniles over the molt and during starvation was investigated by studying their metabolism, bioenergetics of nutrient reserves, and hemolymph biochemistry. Juveniles were shown to downregulate standard metabolic rate by as much as 52% within 14 d during starvation. Hepatopancreas (HP) lipid was prioritized as a source of energy, but this reserve represented only between 1% and 13% of the total measured energy reserve and was used quickly during starvation, especially in the immediate postmolt period when as much as 60% was depleted within 3 d. Abdominal muscle (AM) protein represented between 74% and 90% of the total measured energy reserve in juvenile lobsters, and as much as 40% of available AM protein energy was used over 28 d of starvation after the molt. Carbohydrate reserves represented less than 2% of the measured total energy reserve in fed intermolt lobsters and provided negligible energy during starvation. Eighteen hemolymph parameters were measured to identify a nondestructive biomarker of condition that would reflect accurately the state of energy reserves of the lobster. Among these, the hemolymph Brix index was the most accurate and practical method to predict HP lipid and the total energy content of both the HP and the AM in juvenile S. verreauxi. The Brix index was strongly correlated with hemolymph proteins, triglyceride, cholesterol, calcium, and phosphorus concentrations, as well as lipase activity; all were useful in predicting condition. Electrolytes such as chloride, magnesium, and potassium and metabolites such as glucose and lactate were poor indicators of nutritional condition. Uric acid and the "albumin"-to-"globulin" ratio provided complementary information to the Brix index, which may assist in determining nutritional condition of wild juvenile lobsters of unknown intermolt development. This study will greatly assist future ecological studies examining the nutritional condition

  11. Accelerated Degradation Test and Predictive Failure Analysis of B10 Copper-Nickel Alloy under Marine Environmental Conditions

    PubMed Central

    Sun, Bo; Ye, Tianyuan; Feng, Qiang; Yao, Jinghua; Wei, Mumeng

    2015-01-01

    This paper studies the corrosion behavior of B10 copper-nickel alloy in marine environment. Accelerated degradation test under marine environmental conditions was designed and performed based on the accelerated testing principle and the corrosion degradation mechanism. With the prolongation of marine corrosion time, the thickness of Cu2O film increased gradually. Its corrosion product was Cu2(OH)3Cl, which increased in quantity over time. Cl− was the major factor responsible for the marine corrosion of copper and copper alloy. Through the nonlinear fitting of corrosion rate and corrosion quantity (corrosion weight loss), degradation data of different corrosion cycles, the quantitative effects of two major factors, i.e., dissolved oxygen (DO) and corrosion medium temperature, on corrosion behavior of copper alloy were analyzed. The corrosion failure prediction models under different ambient conditions were built. One-day corrosion weight loss under oxygenated stirring conditions was equivalent to 1.31-day weight loss under stationary conditions, and the corrosion rate under oxygenated conditions was 1.31 times higher than that under stationary conditions. In addition, corrosion medium temperature had a significant effect on the corrosion of B10 copper sheet. PMID:28793549

  12. Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model.

    PubMed

    Olatinwo, Rabiu O; Prabha, Thara V; Paz, Joel O; Hoogenboom, Gerrit

    2012-03-01

    Early leaf spot of peanut (Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

  13. Nondestructive prediction of point source pyroshock response spectra based on experimental conditioning of laser-induced shocks

    NASA Astrophysics Data System (ADS)

    Jang, Jae-Kyeong; Lee, Jung-Ryul

    2014-09-01

    Pyroshock can easily cause failures in electronic and optical components that are sensitive to high-frequency energy. Pyroshock is generated during explosive-based pyrotechnical events, such as the separation of boosters from a space shuttle and the separation of satellites from a space launcher. Therefore, the prediction of high-frequency structural response, particularly the shock response spectrum (SRS), is important for safe operation of pyrotechnical devices. In general, real explosive testing using distributed accelerometers is widely used. This paper proposes a technology to replace the expensive, dangerous, low-repeatability explosive test with a laser-induced shock test based on a laser beam and in-line filter conditioning. This method does not use any special numerical signal processing. Two different experiments based on explosive and laser excitation were performed with a 2-mm thick aluminum plate. The optimum laser-induced shock experimental conditions to predict real pyroshock were investigated while considering the size, energy, and fluence of the laser beam as parameters. The similarity of the SRS of the laser-induced shock to that of the real explosive pyroshock was evaluated based on the mean acceleration difference (MAD, %). The experimentally determined optimal conditions were also applied to four points on the path of a pyroshock propagation. To match the SRS at each point, the laser-induced shock was amplified, for which three different gain concepts are proposed: the initial gain, optimized gain, and constant gain. The proposed technology enables nondestructive pyro SRS prediction by conditioning the laser-induced shock to obtain an SRS with high similarity to the real pyroshock.

  14. Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model

    NASA Astrophysics Data System (ADS)

    Olatinwo, Rabiu O.; Prabha, Thara V.; Paz, Joel O.; Hoogenboom, Gerrit

    2012-03-01

    Early leaf spot of peanut ( Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

  15. Predictive Models for Modulus of Rupture and Modulus of Elasticity of Particleboard Manufactured in Different Pressing Conditions

    NASA Astrophysics Data System (ADS)

    Tiryaki, Sebahattin; Aras, Uğur; Kalaycıoğlu, Hülya; Erişir, Emir; Aydın, Aytaç

    2017-07-01

    Determining the mechanical properties of particleboard has gained a great importance due to its increasing usage as a building material in recent years. This study aims to develop artificial neural network (ANN) and multiple linear regression (MLR) models for predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of particleboard depending on different pressing temperature, pressing time, pressing pressure and resin type. Experimental results indicated that the increased pressing temperature, time and pressure in manufacturing process generally improved the mechanical properties of particleboard. It was also seen that ANN and MLR models were highly successful in predicting the MOR and MOE of particleboard under given conditions. On the other hand, a comparison between ANN and MLR revealed that the ANN was superior compared to the MLR in predicting the MOR and MOE. Finally, the findings of this study are expected to provide beneficial insights for practitioners to better understand usability of such composite materials for engineering applications and to better assess the effects of pressing conditions on the MOR and MOE of particleboard.

  16. Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

    NASA Astrophysics Data System (ADS)

    Zheng, Qin; Yang, Zubin; Sha, Jianxin; Yan, Jun

    2017-02-01

    In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies a certain constraint condition and causes the largest prediction error at the prediction time. The CNOP has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model in the prediction variable will lead to failure of the ADJ-CNOP method, and (2) when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making a false estimation of the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithm, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or when the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. In addition, to check the estimation accuracy of the LBMPT presented by PSO-CNOP and ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the

  17. Upstream watershed condition predicts rural children's health across 35 developing countries.

    PubMed

    Herrera, Diego; Ellis, Alicia; Fisher, Brendan; Golden, Christopher D; Johnson, Kiersten; Mulligan, Mark; Pfaff, Alexander; Treuer, Timothy; Ricketts, Taylor H

    2017-10-09

    Diarrheal disease (DD) due to contaminated water is a major cause of child mortality globally. Forests and wetlands can provide ecosystem services that help maintain water quality. To understand the connections between land cover and childhood DD, we compiled a database of 293,362 children in 35 countries with information on health, socioeconomic factors, climate, and watershed condition. Using hierarchical models, here we find that higher upstream tree cover is associated with lower probability of DD downstream. This effect is significant for rural households but not for urban households, suggesting differing dependence on watershed conditions. In rural areas, the effect of a 30% increase in upstream tree cover is similar to the effect of improved sanitation, but smaller than the effect of improved water source, wealth or education. We conclude that maintaining natural capital within watersheds can be an important public health investment, especially for populations with low levels of built capital.Globally diarrheal disease through contaminated water sources is a major cause of child mortality. Here, the authors compile a database of 293,362 children in 35 countries and find that upstream tree cover is linked to a lower probability of diarrheal disease and that increasing tree cover may lower mortality.

  18. Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT.

    PubMed

    Lopez-Martin, Manuel; Carro, Belen; Sanchez-Esguevillas, Antonio; Lloret, Jaime

    2017-08-26

    The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.

  19. Chemiluminescence as a condition monitoring method for thermal aging and lifetime prediction of an HTPB elastomer.

    SciTech Connect

    Gillen, Kenneth Todd; Minier, Leanna M. G.; Celina, Mathias Christopher; Trujillo, Ana B.

    2007-03-01

    Chemiluminescence (CL) has been applied as a condition monitoring technique to assess aging related changes in a hydroxyl-terminated-polybutadiene based polyurethane elastomer. Initial thermal aging of this polymer was conducted between 110 and 50 C. Two CL methods were applied to examine the degradative changes that had occurred in these aged samples: isothermal 'wear-out' experiments under oxygen yielding initial CL intensity and 'wear-out' time data, and temperature ramp experiments under inert conditions as a measure of previously accumulated hydroperoxides or other reactive species. The sensitivities of these CL features to prior aging exposure of the polymer were evaluated on the basis of qualifying this method as a quick screening technique for quantification of degradation levels. Both the techniques yielded data representing the aging trends in this material via correlation with mechanical property changes. Initial CL rates from the isothermal experiments are the most sensitive and suitable approach for documenting material changes during the early part of thermal aging.

  20. Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

    PubMed Central

    Carro, Belen; Sanchez-Esguevillas, Antonio

    2017-01-01

    The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery. PMID:28846608

  1. Suitability of different comfort indices for the prediction of thermal conditions in tree-covered outdoor spaces in arid cities

    NASA Astrophysics Data System (ADS)

    Ruiz, María Angélica; Correa, Erica Norma

    2015-10-01

    Outdoor thermal comfort is one of the most influential factors in the habitability of a space. Thermal level is defined not only by climate variables but also by the adaptation of people to the environment. This study presents a comparison between inductive and deductive thermal comfort models, contrasted with subjective reports, in order to identify which of the models can be used to most correctly predict thermal comfort in tree-covered outdoor spaces of the Mendoza Metropolitan Area, an intensely forested and open city located in an arid zone. Interviews and microclimatic measurements were carried out in winter 2010 and in summer 2011. Six widely used indices were selected according to different levels of complexity: the Temperature-Humidity Index (THI), Vinje's Comfort Index (PE), Thermal Sensation Index (TS), the Predicted Mean Vote (PMV), the COMFA model's energy balance (S), and the Physiological Equivalent Temperature (PET). The results show that the predictive models evaluated show percentages of predictive ability lower than 25 %. Despite this low indicator, inductive methods are adequate for obtaining a diagnosis of the degree and frequency in which a space is comfortable or not whereas deductive methods are recommended to influence urban design strategies. In addition, it is necessary to develop local models to evaluate perceived thermal comfort more adequately. This type of tool is very useful in the design and evaluation of the thermal conditions in outdoor spaces, based not only to climatic criteria but also subjective sensations.

  2. Head losses prediction and analysis in a bulb turbine draft tube under different operating conditions using unsteady simulations

    NASA Astrophysics Data System (ADS)

    Wilhelm, S.; Balarac, G.; Métais, O.; Ségoufin, C.

    2016-11-01

    Flow prediction in a bulb turbine draft tube is conducted for two operating points using Unsteady RANS (URANS) simulations and Large Eddy Simulations (LES). The inlet boundary condition of the draft tube calculation is a rotating two dimensional velocity profile exported from a RANS guide vane- runner calculation. Numerical results are compared with experimental data in order to validate the flow field and head losses prediction. Velocity profiles prediction is improved with LES in the center of the draft tube compared to URANS results. Moreover, more complex flow structures are obtained with LES. A local analysis of the predicted flow field using the energy balance in the draft tube is then introduced in order to detect the hydrodynamic instabilities responsible for head losses in the draft tube. In particular, the production of turbulent kinetic energy next to the draft tube wall and in the central vortex structure is found to be responsible for a large part of the mean kinetic energy dissipation in the draft tube and thus for head losses. This analysis is used in order to understand the differences in head losses for different operating points. The numerical methodology could then be improved thanks to an in-depth understanding of the local flow topology.

  3. Interval neural networks

    SciTech Connect

    Patil, R.B.

    1995-05-01

    Traditional neural networks like multi-layered perceptrons (MLP) use example patterns, i.e., pairs of real-valued observation vectors, ({rvec x},{rvec y}), to approximate function {cflx f}({rvec x}) = {rvec y}. To determine the parameters of the approximation, a special version of the gradient descent method called back-propagation is widely used. In many situations, observations of the input and output variables are not precise; instead, we usually have intervals of possible values. The imprecision could be due to the limited accuracy of the measuring instrument or could reflect genuine uncertainty in the observed variables. In such situation input and output data consist of mixed data types; intervals and precise numbers. Function approximation in interval domains is considered in this paper. We discuss a modification of the classical backpropagation learning algorithm to interval domains. Results are presented with simple examples demonstrating few properties of nonlinear interval mapping as noise resistance and finding set of solutions to the function approximation problem.

  4. Thermal conditions in freezing chambers and prediction of the thermophysiological responses of workers

    NASA Astrophysics Data System (ADS)

    Raimundo, A. M.; Oliveira, A. V. M.; Gaspar, A. R.; Quintela, D. A.

    2015-11-01

    The present work is dedicated to the assessment of the cold thermal strain of human beings working within freezing chambers. To obtain the present results, both field measurements and a numerical procedure based on a modified version of the Stolwijk thermoregulation model were used. Eighteen freezing chambers were considered. A wide range of physical parameters of the cold stores, the workers clothing insulation, and the working and recovering periods were observed. The combination of these environmental and individual parameters lead to different levels of thermal stress, which were grouped under three categories. Some good practices were observed in the field evaluations, namely situations with appropriate level of clothing protection and limited duration of exposure to cold avoiding unacceptable level of hypothermia. However, the clothing ensembles normally used by the workers do not provide the minimum required insulation, which suggests the possibility of the whole body cooling for levels higher than admissible. The numerical predictions corroborate the main conclusions of the field survey. The results obtained with both methodologies clearly show that, for the low temperature of the freezing chambers, the clothing insulation is insufficient, the exposure periods are too long, and the recovering periods are inadequate. Thus, high levels of physiological strain can indeed be reached by human beings under such working environments.

  5. Prediction of the conditions for the consumption of game by Polish consumers.

    PubMed

    Kwiecińska, Katarzyna; Kosicka-Gębska, Małgorzata; Gębski, Jerzy; Gutkowska, Krystyna

    2017-09-01

    Due to the changing needs of consumers and the increased risk of diet-related diseases, today's consumers are forced to seek alternative types of meat. It should, on one hand be tasty, and on the other will improve the health of the consumer. Game is considered to be such a meat. Although Poland is one of the leading producers and exporters of game in Europe, the level of its consumption is very low at about 0.08kg/person/year. Based on quantitative data from 1000 respondents a model predicting the consumption of wild game based on logistic regression has been prepared. It was demonstrated that consumers are likely to increase their consumption of game, provided that it will have a higher quality and greater commercial availability. A higher propensity to change eating habits in respect of game was displayed mainly by men, city dwellers and those who evaluated their own knowledge on nutritional and diet higher than others. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. The Value of Conditioning Data for Prediction of Conservative Solute Transport at the Oyster Site, Virginia

    SciTech Connect

    Scheibe, Timothy D.

    2001-12-01

    The large and diverse body of subsurface characterization data generated at a field research site near Oyster, VA provides a unique opportunity to test various approaches for characterizing field-scale heterogeneity in aquifer properties and modeling subsurface flow and transport. We are using observed bromide breakthrough curves (BTCs) from an injection experiment conducted in 19