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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. PMID:25603651

  2. 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.

  3. 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…

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

    PubMed Central

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

    2016-01-01

    Objectives 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 τ2 or I2, 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. Design 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. Results In 72.4% of 479 statistically significant (random-effects p<0.05) meta-analyses in the Cochrane Database 2009–2013 with heterogeneity (I2>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. Conclusions 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. PMID:27406637

  5. PREDICTION INTERVALS FOR INTEGRALS OF GAUSSIAN RANDOM FIELDS.

    PubMed

    De Oliveira, Victor; Kone, Bazoumana

    2015-03-01

    Methodology is proposed for the construction of prediction intervals for integrals of Gaussian random fields over bounded regions (called block averages in the geostatistical literature) based on observations at a finite set of sampling locations. Two bootstrap calibration algorithms are proposed, termed indirect and direct, aimed at improving upon plug-in prediction intervals in terms of coverage probability. A simulation study is carried out that illustrates the effectiveness of both procedures, and these procedures are applied to estimate block averages of chromium traces in a potentially contaminated region in Switzerland. PMID:25431507

  6. PREDICTION INTERVALS FOR INTEGRALS OF GAUSSIAN RANDOM FIELDS

    PubMed Central

    De Oliveira, Victor; Kone, Bazoumana

    2014-01-01

    Methodology is proposed for the construction of prediction intervals for integrals of Gaussian random fields over bounded regions (called block averages in the geostatistical literature) based on observations at a finite set of sampling locations. Two bootstrap calibration algorithms are proposed, termed indirect and direct, aimed at improving upon plug-in prediction intervals in terms of coverage probability. A simulation study is carried out that illustrates the effectiveness of both procedures, and these procedures are applied to estimate block averages of chromium traces in a potentially contaminated region in Switzerland. PMID:25431507

  7. 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.

  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. Interval Prediction of Molecular Properties in Parametrized Quantum Chemistry

    NASA Astrophysics Data System (ADS)

    Edwards, David E.; Zubarev, Dmitry Yu.; Packard, Andrew; Lester, William A.; Frenklach, Michael

    2014-06-01

    The accurate evaluation of molecular properties lies at the core of predictive physical models. Most reliable quantum-chemical calculations are limited to smaller molecular systems while purely empirical approaches are limited in accuracy and reliability. A promising approach is to employ a quantum-mechanical formalism with simplifications and to compensate for the latter with parametrization. We propose a strategy of directly predicting the uncertainty interval for a property of interest, based on training-data uncertainties, which sidesteps the need for an optimum set of parameters.

  10. 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

  11. 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

  12. 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.

  13. Selective activation of a putative reinforcement signal conditions cued interval timing in primary visual cortex

    PubMed Central

    Liu, Cheng-Hang; Coleman, Jason E.; Davoudi, Heydar; Zhang, Kechen; Hussain Shuler, Marshall G.

    2015-01-01

    Summary As a consequence of conditioning visual cues with delayed reward, cue-evoked neural activity that predicts the time of expected future reward emerges in the primary visual cortex (V1). We hypothesized that this reward timing activity is engendered by a reinforcement signal conveying reward acquisition to V1. In lieu of behavioral conditioning, we assessed in vivo whether selective activation of either basal forebrain (BF) or cholinergic innervation is sufficient to condition cued interval timing activity. Substituting for actual reward, optogenetic activation of BF or cholinergic input within V1 at fixed delays following visual stimulation entrains neural responses that mimic behaviorally-conditioned reward timing activity. Optogenetically-conditioned neural responses express cue-evoked temporal intervals that correspond to the conditioning intervals, are bidirectionally modifiable, display experience-dependent refinement, and exhibit a scale invariance to the encoded delay. Our results demonstrate that the activation of BF or cholinergic input within V1is sufficient to encode cued interval timing activity, and indicate that V1 itself is a substrate for associative learning that may inform the timing of visually-cued behaviors. PMID:26004763

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. Predicting clutter during anomalous propagation conditions

    NASA Astrophysics Data System (ADS)

    Lee, Susan C.; Maurer, Donald E.; Musser, Keith L.

    1988-06-01

    Excessive clutter caused by anomalous propagation conditions severely degrades radar performance in many regions of the world. This article describes methods that can be used to predict anomalous clutter amplitude for site-specific radar parameters, terrain features, and atmospheric conditions and to predict the effects of radar Doppler processing on evaporation-ducted sea clutter.

  20. ONTOGENY OF EYEBLINK CONDITIONING IN THE RAT: EFFECTS OF US INTENSITY AND INTERSTIMULUS INTERVAL ON DELAY CONDITIONING

    EPA Science Inventory

    Two experiments examined eyeblink conditioning in rat pups (17 or 24 days of age) as a function of US intensity (Experiment 1) and interstimulus interval [(ISI)] Experiment 2]. n Experiment 1 pups received 3 sessions of delay conditioning with a tone CS (380 ms, 2.8 kHz, 90 dB (S...

  1. 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.

  2. Multilevel, discrete, point-interval data can predict bioattenuation`s potential

    SciTech Connect

    Kabis, T.W.

    1996-05-01

    Closely spaced, discrete-interval groundwater sampling is critical for monitoring aqueous-phase contaminants at hazardous waste sites. Data obtained from discrete-interval sampling devices accurately represent horizontal and vertical extants of contaminant plumes. Discrete point-interval groundwater sampling has been tested in various applications, including natural- and forced-gradient tracer tests plume delineation, and in identifying discrete zones of microbial activity and vertical chemical gradients within an aquifer. An increasingly popular strategy is to avoid active remediation in favor of natural, in-situ attenuation processes. Evaluation of natural attenuation, particularly in-situ bioremediation resulting from multiple terminal electron acceptors, requires site-specific data. Here, multilevel, discrete, point-interval groundwater sampling data is critical. Because groundwater samples from standard monitoring wells often are derived from large vertical sampling zones, the resulting data presents a smeared picture of chemical-microbial conditions; moreover, the potential for natural in-situ bioattenuation can be under- or overestimated without multilevel, discrete, point-interval data. Discrete, point-interval samplers provide a sound basis for evaluating remediation options. One potential obstacle has been the lack of a multipurpose, cost-effective sampler that is operational under a variety of field conditions.

  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. Prediction intervals: Placing real bounds on regression-based allometric estimates of biomass.

    PubMed

    Ward, Peter J

    2015-07-01

    Biomass allometry studies routinely assume that regression models can be applied across species and sites, and that goodness of fit of a regression model to its derivation dataset indicates both the relevance of the model to a new dataset and the likely error. Assuming that a model is relevant for a new sample, a prediction interval is a useful error measure for stand mass. Prediction coverage tests whether the model and hence the interval are appropriate in the new sample. Data for three similar shrubby species from four similar sites were combined in various ways to test the impact of varying levels of biodiverse heterogeneity on the performance of the four models most commonly used in published biomass studies. No one model performed consistently well predicting new data, and validation checks were not good indicators of prediction coverage. The highly variable results suggest that the common models might contain insufficient variables. Euclidean distance was used to quantify the relative similarity of samples as a possible means of estimating prediction coverage; it proved unsuccessful with these data. PMID:25974741

  5. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals.

    PubMed

    Doré, Bruce P; Meksin, Robert; Mather, Mara; Hirst, William; Ochsner, Kevin N

    2016-06-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting (a) the overall intensity of their future negative emotion, and (b) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. (PsycINFO Database Record PMID:27100309

  6. 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

  7. 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)

  8. 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.

  9. 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.

  10. 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. PMID:25532191

  11. Demographic factors predict magnitude of conditioned fear.

    PubMed

    Rosenbaum, Blake L; Bui, Eric; Marin, Marie-France; Holt, Daphne J; Lasko, Natasha B; Pitman, Roger K; Orr, Scott P; Milad, Mohammed R

    2015-10-01

    There is substantial variability across individuals in the magnitudes of their skin conductance (SC) responses during the acquisition and extinction of conditioned fear. To manage this variability, subjects may be matched for demographic variables, such as age, gender and education. However, limited data exist addressing how much variability in conditioned SC responses is actually explained by these variables. The present study assessed the influence of age, gender and education on the SC responses of 222 subjects who underwent the same differential conditioning paradigm. The demographic variables were found to predict a small but significant amount of variability in conditioned responding during fear acquisition, but not fear extinction learning or extinction recall. A larger differential change in SC during acquisition was associated with more education. Older participants and women showed smaller differential SC during acquisition. Our findings support the need to consider age, gender and education when studying fear acquisition but not necessarily when examining fear extinction learning and recall. Variability in demographic factors across studies may partially explain the difficulty in reproducing some SC findings. PMID:26151498

  12. Conrad: gene prediction using conditional random fields.

    PubMed

    DeCaprio, David; Vinson, Jade P; Pearson, Matthew D; Montgomery, Philip; Doherty, Matthew; Galagan, James E

    2007-09-01

    We present Conrad, the first comparative gene predictor based on semi-Markov conditional random fields (SMCRFs). Unlike the best standalone gene predictors, which are based on generalized hidden Markov models (GHMMs) and trained by maximum likelihood, Conrad is discriminatively trained to maximize annotation accuracy. In addition, unlike the best annotation pipelines, which rely on heuristic and ad hoc decision rules to combine standalone gene predictors with additional information such as ESTs and protein homology, Conrad encodes all sources of information as features and treats all features equally in the training and inference algorithms. Conrad outperforms the best standalone gene predictors in cross-validation and whole chromosome testing on two fungi with vastly different gene structures. The performance improvement arises from the SMCRF's discriminative training methods and their ability to easily incorporate diverse types of information by encoding them as feature functions. On Cryptococcus neoformans, configuring Conrad to reproduce the predictions of a two-species phylo-GHMM closely matches the performance of Twinscan. Enabling discriminative training increases performance, and adding new feature functions further increases performance, achieving a level of accuracy that is unprecedented for this organism. Similar results are obtained on Aspergillus nidulans comparing Conrad versus Fgenesh. SMCRFs are a promising framework for gene prediction because of their highly modular nature, simplifying the process of designing and testing potential indicators of gene structure. Conrad's implementation of SMCRFs advances the state of the art in gene prediction in fungi and provides a robust platform for both current application and future research. PMID:17690204

  13. 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.

  14. 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

  15. Inhibitory effect of intensity and interstimulus interval of conditioning stimuli on somatosensory evoked magnetic fields.

    PubMed

    Onishi, Hideaki; Sugawara, Kazuhiro; Yamashiro, Koya; Sato, Daisuke; Kirimoto, Hikari; Tamaki, Hiroyuki; Shirozu, Hiroshi; Kameyama, Shigeki

    2016-08-01

    Magnetoencephalography (MEG) recordings were performed to investigate the inhibitory effects of conditioning stimuli with various types of interstimulus intervals (ISIs) or intensities on somatosensory evoked magnetic fields (SEFs) using a 306-ch whole-head MEG system. Twenty-three healthy volunteers participated in this study. Electrical stimuli were applied to the right median nerve at the wrist. Six pulse trains with ISIs of 500 ms were presented in Experiment 1. A paired-pulse paradigm with three kinds of conditioning stimulus (CON) intensities, 500 ms before the test stimulus (TS), was applied in Experiment 2. Finally, three CONs 500 or 1000 ms before TS were presented in Experiment 3. Three main SEF deflections (N20m, P35m, and P60m) were observed, and the source activities of P35m and P60m significantly decreased after the 2nd pulse of a six pulse trains. These source activities also significantly decreased with increasing intensity of CON. In addition, these attenuations of source activities were affected by CON-CON or CON-TS intervals. These results indicated that the source activities were modulated by the intensity and ISIs of CONs. Furthermore, P35m after the stimulation were very sensitive to CONs; however, the attenuation of P60m after the stimulation lasted for a longer period than that of P35m. Our findings suggest that the conditioning stimulation had inhibitory effects on subsequent evoked cortical responses for more than 500 ms. Our results also provide important clues about the nature of short-latency somatosensory responses in human studies. PMID:27319980

  16. 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

  17. 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.

  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). PMID:27026570

  19. 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.

  20. Personalized Metabolomics for Predicting Glucose Tolerance Changes in Sedentary Women After High-Intensity Interval Training

    PubMed Central

    Kuehnbaum, Naomi L.; Gillen, Jenna B.; Gibala, Martin J.; Britz-McKibbin, Philip

    2014-01-01

    High-intensity interval training (HIIT) offers a practical approach for enhancing cardiorespiratory fitness, however its role in improving glucose regulation among sedentary yet normoglycemic women remains unclear. Herein, multi-segment injection capillary electrophoresis-mass spectrometry is used as a high-throughput platform in metabolomics to assess dynamic responses of overweight/obese women (BMI > 25, n = 11) to standardized oral glucose tolerance tests (OGTTs) performed before and after a 6-week HIIT intervention. Various statistical methods were used to classify plasma metabolic signatures associated with post-prandial glucose and/or training status when using a repeated measures/cross-over study design. Branched-chain/aromatic amino acids and other intermediates of urea cycle and carnitine metabolism decreased over time in plasma after oral glucose loading. Adaptive exercise-induced changes to plasma thiol redox and orthinine status were measured for trained subjects while at rest in a fasting state. A multi-linear regression model was developed to predict changes in glucose tolerance based on a panel of plasma metabolites measured for naïve subjects in their untrained state. Since treatment outcomes to physical activity are variable between-subjects, prognostic markers offer a novel approach to screen for potential negative responders while designing lifestyle modifications that maximize the salutary benefits of exercise for diabetes prevention on an individual level. PMID:25164777

  1. 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. PMID:24807030

  2. Long-interval facilitation and inhibition are differentially affected by conditioning stimulus intensity over different time courses.

    PubMed

    Vallence, Ann-Maree; Schneider, Luke A; Pitcher, Julia B; Ridding, Michael C

    2014-06-01

    Intracortical facilitatory and inhibitory processes in the primary motor cortex (M1) play an important role in both the preparation and execution of motor tasks. Here we aimed to (1) confirm the existence of, and further characterise, intracortical facilitation at long conditioning-test stimulus intervals at subthreshold conditioning stimulus (CS) intensities and (2) identify the threshold for long-interval intracortical inhibition (LICI) at different inter-stimulus intervals (ISIs). To examine facilitation, stimulus-response curves at ISIs of 100 and 150 ms were obtained using a range of subthreshold CS intensities. LICI stimulus-response curves were also obtained using varying CS intensities at ISIs of 100 (LICI100) and 150 ms (LICI150). Facilitation of the conditioned MEP was observed at subthreshold CS intensities at an ISI of 100 ms. LICI100 was observed at a lower CS intensity than LICI150. First, we provide evidence of a long-interval facilitation and provide some evidence consistent with a cortical origin of this facilitation. Second, the lower threshold for evoking LICI100 than LICI150 suggests an intensity-duration effect whereby a more intense CS results in longer duration LICI. Investigation of the interaction between LICI and long-interval facilitation might help to elucidate the functional importance of these processes. PMID:24704380

  3. 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.

  4. Conditional Standard Error of Measurement in Prediction.

    ERIC Educational Resources Information Center

    Woodruff, David

    1990-01-01

    A method of estimating conditional standard error of measurement at specific score/ability levels is described that avoids theoretical problems identified for previous methods. The method focuses on variance of observed scores conditional on a fixed value of an observed parallel measurement, decomposing these variances into true and error parts.…

  5. 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…

  6. 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…

  7. Logistic regression analysis to predict weaning-to-estrous interval in first-litter gilts

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Delayed return to estrus after weaning is a significant problem for swine producers. In this study, we investigated the relationships between weaning-to-estrous interval (WEI) and body weight (BW), back fat (BF), plasma leptin (L), glucose (G), albumin (A), urea nitrogen (PUN) concentrations and lit...

  8. 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

  9. 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. PMID:27571518

  10. Conditional monogyny: female quality predicts male faithfulness

    PubMed Central

    2012-01-01

    Introduction Male monogyny in the absence of paternal investment is arguably one of the most puzzling mating systems. Recent evidence suggests that males of monogynous species adjust their life-history and their mating decision to shifting spatial and temporal selection regimes. In the cannibalistic wasp spider Argiope bruennichi males can be either monogynous or mate with a maximum of two females. We studied factors underlying male mating decisions in a natural population over a whole mating season. We documented all matings and categorized the males into single-mated and double-mated monogynous as well as bigynous males. Results We found that all categories were continuously present with relatively stable frequencies despite changes in the operational sex ratio. Males were more likely monogynous when copulating with relatively heavy and old females and otherwise bigynous. Conclusion Our results imply that males make conditional mating decisions based on the quality of the first female they encounter but do not adjust their mating tactic to the local selection regime. PMID:22533854

  11. 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. PMID:26140689

  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. 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…

  14. 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.

  15. 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…

  16. Prediction of left ventricular peak ejection velocity by preceding and prepreceding RR intervals in atrial fibrillation: a new method to adjust the influence between two intervals.

    PubMed Central

    Ko, Hong Sook; Lee, Kwang Je; Kim, Sang Wook; Kim, Tae Ho; Kim, Chee Jeong; Ryu, Wang Seong

    2002-01-01

    In atrial fibrillation, cardiac performance is dependent on both preceding RR (RR-1) and prepreceding RR (RR-2) intervals. However, relative contributions were not well defined. Left ventricular outflow peak ejection velocity (Vpe) was measured by echocardiography from 21 patients. The relation between RR-1 and Vpe could be divided into two zones; steep slope in short RR-1 intervals (< or =0.5 sec) and plateau in long RR-1 intervals (> 0.5 sec). RR-2 had a weak negative association with Vpe. The mean squared correlation coefficient (r2) between RR-2 and Vpe was 0.15 +/-0.13 and improved to 0.29+/-0.21 (p<0.001), when coordinates with RR-1 < or =0.5 sec were excluded. The RR-1 was positively associated with Vpe. The mean r2 between RR-1 and Vpe was 0.52+/-0.17 and improved to 0.72+/-0.11 (p<0.001), when adjusted by RR-2. Simple linear regression analysis showed that mean RR interval, age, fractional shortening (FS), and mean peak velocity were negatively correlated with modified r2 between RR-2 and Vpe. Multiple stepwise regression analysis revealed that mean RR interval (r2=0.32) and FS (r2=0.16) were significant. In summary, simple modification could improve the relationship of both RR-1 and RR-2 with cardiac performance. RR-2 might play a more role in cardiac performance than previously expected, and when cardiac function was impaired. PMID:12482995

  17. 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. PMID:2621757

  18. Hormone naïve prostate cancer: predicting and maximizing response intervals

    PubMed Central

    Moul, Judd W

    2015-01-01

    Hormone naïve advanced prostate cancer is subdivided into two disease states: biochemical recurrence and traditional M1 (metastatic) prostate cancer and characterized by no prior hormonal therapy or androgen deprivation therapy (ADT). In biochemical recurrence/prostate-specific antigen (PSA) recurrence, men should be risk-stratified based on their PSA doubling time, the Gleason score and the timing of the recurrence. In general, only men who are at high risk should be considered for early/immediate ADT although this is best done using shared decision with the patient. The type of ADT to be used in biochemical recurrence ranging from oral-only peripheral blockade (peripheral androgen deprivation) to complete hormonal therapy (combined androgen blockade [CAB]) remains in debate owing to lack of randomized controlled trials (RCT). However, there is good RCT support for use of intermittent hormonal therapy (IHT). There is also limited research on biomarker response (PSA and testosterone decline) to predict prognosis. On the other hand, in the setting of M1 hormone naïve prostate cancer, there are many more RCT's to inform our decisions. CAB and gonadotrophin-releasing hormone antagonists perhaps provide a slight efficacy advantage while IHT may be slightly inferior with minimal M1 disease. The PSA nadir at 7 months after starting ADT is a powerful prognostic tool for M1 patients. There is growing recognition that serum testosterone (T) control while on ADT is linked to the development of castrate-resistant prostate cancer. Especially for a M1 patient, maintaining a serum T below 20–30 ng dl−1 prolongs the response to ADT. Novel oral agents (abiraterone and enzalutamide) may soon find use in hormone naïve disease and may alter the treatment landscape. Despite over 75 years of experience with ADT, many questions remain, and the field continues to evolve. PMID:26112479

  19. Echo state network prediction method and its application in flue gas turbine condition prediction

    NASA Astrophysics Data System (ADS)

    Wang, Shaohong; Chen, Tao; Xu, Xiaoli

    2010-12-01

    On the background of the complex production process of fluid catalytic cracking energy recovery system in large-scale petrochemical refineries, this paper introduced an improved echo state network (ESN) model prediction method which is used to address the condition trend prediction problem of the key power equipment--flue gas turbine. Singular value decomposition method was used to obtain the ESN output weight. Through selecting the appropriate parameters and discarding small singular value, this method overcame the defective solution problem in the prediction by using the linear regression algorithm, improved the prediction performance of echo state network, and gave the network prediction process. In order to solve the problem of noise contained in production data, the translation-invariant wavelet transform analysis method is combined to denoise the noisy time series before prediction. Condition trend prediction results show the effectiveness of the proposed method.

  20. 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.

  1. 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. PMID:23508826

  2. 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

  3. 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

  4. 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. PMID:26822948

  5. Global nonlinear kernel prediction for large data set with a particle swarm-optimized interval support vector regression.

    PubMed

    Ding, Yongsheng; Cheng, Lijun; Pedrycz, Witold; Hao, Kuangrong

    2015-10-01

    A new global nonlinear predictor with a particle swarm-optimized interval support vector regression (PSO-ISVR) is proposed to address three issues (viz., kernel selection, model optimization, kernel method speed) encountered when applying SVR in the presence of large data sets. The novel prediction model can reduce the SVR computing overhead by dividing input space and adaptively selecting the optimized kernel functions to obtain optimal SVR parameter by PSO. To quantify the quality of the predictor, its generalization performance and execution speed are investigated based on statistical learning theory. In addition, experiments using synthetic data as well as the stock volume weighted average price are reported to demonstrate the effectiveness of the developed models. The experimental results show that the proposed PSO-ISVR predictor can improve the computational efficiency and the overall prediction accuracy compared with the results produced by the SVR and other regression methods. The proposed PSO-ISVR provides an important tool for nonlinear regression analysis of big data. PMID:25974954

  6. 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., Jr.; Rice, K.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. Copyright 2006 by the American Geophysical Union.

  7. 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.

  8. Interbirth intervals

    PubMed Central

    Haig, David

    2014-01-01

    Background and objectives: Interbirth intervals (IBIs) mediate a trade-off between child number and child survival. Life history theory predicts that the evolutionarily optimal IBI differs for different individuals whose fitness is affected by how closely a mother spaces her children. The objective of the article is to clarify these conflicts and explore their implications for public health. Methodology: Simple models of inclusive fitness and kin conflict address the evolution of human birth-spacing. Results: Genes of infants generally favor longer intervals than genes of mothers, and infant genes of paternal origin generally favor longer IBIs than genes of maternal origin. Conclusions and implications: The colonization of maternal bodies by offspring cells (fetal microchimerism) raises the possibility that cells of older offspring could extend IBIs by interfering with the implantation of subsequent embryos. PMID:24480612

  9. 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

  10. 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

  11. 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

  12. Predicting Weather Conditions and Climate for Mars Expeditions

    NASA Astrophysics Data System (ADS)

    Read, P. L.; Lewis, S. R.; Bingham, S. J.; Newman, C. E.

    Weather and climatic conditions are among the most important factors to be taken into account when planning expeditions to remote and challenging locations on Earth. This is likely to be equally the case for expedition planners on Mars, where conditions (in terms of extremes of temperature, etc.) can be at least as daunting as back on Earth. With the success of recent unmanned missions to Mars, such as NASA's Mars Pathfinder, Mars Global Surveyor and Mars Odyssey, there is now a great deal of information available on the range of environ- mental conditions on Mars, from the tropics to the CO2 ice-covered polar caps. This has been further supple- mented by the development of advanced numerical models of the Martian atmosphere, allowing detailed and accurate simulations and predictions of the weather and climate across the planet. This report discusses the main weather and climate variables which future Martian human expedition planners will need to take into account. The range of conditions likely to be encountered at a variety of typical locations on Mars is then considered, with reference to predictions from the ESA Mars Climate Database.

  13. RWEQ - Wind erosion predictions for variable soil roughness conditions

    NASA Astrophysics Data System (ADS)

    de Oro, Laura A.; Colazo, Juan C.; Buschiazzo, Daniel E.

    2016-03-01

    The soil surface roughness is a main factor in all wind erosion prediction models, including the Revised Wind Erosion Equation (RWEQ). The objective of this study was to test the erodibility of two typical soils of the semiarid Argentinean Pampas under three different tillage conditions (compared to a flat surface) at three wind velocities using a wind tunnel and to evaluate the performance of the RWEQ model. Results showed that all rough surfaces were less eroded by wind than a flat surface (FS) in both soils and all wind velocities. An exception was LB (lister-bedder) in the Haplustoll that showed similar erosion than FS. Wind erosion increased rapidly above 16.5 m s-1 wind velocity in all tillage conditions. The relative wind erosion (RE) calculated with the RWEQ (K‧ factor) fitted well with measured RE, except for K‧ < 0.1 (rougher surface) where the measured RE were much higher than the predicted. More than 70% of RE variability was explained by the oriented roughness (Kr) in both soils. The aforementioned indicates that Kr can be used instead of K‧ (a value that contains both, Kr and the random roughness - Crr factors) to predict wind erosion with RWEQ in the studied soils. Absolute wind erosion amounts predicted with RWEQ fitted well with measured data only for DT, mainly at low wind velocity. For the other tillage tools, the model did not apply well as it underestimated the erosion for the rougher soil surface condition (LB) and overestimated it for the less rough surface (DH).

  14. 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.

  15. 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.

  16. Neonatal Binge Alcohol Exposure Produces Dose Dependent Deficits in Interstimulus Interval Discrimination Eyeblink Conditioning in Juvenile Rats

    PubMed Central

    Brown, Kevin L.; Burman, Michael A.; Duong, Huan B.; Stanton, Mark E.

    2009-01-01

    Alcohol consumption in neonatal rats produces cerebellar damage and is widely used to model 3rd-trimester human fetal alcohol exposure. Neonatal “binge-like” exposure to high doses of alcohol (5 g/kg/day or more) impairs acquisition of eyeblink classical conditioning (EBC), a cerebellar-dependent Pavlovian motor learning task. We have recently found impairments in interstimulus interval (ISI) discrimination – a complex task variant of EBC - in adult rats following postnatal day (PD) 4–9 alcohol exposure at doses of 3, 4, and 5 g/kg/day. Because robust developmental differences in conditioned response (CR) generation and CR latency measures are present between untreated juveniles and adults in this task, we sought to extend alcohol findings to juvenile rats (PD30). Five neonatal treatment groups were used: (1) undisturbed controls, (2) sham intubation controls, (3) 3 g/kg/day of alcohol (blood alcohol concentration {BAC} = 139.9 mg/dl), (4) 4 g/kg/day of alcohol (BAC = 237.3 mg/dl), or (5) 5 g/kg/day of alcohol (BAC = 301.8 mg/dl). Intubations occurred over PD4-9. ISI discrimination training in juveniles (PD30-33) revealed dose-dependent CR deficits in all three alcohol-exposed groups relative to controls. Contrary to expected outcomes, CR latency measures were not significantly affected as a function of neonatal treatment. Comparison of these findings with our recent study in adults suggests that alcohol-induced impairments in ISI discrimination EBC may be greater in adults relative to juveniles. The present findings provide further evidence that ISI discrimination may provide greater sensitivity to functional deficits resulting from moderate levels of neonatal alcohol exposure relative to single-cue EBC paradigms. PMID:19007754

  17. Electrocardiogram PR Interval Is a Surrogate Marker to Predict New Occurrence of Atrial Fibrillation in Patients with Frequent Premature Atrial Contractions.

    PubMed

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

    2016-04-01

    The clinical significance of prolonged PR interval has not been evaluated in patients with frequent premature atrial contractions (PACs). We investigated whether prolonged PR interval could predict new occurrence of atrial fibrillation (AF) in patients with frequent PACs. We retrospectively analyzed 684 patients with frequent PACs (> 100 PACs/day) who performed repeated 24-hour Holter monitoring. Prolonged PR interval was defined as longer than 200 msec. Among 684 patients, 626 patients had normal PR intervals (group A) and 58 patients had prolonged PR intervals (group B). After a mean follow-up of 59.3 months, 14 patients (24.1%) in group B developed AF compared to 50 patients (8.0%) in group A (P < 0.001). Cox regression analysis showed that prolonged PR interval (hazard ratio [HR], 1.950; 95% CI, 1.029-3.698; P = 0.041), age (HR, 1.033; 95% CI, 1.006-1.060; P = 0.015), and left atrial (LA) dimension (HR, 1.061; 95% CI, 1.012-1.112; P = 0.015) were associated with AF occurrence. Prolonged PR interval, advanced age, and enlarged LA dimension are independent risk factors of AF occurrence in patients with frequent PACs. PMID:27051234

  18. Electrocardiogram PR Interval Is a Surrogate Marker to Predict New Occurrence of Atrial Fibrillation in Patients with Frequent Premature Atrial Contractions

    PubMed Central

    Hwang, Jin Kyung; Choi, So Ra; Park, Seung-Jung; Kim, June Soo

    2016-01-01

    The clinical significance of prolonged PR interval has not been evaluated in patients with frequent premature atrial contractions (PACs). We investigated whether prolonged PR interval could predict new occurrence of atrial fibrillation (AF) in patients with frequent PACs. We retrospectively analyzed 684 patients with frequent PACs (> 100 PACs/day) who performed repeated 24-hour Holter monitoring. Prolonged PR interval was defined as longer than 200 msec. Among 684 patients, 626 patients had normal PR intervals (group A) and 58 patients had prolonged PR intervals (group B). After a mean follow-up of 59.3 months, 14 patients (24.1%) in group B developed AF compared to 50 patients (8.0%) in group A (P < 0.001). Cox regression analysis showed that prolonged PR interval (hazard ratio [HR], 1.950; 95% CI, 1.029–3.698; P = 0.041), age (HR, 1.033; 95% CI, 1.006–1.060; P = 0.015), and left atrial (LA) dimension (HR, 1.061; 95% CI, 1.012–1.112; P = 0.015) were associated with AF occurrence. Prolonged PR interval, advanced age, and enlarged LA dimension are independent risk factors of AF occurrence in patients with frequent PACs. PMID:27051234

  19. 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

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

    PubMed

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

    2016-04-01

    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

  1. 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

  2. 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

    2010-01-01

    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

  3. Reprint of: "Demographic factors predict magnitude of conditioned fear".

    PubMed

    Rosenbaum, Blake L; Bui, Eric; Marin, Marie-France; Holt, Daphne J; Lasko, Natasha B; Pitman, Roger K; Orr, Scott P; Milad, Mohammed R

    2015-12-01

    There is substantial variability across individuals in the magnitudes of their skin conductance (SC) responses during the acquisition and extinction of conditioned fear. To manage this variability, subjects may be matched for demographic variables, such as age, gender and education. However, limited data exist addressing how much variability in conditioned SC responses is actually explained by these variables. The present study assessed the influence of age, gender and education on the SC responses of 222 subjects who underwent the same differential conditioning paradigm. The demographic variables were found to predict a small but significant amount of variability in conditioned responding during fear acquisition, but not fear extinction learning or extinction recall. A larger differential change in SC during acquisition was associated with more education. Older participants and women showed smaller differential SC during acquisition. Our findings support the need to consider age, gender and education when studying fear acquisition but not necessarily when examining fear extinction learning and recall. Variability in demographic factors across studies may partially explain the difficulty in reproducing some SC findings. PMID:26608179

  4. 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.

  5. 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.

  6. Classification of Initial conditions required for Substorm prediction.

    NASA Astrophysics Data System (ADS)

    Patra, S.; Spencer, E. A.

    2014-12-01

    We investigate different classes of substorms that occur as a result of various drivers such as the conditions in the solar wind and the internal state of the magnetosphere ionosphere system during the geomagnetic activity. In performing our study, we develop and use our low order physics based nonlinear model of the magnetosphere called WINDMI to establish the global energy exchange between the solar wind, magnetosphere and ionosphere by constraining the model results to satellite and ground measurements. On the other hand, we make quantitative and qualitative comparisons between our low order model with available MHD, multi-fluid and ring current simulations in terms of the energy transfer between the geomagnetic tail, plasma sheet, field aligned currents, ionospheric currents and ring current, during isolated substorms, storm time substorms, and sawtooth events. We use high resolution solar wind data from the ACE satellite, measurements from the CLUSTER and THEMIS missions satellites, and ground based magnetometer measurements from SUPERMAG and WDC Kyoto, to further develop our low order physics based model. Finally, we attempt to answer the following questions: 1) What conditions in the solar wind influence the type of substorm event. This includes the IMF strength and orientation, the particle densities, velocities and temperatures, and the timing of changes such as shocks, southward turnings or northward turnings of the IMF. 2) What is the state of the magnetosphere ionosphere system before an event begins. These are the steady state conditions prior to an event, if they exist, which produce the satellite and ground based measurements matched to the WINDMI model. 3) How does the prior state of the magnetosphere influence the transition into a particular mode of behavior under solar wind forcing. 4) Is it possible to classify the states of the magnetosphere into distinct categories depending on pre-conditioning, and solar wind forcing conditions? 5) Can we

  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 %. PMID:25824122

  9. 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.

  10. 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.

  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. 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

  13. 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.

  14. 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

  15. 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

  16. Ensemble Prediction of Flood Maps Under Uncertain Conditions

    NASA Astrophysics Data System (ADS)

    Pedrozo-Acuña, A.; Rodríguez-Rincón, J. P.; Brena-Naranjo, J. A. A.

    2014-12-01

    Hydro-meteorological hazards can have cascading effects and far-reaching implications on water security, with socio-economic and environmental consequences. Worldwide the magnitude of recent floods highlight the necessity to generate a better understanding on their causes and associated risk. An improved flood risk strategy should incorporate the communication of uncertain research results to decision-makers. Therefore, it is of paramount importance to generate a robust framework that enables its quantification. The purpose of this study is to investigate the propagation of meteorological uncertainty within a cascade modelling approach to flood mapping. The methodology is comprised of a Numerical Weather Prediction Model (NWP), a distributed rainfall-runoff model and a standard 2D hydrodynamic model. The cascade of models is used to reproduce an extreme flood event that took place in Southern Mexico, during September 2013. The event is selected as high quality field data (e.g. LiDAR; rain gauges) and satellite imagery are available. Uncertainty in the meteorological model (Weather Research and Forecasting model) is evaluated through the use of a multi-physics ensemble technique, which considers twelve parameterisation schemes to determine a given precipitation. The resulting precipitation fields are used as input in a distributed hydrological model, enabling the determination of different hydrographs associated to this event. Lastly, by means of a standard 2D hydrodynamic model, resulting hydrographs are used as forcing conditions to study the propagation of the meteorological uncertainty to an estimated flooded area. Results show the utility of the selected modelling approach to investigate error propagation within a cascade of models. Moreover, the error associated to the determination of the runoff, is showed to be lower than that obtained in the precipitation estimation suggesting that uncertainty do not necessarily increase within a model cascade.

  17. 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

  18. Eyeblink conditioning during an inter-stimulus interval switch in rabbits using picrotoxin to disrupt cerebellar cortical input to the interpositus nucleus

    PubMed Central

    Vogel, Richard W.; Amundson, Jeffrey C.; Lindquist, Derick H.; Steinmetz, Joseph E.

    2008-01-01

    The role of the cerebellar cortex in eyeblink classical conditioning remains unclear. Experimental manipulations that disrupt the normal function of this region impair learning to various degrees and task parameters may be important factors in determining the severity of impairment. The present investigation was undertaken to study the role of cerebellar cortex in eyeblink conditioning under CS-US intervals known to be optimal or non-optimal for learning. Using discrete infusions of picrotoxin to the interpositus nucleus of the rabbit cerebellum, we pharmacologically disrupted input from the cerebellar cortex while training with an inter-stimulus interval (ISI) switch procedure. One group of rabbits was first trained with a 250 ms ISI (optimal) and then switched to a 750 ms ISI (non-optimal). A second group was trained in the opposite order. As expected, control rabbits learned the 250 ms ISI much faster than the 750 ms ISI. The most striking effect was that picrotoxin-treated rabbits initially trained with a 250 ms ISI learned comparably to controls, but those initially trained with a 750 ms ISI were severely impaired. These results suggest that functional input from cerebellar cortex becomes increasingly important for the interpositus nucleus to learn delay eyeblink conditioning as the ISI departs from an optimal interval of 250 ms. PMID:19170431

  19. Interval Training.

    ERIC Educational Resources Information Center

    President's Council on Physical Fitness and Sports, Washington, DC.

    Regardless of the type of physical activity used, interval training is simply repeated periods of physical stress interspersed with recovery periods during which activity of a reduced intensity is performed. During the recovery periods, the individual usually keeps moving and does not completely recover before the next exercise interval (e.g.,…

  20. Prediction of the distillation temperatures of crude oils using ¹H NMR and support vector regression with estimated confidence intervals.

    PubMed

    Filgueiras, Paulo R; Terra, Luciana A; Castro, Eustáquio V R; Oliveira, Lize M S L; Dias, Júlio C M; Poppi, Ronei J

    2015-09-01

    This paper aims to estimate the temperature equivalent to 10% (T10%), 50% (T50%) and 90% (T90%) of distilled volume in crude oils using (1)H NMR and support vector regression (SVR). Confidence intervals for the predicted values were calculated using a boosting-type ensemble method in a procedure called ensemble support vector regression (eSVR). The estimated confidence intervals obtained by eSVR were compared with previously accepted calculations from partial least squares (PLS) models and a boosting-type ensemble applied in the PLS method (ePLS). By using the proposed boosting strategy, it was possible to identify outliers in the T10% property dataset. The eSVR procedure improved the accuracy of the distillation temperature predictions in relation to standard PLS, ePLS and SVR. For T10%, a root mean square error of prediction (RMSEP) of 11.6°C was obtained in comparison with 15.6°C for PLS, 15.1°C for ePLS and 28.4°C for SVR. The RMSEPs for T50% were 24.2°C, 23.4°C, 22.8°C and 14.4°C for PLS, ePLS, SVR and eSVR, respectively. For T90%, the values of RMSEP were 39.0°C, 39.9°C and 39.9°C for PLS, ePLS, SVR and eSVR, respectively. The confidence intervals calculated by the proposed boosting methodology presented acceptable values for the three properties analyzed; however, they were lower than those calculated by the standard methodology for PLS. PMID:26003712

  1. Body condition predicts energy stores in apex predatory sharks.

    PubMed

    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

  2. Deciphering and prediction of plant dynamics under field conditions.

    PubMed

    Izawa, Takeshi

    2015-04-01

    Elucidation of plant dynamics under fluctuating natural environments is a challenging goal in plant physiology. Recently, using a computer statistics integrating a series of transcriptome data of field-grown rice leaves during an entire crop season and several corresponding environmental data such as solar radiation and ambient temperature, most parts of transcriptome have been modeled. This reveals the detailed contributions of developmental timing, circadian clocks and each environmental factor to transcriptome dynamics in the field and can predict transcriptome dynamics under given environments. Furthermore, some traits such as flowering time in natural environments have been shown to be predicted by mathematical models based on gene-networks parameterized with data obtained in the laboratory, and phenology models refined by knowledge of molecular genetics. New molecular physiology is beginning in plant science. PMID:25706440

  3. Predictive value of ventilatory inflection points determined under field conditions.

    PubMed

    Heyde, Christian; Mahler, Hubert; Roecker, Kai; Gollhofer, Albert

    2016-05-01

    The aim of this study was to evaluate the predictive potential provided by two ventilatory inflection points (VIP1 and VIP2) examined in field without using gas analysis systems and uncomfortable facemasks. A calibrated respiratory inductance plethysmograph (RIP) and a computerised routine were utilised, respectively, to derive ventilation and to detect VIP1 and VIP2 during a standardised field ramp test on a 400 m running track on 81 participants. In addition, average running speed of a competitive 1000 m run (S1k) was observed as criterion. The predictive value of running speed at VIP1 (SVIP1) and the speed range between VIP1 and VIP2 in relation to VIP2 (VIPSPAN) was analysed via regression analysis. VIPSPAN rather than running speed at VIP2 (SVIP2) was operationalised as a predictor to consider the covariance between SVIP1 and SVIP2. SVIP1 and VIPSPAN, respectively, provided 58.9% and 22.9% of explained variance in regard to S1k. Considering covariance, the timing of two ventilatory inflection points provides predictive value in regard to a competitive 1000 m run. This is the first study to apply computerised detection of ventilatory inflection points in a field setting independent on measurements of the respiratory gas exchange and without using any facemasks. PMID:26190229

  4. 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.

  5. 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...

  6. 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. PMID:26230618

  7. 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.

  8. 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.

  9. 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

  10. The cystatin C/creatinine ratio, a marker of glomerular filtration quality: associated factors, reference intervals, and prediction of morbidity and mortality in healthy seniors.

    PubMed

    Purde, Mette-Triin; Nock, Stefan; Risch, Lorenz; Medina Escobar, Pedro; Grebhardt, Chris; Nydegger, Urs E; Stanga, Zeno; Risch, Martin

    2016-03-01

    The ratio of cystatin C (cysC) to creatinine (crea) is regarded as a marker of glomerular filtration quality associated with cardiovascular morbidities. We sought to determine reference intervals for serum cysC-crea ratio in seniors. Furthermore, we sought to determine whether other low-molecular weight molecules exhibit a similar behavior in individuals with altered glomerular filtration quality. Finally, we investigated associations with adverse outcomes. A total of 1382 subjectively healthy Swiss volunteers aged 60 years or older were enrolled in the study. Reference intervals were calculated according to Clinical & Laboratory Standards Institute (CLSI) guideline EP28-A3c. After a baseline exam, a 4-year follow-up survey recorded information about overall morbidity and mortality. The cysC-crea ratio (mean 0.0124 ± 0.0026 mg/μmol) was significantly higher in women and increased progressively with age. Other associated factors were hemoglobin A1c, mean arterial pressure, and C-reactive protein (P < 0.05 for all). Participants exhibiting shrunken pore syndrome had significantly higher ratios of 3.5-66.5 kDa molecules (brain natriuretic peptide, parathyroid hormone, β2-microglobulin, cystatin C, retinol-binding protein, thyroid-stimulating hormone, α1-acid glycoprotein, lipase, amylase, prealbumin, and albumin) and creatinine. There was no such difference in the ratios of very low-molecular weight molecules (urea, uric acid) to creatinine or in the ratios of molecules larger than 66.5 kDa (transferrin, haptoglobin) to creatinine. The cysC-crea ratio was significantly predictive of mortality and subjective overall morbidity at follow-up in logistic regression models adjusting for several factors. The cysC-crea ratio exhibits age- and sex-specific reference intervals in seniors. In conclusion, the cysC-crea ratio may indicate the relative retention of biologically active low-molecular weight compounds and can independently predict the risk for overall

  11. Interval-valued random functions and the kriging of intervals

    SciTech Connect

    Diamond, P.

    1988-04-01

    Estimation procedures using data that include some values known to lie within certain intervals are usually regarded as problems of constrained optimization. A different approach is used here. Intervals are treated as elements of a positive cone, obeying the arithmetic of interval analysis, and positive interval-valued random functions are discussed. A kriging formalism for interval-valued data is developed. It provides estimates that are themselves intervals. In this context, the condition that kriging weights be positive is seen to arise in a natural way. A numerical example is given, and the extension to universal kriging is sketched.

  12. Suspended-sediment transport rates at the 1.5-year recurrence interval for ecoregions of the United States: transport conditions at the bankfull and effective discharge?

    NASA Astrophysics Data System (ADS)

    Simon, Andrew; Dickerson, Wendy; Heins, Amanda

    2004-03-01

    Historical flow and suspended-sediment transport data from more than 2900 sites across the United States have been analyzed in the context of estimating flow and suspended-sediment transport conditions at the 1.5-year recurrence interval flow ( Q1.5). This is particularly relevant with the renewed focus on stream restoration activities and the urgency in developing water-quality criteria for sediment. Data were sorted into the 84 Level III ecoregions to identify spatial trends in suspended-sediment concentrations and yields to meaningfully describe suspended-sediment transport rates across the United States. Arguments are developed that in lieu of form-based estimates of say the bankfull level, a flow of a given recurrence interval ( Q1.5) is more appropriate to integrate suspended-sediment transport ratings for the purpose of defining long-term transport conditions at a site (the "effective discharge"). The use of the Q1.5 as a measure of the effective discharge for suspended-sediment transport is justified on the basis of literature reports and analytic results from hundreds of sites in 17 ecoregions that span a diverse range of hydrologic and topographic conditions (i.e., Coast Range, Arizona/New Mexico Plateau, Mississippi Valley Loess Plains, Middle Atlantic Coastal Plain). There is sufficient data to also develop regional curves for the Q1.5 in all but eight of the ecoregions. At the Q1.5 the highest median suspended-sediment concentrations occur in semiarid environments (Southwest Tablelands, Arizona/New Mexico Plateau and the Mojave Basin and Range); the highest yields occur in humid regions with erodible soils and steep slopes or channel gradients (Mississippi Valley Loess Plains [MVLP] and the Coast Range). Suspended-sediment yields for stable streams are used to determine "background" or "reference" sediment transport conditions in eight ecoregions where there is sufficient field data. The median value for stable sites within a given ecoregion are

  13. Interval ridge regression (iRR) as a fast and robust method for quantitative prediction and variable selection applied to edible oil adulteration.

    PubMed

    Jović, Ozren; Smrečki, Neven; Popović, Zora

    2016-04-01

    A novel quantitative prediction and variable selection method called interval ridge regression (iRR) is studied in this work. The method is performed on six data sets of FTIR, two data sets of UV-vis and one data set of DSC. The obtained results show that models built with ridge regression on optimal variables selected with iRR significantly outperfom models built with ridge regression on all variables in both calibration (6 out of 9 cases) and validation (2 out of 9 cases). In this study, iRR is also compared with interval partial least squares regression (iPLS). iRR outperfomed iPLS in validation (insignificantly in 6 out of 9 cases and significantly in one out of 9 cases for p<0.05). Also, iRR can be a fast alternative to iPLS, especially in case of unknown degree of complexity of analyzed system, i.e. if upper limit of number of latent variables is not easily estimated for iPLS. Adulteration of hempseed (H) oil, a well known health beneficial nutrient, is studied in this work by mixing it with cheap and widely used oils such as soybean (So) oil, rapeseed (R) oil and sunflower (Su) oil. Binary mixture sets of hempseed oil with these three oils (HSo, HR and HSu) and a ternary mixture set of H oil, R oil and Su oil (HRSu) were considered. The obtained accuracy indicates that using iRR on FTIR and UV-vis data, each particular oil can be very successfully quantified (in all 8 cases RMSEP<1.2%). This means that FTIR-ATR coupled with iRR can very rapidly and effectively determine the level of adulteration in the adulterated hempseed oil (R(2)>0.99). PMID:26838379

  14. 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

  15. 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

  16. 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

  17. Practical use of a uterine score system for predicting effects on interval from calving to first insemination and non-return rate 56 in Danish dairy herds.

    PubMed

    Elkjær, Karina; Labouriau, Rodrigo; Ancker, Marie-Louise; Gustafsson, Hans; Callesen, Henrik

    2013-12-01

    A detailed study of 398,237 lactations of Danish Holstein dairy cows was undertaken. The objective was to investigate the information gained by evaluating vaginal discharge in cows from 5 to 19 days post-partum (p.p.) using an ordinal scale from 0 to 9. The study focused on the interval from calving to first insemination (CFI) and the non-return rate 56 days after first insemination (NR56), adjusted for the confounders milk production and body condition score (BCS). For the analyses, BCS was evaluated on the same day that the uterine score was made. Milk production was defined as test-day milk yield in the first month p.p. The study showed that the evaluation of vaginal discharge according to this score system permitted ranking of cows according to CFI and NR56, i.e. an increasing uterine score was associated with a significantly longer time from calving to first insemination and significantly reduced the probability of success of the first insemination. Reproductive success was already affected if the uterine score had reached 4 (i.e. before the discharge smelled abnormally). The negative effect on CFI and NR56 increased as the uterine score increased, which suggested that the uterine scoring system was a useful guide to dairy producers. PMID:24144773

  18. Decreases in Electrocardiographic R-Wave Amplitude and QT Interval Predict Myocardial Ischemic Infarction in Rhesus Monkeys with Left Anterior Descending Artery Ligation

    PubMed Central

    Han, Pengfei; Xie, Yuping; Chen, Jianmin; Xiao, Ying; Kang, Y. James

    2013-01-01

    Clinical studies have demonstrated the predictive values of changes in electrocardiographic (ECG) parameters for the preexisting myocardial ischemic infarction. However, a simple and early predictor for the subsequent development of myocardial infarction during the ischemic phase is of significant value for the identification of ischemic patients at high risk. The present study was undertaken by using non-human primate model of myocardial ischemic infarction to fulfill this gap. Twenty male Rhesus monkeys at age of 2–3 years old were subjected to left anterior descending artery ligation. This ligation was performed at varying position along the artery so that it produced varying sizes of myocardial infarction at the late stage. The ECG recording was undertaken before the surgical procedure, at 2 h after the ligation, and 8 weeks after the surgery for each animal. The correlation of the changes in the ECG waves in the early or the late stage with the myocardial infarction size was analyzed. The R wave depression and the QT shortening in the early ischemic stage were found to have an inverse correlation with the myocardial infarction size. At the late stage, the R wave depression, the QT prolongation, the QRS score, and the ST segment elevation were all closely correlated with the developed infarction size. The poor R wave progression was identified at both the early ischemic and the late infarction stages. Therefore, the present study using non-human primate model of myocardial ischemic infarction identified the decreases in the R wave and the QT interval as early predictors of myocardial infarction. Validation of these parameters in clinical studies would greatly help identifying patients with myocardial ischemia at high risk for the subsequent development of myocardial infarction. PMID:23967258

  19. 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.

  20. 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.

  1. Prediction of heat-illness symptoms with the prediction of human vascular response in hot environment under resting condition.

    PubMed

    Aggarwal, Yogender; Karan, Bhuwan Mohan; Das, Barsa Nand; Sinha, Rakesh Kumar

    2008-04-01

    The thermoregulatory control of human skin blood flow is vital to maintain the body heat storage during challenges of thermal homeostasis under heat stress. Whenever thermal homeostasis disturbed, the heat load exceeds heat dissipation capacity, which alters the cutaneous vascular responses along with other body physiological variables. Whole body skin blood flow has been calculated from the forearm blood flow. Present model has been designed using electronics circuit simulator (Multisim 8.0, National Instruments, USA), is to execute a series of predictive equations for early prediction of physiological parameters of young nude subjects during resting condition at various level of dry heat stress under almost still air to avoid causalities associated with hot environmental. The users can execute the model by changing the environmental temperature in degrees C and exposure time in minutes. The model would be able to predict and detect the changes in human vascular responses along with other physiological parameters and from this predicted values heat related-illness symptoms can be inferred. PMID:18461820

  2. Linear Superposition and Prediction of Bacterial Promoter Activity Dynamics in Complex Conditions

    PubMed Central

    Rothschild, Daphna; Dekel, Erez; Hausser, Jean; Bren, Anat; Aidelberg, Guy; Szekely, Pablo; Alon, Uri

    2014-01-01

    Bacteria often face complex environments. We asked how gene expression in complex conditions relates to expression in simpler conditions. To address this, we obtained accurate promoter activity dynamical measurements on 94 genes in E. coli in environments made up of all possible combinations of four nutrients and stresses. We find that the dynamics across conditions is well described by two principal component curves specific to each promoter. As a result, the promoter activity dynamics in a combination of conditions is a weighted average of the dynamics in each condition alone. The weights tend to sum up to approximately one. This weighted-average property, called linear superposition, allows predicting the promoter activity dynamics in a combination of conditions based on measurements of pairs of conditions. If these findings apply more generally, they can vastly reduce the number of experiments needed to understand how E. coli responds to the combinatorially huge space of possible environments. PMID:24809350

  3. 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.

  4. On boundary conditions for the diffusion equation in room-acoustic prediction: Theory, simulations, and experiments.

    PubMed

    Jing, Yun; Xiang, Ning

    2008-01-01

    This paper proposes a modified boundary condition to improve the room-acoustic prediction accuracy of a diffusion equation model. Previous boundary conditions for the diffusion equation model have certain limitations which restrict its application to a certain number of room types. The boundary condition employing the Sabine absorption coefficient [V. Valeau et al., J. Acoust. Soc. Am. 119, 1504-1513 (2006)] cannot predict the sound field well when the absorption coefficient is high, while the boundary condition employing the Eyring absorption coefficient [Y. Jing and N. Xiang, J. Acoust. Soc. Am. 121, 3284-3287 (2007); A. Billon et al., Appl. Acoust. 69, (2008)] has a singularity whenever any surface material has an absorption coefficient of 1.0. The modified boundary condition is derived based on an analogy between sound propagation and light propagation. Simulated and experimental data are compared to verify the modified boundary condition in terms of room-acoustic parameter prediction. The results of this comparison suggest that the modified boundary condition is valid for a range of absorption coefficient values and successfully eliminates the singularity problem. PMID:18177146

  5. 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

  6. 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.

  7. Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task

    PubMed Central

    Villa, Alessandro E. P.; Tetko, Igor V.; Hyland, Brian; Najem, Abdellatif

    1999-01-01

    Precise and repeated spike-train timings within and across neurons define spatiotemporal patterns of activity. Although the existence of these patterns in the brain is well established in several species, there has been no direct evidence of their influence on behavioral output. To address this question, up to 15 neurons were recorded simultaneously in the auditory cortex of freely moving rats while animals waited for acoustic cues in a Go/NoGo task. A total of 235 significant patterns were detected during this interval from an analysis of 13 hr of recording involving over 1 million spikes. Of particular interest were 129 (55%) patterns that were significantly associated with the type of response the animal made later, independent of whether the response was that prompted by the cue because the response occurred later and the cue was chosen randomly. Of these behavior-predicting patterns, half (59/129) were associated with an enhanced tendency to go in response to the stimulus, and for 11 patterns of this subset, trials including the pattern were followed by significantly faster reaction time than those lacking the pattern. The remaining behavior-predicting patterns were associated with an enhanced NoGo tendency. Overall mean discharge rates did not vary across trials. Hence, these data demonstrate that particular spatiotemporal patterns predict future behavioral responses. Such presignal activity could form templates for extracting specific sensory information, motor programs prespecifying preference for a particular act, and/or some intermediate, associative brain process. PMID:9927701

  8. UREA SPACE AND BODY CONDITION SCORE TO PREDICT BODY COMPOSITION OF MEAT GOATS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Yearling Boer x Spanish wethers (n=40) were used to develop and compare body composition prediction equations for mature meat goats based on urea space (US) and body condition score (BCS). Before the experiment, one-half of the animals were managed to have high BW and BCS (1-5, with 1 being extreme...

  9. 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…

  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

    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.

  11. Evidence that conditioned avoidance responses are reinforced by positive prediction errors signaled by tonic striatal dopamine.

    PubMed

    Dombrowski, Patricia A; Maia, Tiago V; Boschen, Suelen L; Bortolanza, Mariza; Wendler, Etieli; Schwarting, Rainer K W; Brandão, Marcus Lira; Winn, Philip; Blaha, Charles D; Da Cunha, Claudio

    2013-03-15

    We conducted an experiment in which hedonia, salience and prediction error hypotheses predicted different patterns of dopamine (DA) release in the striatum during learning of conditioned avoidance responses (CARs). The data strongly favor the latter hypothesis. It predicts that during learning of the 2-way active avoidance CAR task, positive prediction errors generated when rats do not receive an anticipated footshock (which is better than expected) cause DA release that reinforces the instrumental avoidance action. In vivo microdialysis in the rat striatum showed that extracellular DA concentration increased during early CAR learning and decreased throughout training returning to baseline once the response was well learned. In addition, avoidance learning was proportional to the degree of DA release. Critically, exposure of rats to the same stimuli but in an unpredictable, unavoidable, and inescapable manner, did not produce alterations from baseline DA levels as predicted by the prediction error but not hedonic or salience hypotheses. In addition, rats with a partial lesion of substantia nigra DA neurons, which did not show increased DA levels during learning, failed to learn this task. These data represent clear and unambiguous evidence that it was the factor positive prediction error, and not hedonia or salience, which caused increase in the tonic level of striatal DA and which reinforced learning of the instrumental avoidance response. PMID:22771418

  12. Corrosion fatigue behavior and life prediction method under changing temperature condition

    SciTech Connect

    Kanasaki, Hiroshi; Hirano, Akihiko; Iida, Kunihiro; Asada, Yasuhide

    1997-12-01

    Axially strain controlled low cycle fatigue tests of a carbon steel in oxygenated high temperature water were carried out under changing temperature conditions. Two patterns of triangular wave were selected for temperature cycling. One was in-phase pattern synchronizing with strain cycling and the other was an out-of-phase pattern in which temperature was changed in anti-phase to the strain cycling. The fatigue life under changing temperature condition was in the range of the fatigue life under various constant temperature within the range of the changing temperature. The fatigue life of in-phase pattern was equivalent to that of out-of-phase pattern. The corrosion fatigue life prediction method was proposed for changing temperature condition, and was based on the assumption that the fatigue damage increased in linear proportion to increment of strain during cycling. The fatigue life predicted by this method was in good agreement with the test results.

  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. 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

  15. Predictions for the Higgs Mass from the Stability and Triviality Conditions

    SciTech Connect

    Solis R, H. Gabriel; Juarez W, S. Rebeca; Kielanowski, P.

    2006-09-25

    In the context of the Standard Model (SM), we use the one-loop and two-loop Renormalization Group Equations (RGE) in order to analyze the evolution of the Higgs quartic coupling {lambda}H in the interval [mt, EGU], where mt is the mass of the top quark and EGU = 1014GeV. The analytical solution for the one-loop differential equation (Riccati type) is obtained and analyzed and in the two-loop case we obtain a numerical solution which takes into account all the parameters (couplings) at the same order of approximation. In both cases, we restrict the possible initial values for {lambda}H by means of imposing the triviality and stability conditions which determine the range of energies where the SM is valid. We obtain the following bounds: 0.387 < {lambda}H < 0.623 for the one-loop case and 0.360 < {lambda}H < 0.628 for the two-loop case. These results determine the interval of the possible Higgs mass values: 151.9 < MH < 192.3 GeV, 143.8 < MH < 190.3 GeV for the one-loop and two-loop cases, respectively.

  16. 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.

  17. 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.

  18. 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. PMID:26581864

  19. 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.

  20. Prediction of physical properties of water under extremely supercritical conditions: a molecular dynamics study.

    PubMed

    Sakuma, Hiroshi; Ichiki, Masahiro; Kawamura, Katsuyuki; Fuji-ta, Kiyoshi

    2013-04-01

    The physical properties of water under a wide range of pressure and temperature conditions are important in fundamental physics, chemistry, and geoscience. Molecular simulations are useful for predicting and understanding the physical properties of water at phases extremely different from ambient conditions. In this study, we developed a new five-site flexible induced point charge model to predict the density, static dielectric constant, and transport properties of water in the extremely supercritical phase at high temperatures and pressures of up to 2000 K and 2000 MPa. The model satisfactorily reproduced the density, radial distribution function, static dielectric constant, reorientation time, and self-diffusion coefficients of water above the critical points. We also developed a database of the static dielectric constant, which is useful for discussing the electrical conductivity of aqueous fluids in the earth's crust and mantle. PMID:23574243

  1. Prediction of "fear" acquisition in healthy control participants in a de novo fear-conditioning paradigm.

    PubMed

    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 characteristics. To further examine these issues, the authors 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, the authors 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, the authors found that anxiety sensitivity was more linked to individual differences in orienting response than differences in conditioning per se. PMID:17179530

  2. 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.

  3. 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.

  4. A symbiont's dispersal strategy: condition-dependent dispersal underlies predictable variation in direct transmission among hosts.

    PubMed

    Skelton, James; Creed, Robert P; Brown, Bryan L

    2015-11-22

    Direct horizontal transmission of pathogenic and mutualistic symbionts has profound consequences for host and symbiont fitness alike. While the importance of contact rates for transmission is widely recognized, the processes that underlie variation in transmission during contact are rarely considered. Here, we took a symbiont's perspective of transmission as a form of dispersal and adopted the concept of condition-dependent dispersal strategies from the study of free-living organisms to understand and predict variation in transmission in the cleaning symbiosis between crayfish and ectosymbiotic branchiobdellidan worms. Field study showed that symbiont reproductive success was correlated with host size and competition among worms for microhabitats. Laboratory experiments demonstrated high variability in transmission among host contacts. Moreover, symbionts were more likely to disperse when host size and competition for microhabitat created a fitness environment below a discrete minimum threshold. A predictive model based on a condition-dependent symbiont dispersal strategy correctly predicted transmission in 95% of experimental host encounters and the exact magnitude of transmission in 67%, both significantly better than predictions that assumed a fixed transmission rate. Our work provides a dispersal-based understanding of symbiont transmission and suggests adaptive symbiont dispersal strategies can explain variation in transmission dynamics and complex patterns of host infection. PMID:26559953

  5. 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.

  6. 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. PMID:25813489

  7. 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.

  8. Neonatal body condition, immune responsiveness, and hematocrit predict longevity in a wild bird population

    PubMed Central

    Hodges, Christine J.; Forsman, Anna M.; Vogel, Laura A.; Masters, Brian S.; Johnson, Bonnie G. P.; Johnson, L. Scott; Thompson, Charles F.; Sakaluk, Scott K.

    2014-01-01

    Measures of body condition, immune function, and hematological health are widely used in ecological studies of vertebrate populations, predicated on the assumption that these traits are linked to fitness. However, compelling evidence that these traits actually predict long-term survival and reproductive success among individuals in the wild is lacking. Here, we show that body condition (i.e., size-adjusted body mass) and cutaneous immune responsiveness to phytohaemagglutinin (PHA) injection among neonates positively predict recruitment and subsequent longevity in a wild, migratory population of house wrens (Troglodytes aedon). However, neonates with intermediate hematocrit had the highest recruitment and longevity. Neonates with the highest PHA responsiveness and intermediate hematocrit prior to independence eventually produced the most offspring during their lifetime breeding on the study site. Importantly, the effects of PHA responsiveness and hematocrit were revealed while controlling for variation in body condition, sex, and environmental variation. Thus, our data demonstrate that body condition, cutaneous immune responsiveness, and hematocrit as a neonate are associated with individual fitness. Although hematocrit's effect is more complex than traditionally thought, our results suggest a previously underappreciated role for this trait in influencing survival in the wild. PMID:25505800

  9. Short communication: Prediction of intake in dairy cows under tropical conditions.

    PubMed

    Souza, M C; Oliveira, A S; Araújo, C V; Brito, A F; Teixeira, R M A; Moares, E H B K; Moura, D C

    2014-01-01

    A meta-analysis was conducted to develop a model for predicting dry matter intake (DMI) in dairy cows under the tropical conditions of Brazil and to assess its adequacy compared with 5 currently available DMI prediction models: Agricultural and Food Research Council (AFRC); National Research Council (NRC); Cornell Net Carbohydrate and Protein System (CNCPS; version 6); and 2 other Brazilian models. The data set was created using 457 observations (n=1,655 cows) from 100 studies, and it was randomly divided into 2 subsets for statistical analysis. The first subset was used to develop a DMI prediction equation (60 studies; 309 treatment means) and the second subset was used to assess the adequacy of DMI predictive models (40 studies; 148 treatment means). The DMI prediction model proposed in the current study was developed using a nonlinear mixed model analysis after reparameterizing the NRC equation but including study as a random effect in the model. Body weight (mean = 540 ± 57.6 kg), 4% fat-corrected milk (mean = 21.3 ± 7.7 kg/d), and days in milk (mean = 110 ± 62 d) were used as independent variables in the model. The adequacy of the DMI prediction models was evaluated based on coefficient of determination, mean square prediction error (MSPE), root MSPE (RMSPE), and concordance correlation coefficient (CCC). The observed DMI obtained from the data set used to evaluate the prediction models averaged 17.6 ± 3.2 kg/d. The following model was proposed: DMI (kg/d) = [0.4762 (± 0.0358) × 4% fat-corrected milk + 0.07219 (± 0.00605) × body weight(0.75)] × (1 - e(-0.03202 (± 0.00615) × [days in milk + 24.9576 (± 5.909)])). This model explained 93.0% of the variation in DMI, predicting it with the lowest mean bias (0.11 kg/d) and RMSPE (4.9% of the observed DMI) and the highest precision [correlation coefficient estimate (ρ) = 0.97] and accuracy [bias correction factor (Cb)=0.99]. The NRC model prediction equation explained 92.0% of the variation in DMI and

  10. The salt marsh vegetation spread dynamics simulation and prediction based on conditions optimized CA

    NASA Astrophysics Data System (ADS)

    Guan, Yujuan; Zhang, Liquan

    2006-10-01

    The biodiversity conservation and management of the salt marsh vegetation relies on processing their spatial information. Nowadays, more attentions are focused on their classification surveying and describing qualitatively dynamics based on RS images interpreted, rather than on simulating and predicting their dynamics quantitatively, which is of greater importance for managing and planning the salt marsh vegetation. In this paper, our notion is to make a dynamic model on large-scale and to provide a virtual laboratory in which researchers can run it according requirements. Firstly, the characteristic of the cellular automata was analyzed and a conclusion indicated that it was necessary for a CA model to be extended geographically under varying conditions of space-time circumstance in order to make results matched the facts accurately. Based on the conventional cellular automata model, the author introduced several new conditions to optimize it for simulating the vegetation objectively, such as elevation, growth speed, invading ability, variation and inheriting and so on. Hence the CA cells and remote sensing image pixels, cell neighbors and pixel neighbors, cell rules and nature of the plants were unified respectively. Taking JiuDuanSha as the test site, where holds mainly Phragmites australis (P.australis) community, Scirpus mariqueter (S.mariqueter) community and Spartina alterniflora (S.alterniflora) community. The paper explored the process of making simulation and predictions about these salt marsh vegetable changing with the conditions optimized CA (COCA) model, and examined the links among data, statistical models, and ecological predictions. This study exploited the potential of applying Conditioned Optimized CA model technique to solve this problem.

  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. Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation

    NASA Astrophysics Data System (ADS)

    Renard, Benjamin; Kavetski, Dmitri; Leblois, Etienne; Thyer, Mark; Kuczera, George; Franks, Stewart W.

    2011-11-01

    This study explores the decomposition of predictive uncertainty in hydrological modeling into its contributing sources. This is pursued by developing data-based probability models describing uncertainties in rainfall and runoff data and incorporating them into the Bayesian total error analysis methodology (BATEA). A case study based on the Yzeron catchment (France) and the conceptual rainfall-runoff model GR4J is presented. It exploits a calibration period where dense rain gauge data are available to characterize the uncertainty in the catchment average rainfall using geostatistical conditional simulation. The inclusion of information about rainfall and runoff data uncertainties overcomes ill-posedness problems and enables simultaneous estimation of forcing and structural errors as part of the Bayesian inference. This yields more reliable predictions than approaches that ignore or lump different sources of uncertainty in a simplistic way (e.g., standard least squares). It is shown that independently derived data quality estimates are needed to decompose the total uncertainty in the runoff predictions into the individual contributions of rainfall, runoff, and structural errors. In this case study, the total predictive uncertainty appears dominated by structural errors. Although further research is needed to interpret and verify this decomposition, it can provide strategic guidance for investments in environmental data collection and/or modeling improvement. More generally, this study demonstrates the power of the Bayesian paradigm to improve the reliability of environmental modeling using independent estimates of sampling and instrumental data uncertainties.

  13. 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.

  14. Theoretical prediction of lung nodule measurement accuracy under different acquisition and reconstruction conditions

    NASA Astrophysics Data System (ADS)

    Hsieh, Jiang; Karau, Kelly

    2004-04-01

    Utilization of computed tomography (CT) for lung cancer screening has attracted significant research interests in recent years. Images reconstructed from CT studies are used for lung nodule characterization and three-dimensional lung lesion sizing. Methodologies have been developed to automatically identify and characterize lung nodules. In this paper, we analyze the impact of acquisition and reconstruction parameters on the accuracy of quantitative lung nodule characterization. The two major data acquisition parameters that impact the accuracy of the lung nodule measurement are acquisition mode and slice aperture. Acquisition mode includes both axial and helical scans. The investigated reconstruction parameters are the reconstruction filters and field-of-view. We first develop theoretical models that predict the system response under various acquisition and reconstruction conditions. These models allow clinicians to compare results under different conditions and make appropriate acquisition and reconstruction decisions. To validate our model, extensive phantom experiments are conducted. Experiments have demonstrated that our analytical models accurately predict the performance parameters under various conditions. Our study indicates that acquisition and reconstruction parameters can significantly impact the accuracy of the nodule volume measurement. Consequently, when conducting quantitative analysis on lung nodules, especially in sequential growth studies, it is important to make appropriate adjustment and correction to maintain the desired accuracy and to ensure effective patient management.

  15. 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.

  16. 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

  17. The neural correlates of negative prediction error signaling in human fear conditioning.

    PubMed

    Spoormaker, V I; Andrade, K C; Schröter, M S; Sturm, A; Goya-Maldonado, R; Sämann, P G; Czisch, M

    2011-02-01

    In a temporal difference (TD) learning approach to classical conditioning, a prediction error (PE) signal shifts from outcome deliverance to the onset of the conditioned stimulus. Omission of an expected outcome results in a negative PE signal, which is the initial step towards successful extinction. In order to visualize negative PE signaling during fear conditioning, we employed combined functional magnetic resonance (fMRI) and skin conductance response (SCR) measurements in a conditioning task with visual stimuli and mild electrical shocks. Positive PE signaling was associated with increased activation in the bilateral insula, supplementary motor area, brainstem, and visual cortices. Negative PE signaling was associated with increased activation in the ventromedial and dorsolateral prefrontal cortices, the left lateral orbital gyrus, the middle temporal gyri, angular gyri, and visual cortices. The involvement of the ventromedial prefrontal and orbitofrontal cortex in extinction learning has been well documented, and this study provides evidence for the notion that these regions are already involved in negative PE signaling during fear conditioning. PMID:20869454

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

    PubMed

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

    2016-03-01

    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 800ms 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 240ms post-CS onset and over occipital and frontal sites between 308 and 344ms 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

  19. Do Early-Life Conditions Predict Functional Health Status in Adulthood? The Case of Mexico

    PubMed Central

    Huang, Cheng; Soldo, Beth J; Elo, Irma T

    2010-01-01

    Relatively few researchers have investigated early antecedents of adult functional limitations in developing countries. In this study, we assessed associations between childhood conditions and adult lower-body functional limitations (LBFL) as well as the potential mediating role of adult socioeconomic status, smoking, body mass index, and chronic diseases or symptoms. Based on data from the Mexican Health and Aging Study (MHAS) of individuals born prior to 1951 and contacted in 2001 and 2003, we found that childhood nutritional deprivation, serious health problems, and family background predict adult LBFL in Mexico. Adjustment for the potential mediators in adulthood attenuates these associations only to a modest degree. PMID:21074924

  20. 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.

  1. 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

  2. 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

  3. Experimenting with musical intervals

    NASA Astrophysics Data System (ADS)

    Lo Presto, Michael C.

    2003-07-01

    When two tuning forks of different frequency are sounded simultaneously the result is a complex wave with a repetition frequency that is the fundamental of the harmonic series to which both frequencies belong. The ear perceives this 'musical interval' as a single musical pitch with a sound quality produced by the harmonic spectrum responsible for the waveform. This waveform can be captured and displayed with data collection hardware and software. The fundamental frequency can then be calculated and compared with what would be expected from the frequencies of the tuning forks. Also, graphing software can be used to determine equations for the waveforms and predict their shapes. This experiment could be used in an introductory physics or musical acoustics course as a practical lesson in superposition of waves, basic Fourier series and the relationship between some of the ear's subjective perceptions of sound and the physical properties of the waves that cause them.

  4. A uniqueness theorem of a boundary inverse problem of a differential operator on an interval with integro-differential boundary conditions

    NASA Astrophysics Data System (ADS)

    Kanguzhin, Baltabek; Tokmagambetov, Niyaz

    2016-08-01

    In this work, we research a boundary inverse problem of spectral analysis of a differential operator with integral boundary conditions in the functional space L2(0, b) where b < ∞. A uniqueness theorem of the inverse boundary problem in L2(0, b) is proved. Note that a boundary inverse problem of spectral analysis is the problem of recovering boundary conditions of the operator by its spectrum and some additional data.

  5. 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.

  6. 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.

  7. Predicting long-term risk for relationship dissolution using nonparametric conditional survival trees.

    PubMed

    Kliem, Sören; Weusthoff, Sarah; Hahlweg, Kurt; Baucom, Katherine J W; Baucom, Brian R

    2015-12-01

    Identifying risk factors for divorce or separation is an important step in the prevention of negative individual outcomes and societal costs associated with relationship dissolution. Programs that aim to prevent relationship distress and dissolution typically focus on changing processes that occur during couple conflict, although the predictive ability of conflict-specific variables has not been examined in the context of other factors related to relationship dissolution. The authors examine whether emotional responding and communication during couple conflict predict relationship dissolution after controlling for overall relationship quality and individual well-being. Using nonparametric conditional survival trees, the study at hand simultaneously examined the predictive abilities of physiological (systolic and diastolic blood pressure, heart rate, cortisol) and behavioral (fundamental frequency; f0) indices of emotional responding, as well as observationally coded positive and negative communication behavior, on long-term relationship stability after controlling for relationship satisfaction and symptoms of depression. One hundred thirty-six spouses were assessed after participating in a randomized clinical trial of a relationship distress prevention program as well as 11 years thereafter; 32.5% of the couples' relationships had dissolved by follow up. For men, the only significant predictor of relationship dissolution was cortisol change score (p = .012). For women, only f0 range was a significant predictor of relationship dissolution (p = .034). These findings highlight the importance of emotional responding during couple conflict for long-term relationship stability. PMID:26192131

  8. 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.

  9. Elevated neutrophil to lymphocyte ratio predicts mortality in medical inpatients with multiple chronic conditions.

    PubMed

    Isaac, Vivian; Wu, Chia-Yi; Huang, Chun-Ta; Baune, Bernhard T; Tseng, Chia-Lin; McLachlan, Craig S

    2016-06-01

    Neutrophil to lymphocyte ratio (NLR) is an easy measurable laboratory marker used to evaluate systemic inflammation. Elevated NLR is associated with poor survival and increased morbidity in cancer and cardiovascular disease. However, the usefulness of NLR to predict morbidity and mortality in a hospital setting for patients with multiple chronic conditions has not been previously examined. In this study, we investigate the association between NLR and mortality in multimorbid medical inpatients. Two hundred thirty medical in-patients with chronic conditions were selected from a single academic medical center in Taiwan. Retrospective NLRs were calculated from routine full blood counts previously obtained during the initial hospital admission and at the time of discharge. Self-rated health (using a single-item question), medical disorders, depressive symptoms, and medical service utilization over a 1-year period were included in the analyses. Mortality outcomes were ascertained by reviewing electronic medical records and follow-up. The mortality rate at 2-year follow-up was 23%. Depression (odds ratio [OR] 1.9 [95% CI 1.0-3.7]), poor self-rated health (OR 2.1 [95% CI 1.1-3.9]), being hospitalized 2 or more times in the previous year (OR 2.3 [95% CI 1.2-4.6]), metastatic cancer (OR 4.7 [95% CI 2.3-9.7]), and chronic liver disease (OR 4.3 [95% CI 1.5-12.1]) were associated with 2-year mortality. The median (interquartile range) NLR at admission and discharge were 4.47 (2.4-8.7) and 3.65 (2.1-6.5), respectively. Two-year mortality rates were higher in patients with an elevated NLR at admission (NLR <3 = 15.5%, NLR >3 = 27.6%) and discharge (NLR < 3 = 14.7%, NLR >3 = 29.1%). Multivariate logistic regression demonstrated that an elevated NLR >3.0 at admission (OR 2.3 [95% CI 1.0-5.2]) and discharge (OR 2.3 [95% CI 1.1-5.0]) were associated with mortality independent of baseline age, sex, education, metastatic cancer, liver disease, depression, and previous

  10. 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...

  11. Toxicokinetics of perfluorooctane sulfonate in birds under environmentally realistic exposure conditions and development of a kinetic predictive model.

    PubMed

    Tarazona, J V; Rodríguez, C; Alonso, E; Sáez, M; González, F; San Andrés, M D; Jiménez, B; San Andrés, M I

    2015-01-22

    This article describes the toxicokinetics of perfluorooctane sulfonate (PFOS) in birds under low repeated dosing, equivalent to 0.085 μg/kg per day, representing environmentally realistic exposure conditions. The best fitting was provided by a simple pseudo monocompartmental first-order kinetics model, regulated by two rates, with a pseudo first-order dissipation half-life of 230 days, accounting for real elimination as well as binding of PFOS to non-exchangeable structures. The calculated assimilation efficiency was 0.66 with confidence intervals of 0.64 and 0.68. The model calculations confirmed that the measured maximum concentrations were still far from the steady state situation, which for this dose regime, was estimated at a value of about 65 μg PFOS/L serum achieved after a theoretical 210 weeks continuous exposure. The results confirm a very different kinetics than that observed in single-dose experiments confirming clear dose-related differences in apparent elimination rates in birds, as described for humans and monkeys; suggesting that a capacity-limited saturable process should also be considered in the kinetic behavior of PFOS in birds. Pseudo first-order kinetic models are highly convenient and frequently used for predicting bioaccumulation of chemicals in livestock and wildlife; the study suggests that previous bioaccumulation models using half-lives obtained at high doses are expected to underestimate the biomagnification potential of PFOS. The toxicokinetic parameters presented here can be used for higher-tier bioaccumulation estimations of PFOS in chickens and as surrogate values for modeling PFOS kinetics in wild bird species. PMID:25445721

  12. Microbial Forensics: Predicting Phenotypic Characteristics and Environmental Conditions from Large-Scale Gene Expression Profiles

    PubMed Central

    Kim, Minseung; Zorraquino, Violeta; Tagkopoulos, Ilias

    2015-01-01

    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. PMID:25774498

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. In Patients Experiencing Biochemical Failure After Radiotherapy, Pretreatment Risk Group and PSA Velocity Predict Differences in Overall Survival and Biochemical Failure-Free Interval

    SciTech Connect

    Soto, Daniel E. Andridge, Rebecca R.; Pan, Charlie C.; Williams, Scott G.; Taylor, Jeremy M.G.; Sandler, Howard M.

    2008-08-01

    Purpose: To characterize the demographics and survival outcomes of localized prostate cancer patients who developed biochemical failure (BF) according to a prostate-specific antigen (PSA) nadir plus 2 ng/mL. Methods and Materials: We identified 375 prostate cancer patients who had undergone external beam radiotherapy without androgen deprivation therapy but with sufficient PSA data to study PSA kinetics. Of these patients, we identified 82 with BF. The pretreatment PSA velocity was calculated for each patient. Results: For the BF cohort, 26% were low-risk and 74% were intermediate- or high-risk patients. Of the 82 BF patients, 16 (20%) were noted to have both low-risk disease and a pretreatment low PSA velocity of {<=}2 ng/mL/y (termed 'low-risk low-velocity' [LRLV]). The remaining BF patients had either intermediate- or high-risk features or a high PSA velocity >2 ng/mL/y (termed 'higher risk' [HR]). For patients who had BF, the LRLV group had a delayed median time to BF of 55 months compared with 33 months for the HR patients (p = 0.04). With a median clinical follow-up of 112 months, the 5-year overall survival rate was 100% for the LRLV BF patients vs. 84% for the HR patients (p = 0.02). Conclusions: We observed that LRLV BF patients represent a sizeable proportion of all patients with treatment failure. However, when comparing LRLV BF with HR BF patients, the former had significantly better overall survival and a longer interval to BF. This suggests that not all BF events are equivalent and emphasizes the challenges associated with using BF alone as a surrogate for a survival endpoint.

  1. 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.

  2. 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. PMID:26773481

  3. Transport of simazine in unsaturated sandy soil and predictions of its leaching under hypothetical field conditions.

    PubMed

    Suárez, Francisco; Bachmann, Jaime; Muñoz, José F; Ortiz, Cristian; Tyler, Scott W; Alister, Claudio; Kogan, Marcelo

    2007-12-01

    The potential contamination of groundwater by herbicides is often controlled by processes in the vadose zone, through which herbicides travel before entering groundwater. In the vadose zone, both physical and chemical processes affect the fate and transport of herbicides, therefore it is important to represent these processes by mathematical models to predict contaminant movement. To simulate the movement of simazine, a herbicide commonly used in Chilean vineyards, batch and miscible displacement column experiments were performed on a disturbed sandy soil to quantify the primary parameters and processes of simazine transport. Chloride (Cl(-)) was used as a non-reactive tracer, and simazine as the reactive tracer. The Hydrus-1D model was used to estimate the parameters by inversion from the breakthrough curves of the columns and to evaluate the potential groundwater contamination in a sandy soil from the Casablanca Valley, Chile. The two-site, chemical non-equilibrium model was observed to best represent the experimental results of the miscible displacement experiments in laboratory soil columns. Predictions of transport under hypothetical field conditions using the same soil from the column experiments were made for 40 years by applying herbicide during the first 20 years, and then halting the application and considering different rates of groundwater recharge. For recharge rates smaller than 84 mm year(-1), the predicted concentration of simazine at a depth of 1 m is below the U.S. EPA's maximum contaminant levels (4 microg L(-1)). After eight years of application at a groundwater recharge rate of 180 mm year(-1) (approximately 50% of the annual rainfall), simazine was found to reach the groundwater (located at 1 m depth) at a higher concentration (more than 40 microg L(-1)) than the existing guidelines in the USA and Europe. PMID:17604874

  4. 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

  5. Experimental tests and predictive model of an adsorptive air conditioning unit

    SciTech Connect

    Poyelle, F.; Guilleminot, J.J.; Meunier, F.

    1999-01-01

    An adsorption air conditioning unit has been built operating with a heat nd mass recovery cycle and a zeolite-water pair. A new consolidated adsorbent composite with good heat transfer properties has been developed and implemented in the adsorber. At an evaporating temperature of 4 C, the experimental specific cooling power (SCP) of 97 W/kg achieved represents a real improvement in comparison with those measured with a packed bed technology. At this evaporating pressure, the mass transfer resistance controls the process. Therefore, at higher evaporating temperature a COP of 0.68 and a SCP of 135 W/kg were experimentally achieved. A new model has been developed to take into account the mass transfer limitations. The model has been validated and can predict the average pressure inside the adsorber and the components temperature of the unit. A new high conductive material with enhanced mass transfer properties has been developed. The predictive model shows that a SCP of 600 W/kg and a COP of 0.74 could be achieved with this new material.

  6. 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

  7. Mercury's core fraction and ancient crustal composition: Predictions from planetary formation under extremely reducing conditions

    NASA Astrophysics Data System (ADS)

    Brown, S. M.; Elkins-Tanton, L.

    2007-12-01

    Several hypotheses have been suggested to explain the paradox of Mercury's large core, which is on the order of sixty percent of the mass of the planet and recently demonstrated to be at least partially molten. Here we suggest that extremely reducing conditions in the earliest stages of planetary accretion nearest to the Sun may have produced the unusual metallic iron fraction by reducing iron otherwise bound into silicates. We demonstrate the formation conditions necessary for various meteoritic bulk compositions to produce the core/mantle ratio of Mercury. During this hypothetical core formation, we assume the remaining silicate fraction of Mercury (now largely lacking iron) has been heated to produce a magma ocean. The resulting cumulate mantle composition is calculated in a Matlab simulation of magma ocean solidification using a CMAS system adapted for Mercury. Plagioclase flotation, frequently cited as the necessary signature of a magma ocean, is highly dependent upon initial bulk composition. We demonstrate the initial silicate iron content of the magma ocean necessary to make plagioclase buoyant and thus produce a plagioclase flotation crust as seen on the Moon. In addition, over a range of bulk compositions the solidified mantle cumulates are unstable to gravitational overturn. During overturn hot cumulates rise from depth and may cross their solidi and melt, producing an earliest planetary crust. This crust may still exist on Mercury. With the first flyby results of the MESSENGER mission coming this winter, predictions from these models can be compared with initial ground measurements.

  8. Prediction of the radiation situation during conditioned radioactive waste storage in hangar-type storage facilities

    NASA Astrophysics Data System (ADS)

    Rosnovskii, S. V.; Bulka, S. K.

    2014-02-01

    An original technology for the conditioning of solidified radioactive waste was developed by the Novovoronezh nuclear power plant (NPP) staff. The technology provides for waste placement inside NZK-150-1.5P containers with their further storage at light hangar-type storage facilities. A number of technical solutions were developed that allow for reducing the gamma-radiation dose rate from the package formed. A methodology for prediction of the radiation situation around hangars, depending on the radiation characteristics of irrecoverable shielding containers (ISCs) located in the peripheral row of a storage facility, was developed with the purpose of assuring safe storage. Based on empirical data, the field background gamma-radiation dose rate at an area as a function of the average dose rate at the hangar surface and the average dose rate close packages, placed in the peripheral row of the storage facility, was calculated. The application of the developed methodology made it possible to reduce by ten times the expenditures for the conditioning and holding of solidified radioactive waste (SRW) while unconditionally providing storage safety.

  9. 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.

  10. 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.

  11. Updating representations of temporal intervals.

    PubMed

    Danckert, James; Anderson, Britt

    2015-12-01

    Effectively engaging with the world depends on accurate representations of the regularities that make up that world-what we call mental models. The success of any mental model depends on the ability to adapt to changes-to 'update' the model. In prior work, we have shown that damage to the right hemisphere of the brain impairs the ability to update mental models across a range of tasks. Given the disparate nature of the tasks we have employed in this prior work (i.e. statistical learning, language acquisition, position priming, perceptual ambiguity, strategic game play), we propose that a cognitive module important for updating mental representations should be generic, in the sense that it is invoked across multiple cognitive and perceptual domains. To date, the majority of our tasks have been visual in nature. Given the ubiquity and import of temporal information in sensory experience, we examined the ability to build and update mental models of time. We had healthy individuals complete a temporal prediction task in which intervals were initially drawn from one temporal range before an unannounced switch to a different range of intervals. Separate groups had the second range of intervals switch to one that contained either longer or shorter intervals than the first range. Both groups showed significant positive correlations between perceptual and prediction accuracy. While each group updated mental models of temporal intervals, those exposed to shorter intervals did so more efficiently. Our results support the notion of generic capacity to update regularities in the environment-in this instance based on temporal information. The task developed here is well suited to investigations in neurological patients and in neuroimaging settings. PMID:26303026

  12. QT interval in anorexia nervosa.

    PubMed Central

    Cooke, R A; Chambers, J B; Singh, R; Todd, G J; Smeeton, N C; Treasure, J; Treasure, T

    1994-01-01

    OBJECTIVES--To determine the incidence of a long QT interval as a marker for sudden death in patients with anorexia nervosa and to assess the effect of refeeding. To define a long QT interval by linear regression analysis and estimation of the upper limit of the confidence interval (95% CI) and to compare this with the commonly used Bazett rate correction formula. DESIGN--Prospective case control study. SETTING--Tertiary referral unit for eating disorders. SUBJECTS--41 consecutive patients with anorexia nervosa admitted over an 18 month period. 28 age and sex matched normal controls. MAIN OUTCOME MEASURES--maximum QT interval measured on 12 lead electrocardiograms. RESULTS--43.6% of the variability in the QT interval was explained by heart rate alone (p < 0.00001) and group analysis contributed a further 5.9% (p = 0.004). In 6 (15%) patients the QT interval was above the upper limit of the 95% CI for the prediction based on the control equation (NS). Two patients died suddenly; both had a QT interval at or above the upper limit of the 95% CI. In patients who reached their target weights the QT interval was significantly shorter (median 9.8 ms; p = 0.04) relative to the upper limit of the 60% CI of the control regression line, which best discriminated between patients and controls. The median Bazett rate corrected QT interval (QTc) in patients and controls was 435 v 405 ms.s-1/2 (p = 0.0004), and before and after refeeding it was 435 v 432 ms.s1/2 (NS). In 14(34%) patients and three (11%) controls the QTc was > 440 ms.s-1/2 (p = 0.053). CONCLUSIONS--The QT interval was longer in patients with anorexia nervosa than in age and sex matched controls, and there was a significant tendency to reversion to normal after refeeding. The Bazett rate correction formula overestimated the number of patients with QT prolongation and also did not show an improvement with refeeding. PMID:8068473

  13. 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.

  14. A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization.

    PubMed

    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 R (2)~ 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

  15. 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

  16. 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

  17. Can the soil conditioning index predict soil organic carbon sequestration with conservation agricultural systems in the South?

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The soil conditioning index (SCI) is a relatively simple model used by NRCS to predict changes in soil organic C. It is based on three important conditions: (1) organic material (OM), (2) field operations (FO), and (3) erosion (ER). Our objective was to develop quantitative relationships between (...

  18. 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

    2016-05-01

    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

  19. 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.

  20. 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. PMID:26854560

  1. 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...

  2. 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. PMID:22262906

  3. 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.

  4. 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. PMID:11594634

  5. 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

  6. 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.

  7. 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.

  8. Relationships between host body condition and immunocompetence, not host sex, best predict parasite burden in a bat-helminth system.

    PubMed

    Warburton, Elizabeth M; Pearl, Christopher A; Vonhof, Maarten J

    2016-06-01

    Sex-biased parasitism highlights potentially divergent approaches to parasite resistance resulting in differing energetic trade-offs for males and females; however, trade-offs between immunity and self-maintenance could also depend on host body condition. We investigated these relationships in the big brown bat, Eptesicus fuscus, to determine if host sex or body condition better predicted parasite resistance, if testosterone levels predicted male parasite burdens, and if immune parameters could predict male testosterone levels. We found that male and female hosts had similar parasite burdens and female bats scored higher than males in only one immunological measure. Top models of helminth burden revealed interactions between body condition index and agglutination score as well as between agglutination score and host sex. Additionally, the strength of the relationships between sex, agglutination, and helminth burden is affected by body condition. Models of male parasite burden provided no support for testosterone predicting helminthiasis. Models that best predicted testosterone levels did not include parasite burden but instead consistently included month of capture and agglutination score. Thus, in our system, body condition was a more important predictor of immunity and worm burden than host sex. PMID:26898834

  9. Predicting Low Flow Conditions from Climatic Indices - Indicator-Based Modeling for Climate Change Impact Assessment

    NASA Astrophysics Data System (ADS)

    Fangmann, Anne; Haberlandt, Uwe

    2014-05-01

    In the face of climate change, the assessment of future hydrological regimes has become indispensable in the field of water resources management. Investigation of potential change is vital for proper planning, especially with regard to hydrological extremes. Commonly, projection of future streamflow is done applying process-based hydrological models, using climate model data as input, whose complex model structures generally require excessive amounts of time and effort for set-up and computation. This study aims at identifying practical alternatives to the employment of sophisticated models by considering simpler, yet sufficiently accurate methods for modeling rainfall-runoff relations with regard to hydrological extremes. The focus is thereby put on the prediction of low flow periods, which are, in contrast to flood events, characterized by extended durations and spatial dimensions. The models to be established in this study base on indicators, which characterize both meteorological and hydrological conditions within dry periods. This approach makes direct use of the coupling between atmospheric driving forces and streamflow response with the underlying presumption that low-precipitation and high-evaporation periods result in diminished flow, implying that relationships exist between the properties of both meteorological and hydrological events (duration, volume, severity etc.). Eventually, optimal combinations of meteorological indicators are sought that are suitable to predict various low flow characteristics with satisfactory accuracy. Two approaches for model specification are tested: a) multiple linear regression, and b) Fuzzy logic. The data used for this study are daily time series of mean discharge obtained from 294 gauges with variable record length situated in the federal state of Lower Saxony, Germany, as well as interpolated climate variables available for a period from 1951 to 2011. After extraction of a variety of indicators from the available

  10. Predicting New Target Conditions for Drug Retesting Using Temporal Patterns in Clinical Trials: A Proof of Concept.

    PubMed

    He, Zhe; Weng, Chunhua

    2015-01-01

    Drug discovery is costly and time-consuming. Efficient drug repurposing promises to accelerate drug discovery with reduced cost. However, most successful repurposing cases so far have been achieved by serendipity. There is a need for more efficient computational methods for predicting new indications for existing drugs. This paper conducts a retrospective analysis of the temporal patterns of drug intervention trials for every drug in a pair of different conditions in ClinicalTrials.gov, including 550 drugs used for 451 conditions between 2003 and 2013. We found that drugs are often targeted towards conditions that are related by similar or identical eligibility criteria. We demonstrated the preliminary feasibility of predicting new target conditions for drug retesting among conditions with similar aggregated clinical trial eligibility criteria and confirmed this hypothesis using evidence from the literature. PMID:26306283

  11. Predicting New Target Conditions for Drug Retesting Using Temporal Patterns in Clinical Trials: A Proof of Concept

    PubMed Central

    He, Zhe; Weng, Chunhua

    2015-01-01

    Drug discovery is costly and time-consuming. Efficient drug repurposing promises to accelerate drug discovery with reduced cost. However, most successful repurposing cases so far have been achieved by serendipity. There is a need for more efficient computational methods for predicting new indications for existing drugs. This paper conducts a retrospective analysis of the temporal patterns of drug intervention trials for every drug in a pair of different conditions in ClinicalTrials.gov, including 550 drugs used for 451 conditions between 2003 and 2013. We found that drugs are often targeted towards conditions that are related by similar or identical eligibility criteria. We demonstrated the preliminary feasibility of predicting new target conditions for drug retesting among conditions with similar aggregated clinical trial eligibility criteria and confirmed this hypothesis using evidence from the literature. PMID:26306283

  12. 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

  13. 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

  14. Surface roughness prediction from combination of cutting forces, turning vibrations and machining conditions using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Morala-Argüello, P.; Barreiro, J.; Alegre, E.; García-Ordás, M.; García-Olalla, Oscar; González-Madruga, D.

    2012-04-01

    Nowadays the evaluation of surface quality of manufactured products continues being a very important task for the industry. One of the lines of research associated to this subject tries to predict the surface roughness using signal analysis, such as vibrations, in the machining process. Many researchers have proposed models for determining the roughness based on the cutting conditions or the cutting forces. As the surface roughness depends on many variables, in this work different statistical values of cutting forces, tool vibration values and machining conditions are considered together for predicting surface roughness of turned metallic parts using artificial neural networks. We have notice the best predictions have been obtained when force and cutting conditions were combined together. The absolute error values obtained have been always below to 1.28 and 1.11 μm when using the median and root mean square (RMS) as descriptors, respectively.

  15. A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients.

    PubMed

    Sideris, Costas; Alshurafa, Nabil; Pourhomayoun, Mohammad; Shahmohammadi, Farhad; Samy, Lauren; Sarrafzadeh, Majid

    2015-08-01

    In this paper, we propose a novel methodology for utilizing disease diagnostic information to predict severity of condition for Congestive Heart Failure (CHF) patients. Our methodology relies on a novel, clustering-based, feature extraction framework using disease diagnostic information. To reduce the dimensionality we identify disease clusters using cooccurence frequencies. We then utilize these clusters as features to predict patient severity of condition. We build our clustering and feature extraction algorithm using the 2012 National Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP) which contains 7 million discharge records and ICD-9-CM codes. The proposed framework is tested on Ronald Reagan UCLA Medical Center Electronic Health Records (EHR) from 3041 patients. We compare our cluster-based feature set with another that incorporates the Charlson comorbidity score as a feature and demonstrate an accuracy improvement of up to 14% in the predictability of the severity of condition. PMID:26736808

  16. Global atmospheric sulfur budget under volcanically quiescent conditions: Aerosol-chemistry-climate model predictions and validation

    NASA Astrophysics Data System (ADS)

    Sheng, Jian-Xiong; Weisenstein, Debra K.; Luo, Bei-Ping; Rozanov, Eugene; Stenke, Andrea; Anet, Julien; Bingemer, Heinz; Peter, Thomas

    2015-01-01

    The global atmospheric sulfur budget and its emission dependence have been investigated using the coupled aerosol-chemistry-climate model SOCOL-AER. The aerosol module comprises gaseous and aqueous sulfur chemistry and comprehensive microphysics. The particle distribution is resolved by 40 size bins spanning radii from 0.39 nm to 3.2 μm, including size-dependent particle composition. Aerosol radiative properties required by the climate model are calculated online from the aerosol module. The model successfully reproduces main features of stratospheric aerosols under nonvolcanic conditions, including aerosol extinctions compared to Stratospheric Aerosol and Gas Experiment II (SAGE II) and Halogen Occultation Experiment, and size distributions compared to in situ measurements. The calculated stratospheric aerosol burden is 109 Gg of sulfur, matching the SAGE II-based estimate (112 Gg). In terms of fluxes through the tropopause, the stratospheric aerosol layer is due to about 43% primary tropospheric aerosol, 28% SO2, 23% carbonyl sulfide (OCS), 4% H2S, and 2% dimethyl sulfide (DMS). Turning off emissions of the short-lived species SO2, H2S, and DMS shows that OCS alone still establishes about 56% of the original stratospheric aerosol burden. Further sensitivity simulations reveal that anticipated increases in anthropogenic SO2 emissions in China and India have a larger influence on stratospheric aerosols than the same increase in Western Europe or the U.S., due to deep convection in the western Pacific region. However, even a doubling of Chinese and Indian emissions is predicted to increase the stratospheric background aerosol burden only by 9%. In contrast, small to moderate volcanic eruptions, such as that of Nabro in 2011, may easily double the stratospheric aerosol loading.

  17. Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

    PubMed Central

    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. PMID:25803302

  18. 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.

  19. 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

  20. Interval polynomial positivity

    NASA Technical Reports Server (NTRS)

    Bose, N. K.; Kim, K. D.

    1989-01-01

    It is shown that a univariate interval polynomial is globally positive if and only if two extreme polynomials are globally positive. It is shown that the global positivity property of a bivariate interval polynomial is completely determined by four extreme bivariate polynomials. The cardinality of the determining set for k-variate interval polynomials is 2k. One of many possible generalizations, where vertex implication for global positivity holds, is made by considering the parameter space to be the set dual of a boxed domain.

  1. 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...

  2. The Application of the Unified Homogeneous Periodical Boundary Conditions to the Prediction of Effective Elastic Stiffness in a Widespread Field

    NASA Astrophysics Data System (ADS)

    Yu, Dong; Yang, Hong; Luo, Dong-Mei

    2011-06-01

    Periodical boundary conditions (PBC) are important for the prediction of effective elastic stiffness of composites by applying the macro-microscopic asymptotic expansion homogenization method (HM). In this paper, two kinds of homogeneous periodical boundary conditions are proposed to satisfy the improved expression for the homogenized effective stiffness with the homogeneous characteristic function, and one is the relaxed periodical boundary condition, and the other is a precise polynomial derived from the first one. A typical example of the off-axis short-fiber reinforced composites is analyzed by the described procedure. The results show that the periodical boundary condition is not unique, and the relaxed periodic boundary condition is the simplest and most convenient method to guarantee periodical displacement and anti-periodical traction boundary conditions simultaneously in a widespread field with a unified form.

  3. 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.

  4. Predicting soil organic carbon sequestration in crop production systems of the southeastern USA with EPIC and the soil conditioning index

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Soil Conditioning Index (SCI), administered by the USDA-Natural Resource Conservation Service, predicts a positive or negative trend in soil organic carbon (SOC) based on knowledge of field operations, erosion loss, and organic matter inputs, but has not been adequately calibrated against long-t...

  5. 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

  6. Post-Extinction Conditional Stimulus Valence Predicts Reinstatement Fear: Relevance for Long Term Outcomes of Exposure Therapy

    PubMed Central

    Zbozinek, Tomislav D.; Hermans, Dirk; Prenoveau, Jason M.; Liao, Betty; Craske, Michelle G.

    2014-01-01

    Exposure therapy for anxiety disorders is translated from fear conditioning and extinction. While exposure therapy is effective in treating anxiety, fear sometimes returns after exposure. One pathway for return of fear is reinstatement: unsignaled unconditional stimuli following completion of extinction. The present study investigated the extent to which valence of the conditional stimulus (CS+) after extinction predicts return of CS+ fear after reinstatement. Participants (N = 84) engaged in a differential fear conditioning paradigm and were randomized to reinstatement or non-reinstatement. We hypothesized that more negative post-extinction CS+ valence would predict higher CS+ fear after reinstatement relative to non-reinstatement and relative to extinction retest. Results supported the hypotheses and suggest that strategies designed to decrease negative valence of the CS+ may reduce the return of fear via reinstatement following exposure therapy. PMID:24957680

  7. 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

  8. Methodology for prediction of ecological impacts under real conditions in Mexico

    NASA Astrophysics Data System (ADS)

    Bojórquez-Tapia, Luis Antonio

    1989-09-01

    Most environmental impact assessments in Mexico are short-duration studies that tend to be descriptive rather than analytical and predictive. Therefore, a general methodology has been developed for the prediction and communication of ecological impacts in situations where time, funds, and information are major limitations. The approach] involves two stages: (1) environmental characterization and (2) prediction and analysis of impacts. In stage 1, the assessment team is divided into autonomous groups; the groups work on the same land systems, which are characterized based on literature, aerial photography, and short field observations. In stage 2, the groups meet to evaluate impacts by means of interaction matrices and nonnumerical simulation models. This methodology has proved to be valuable to perform environmental impact statements for large engineering projects in Mexico.

  9. Techniques for the Enhancement of Linear Predictive Speech Coding in Adverse Conditions

    NASA Astrophysics Data System (ADS)

    Wrench, Alan A.

    Available from UMI in association with The British Library. Requires signed TDF. The Linear Prediction model was first applied to speech two and a half decades ago. Since then it has been the subject of intense research and continues to be one of the principal tools in the analysis of speech. Its mathematical tractability makes it a suitable subject for study and its proven success in practical applications makes the study worthwhile. The model is known to be unsuited to speech corrupted by background noise. This has led many researchers to investigate ways of enhancing the speech signal prior to Linear Predictive analysis. In this thesis this body of work is extended. The chosen application is low bit-rate (2.4 kbits/sec) speech coding. For this task the performance of the Linear Prediction algorithm is crucial because there is insufficient bandwidth to encode the error between the modelled speech and the original input. A review of the fundamentals of Linear Prediction and an independent assessment of the relative performance of methods of Linear Prediction modelling are presented. A new method is proposed which is fast and facilitates stability checking, however, its stability is shown to be unacceptably poorer than existing methods. A novel supposition governing the positioning of the analysis frame relative to a voiced speech signal is proposed and supported by observation. The problem of coding noisy speech is examined. Four frequency domain speech processing techniques are developed and tested. These are: (i) Combined Order Linear Prediction Spectral Estimation; (ii) Frequency Scaling According to an Aural Model; (iii) Amplitude Weighting Based on Perceived Loudness; (iv) Power Spectrum Squaring. These methods are compared with the Recursive Linearised Maximum a Posteriori method. Following on from work done in the frequency domain, a time domain implementation of spectrum squaring is developed. In addition, a new method of power spectrum estimation is

  10. 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…

  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. 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.

  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. 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.

  13. Merger Rates of Double Neutron Stars and Stellar Origin Black Holes: The Impact of Initial Conditions on Binary Evolution Predictions

    NASA Astrophysics Data System (ADS)

    de Mink, S. E.; Belczynski, K.

    2015-11-01

    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.

  14. 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.

  15. 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.

  16. 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...

  17. 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.

  18. Proper Interval Vertex Deletion

    NASA Astrophysics Data System (ADS)

    Villanger, Yngve

    Deleting a minimum number of vertices from a graph to obtain a proper interval graph is an NP-complete problem. At WG 2010 van Bevern et al. gave an O((14k + 14) k + 1 kn 6) time algorithm by combining iterative compression, branching, and a greedy algorithm. We show that there exists a simple greedy O(n + m) time algorithm that solves the Proper Interval Vertex Deletion problem on \\{claw,net,allowbreak tent,allowbreak C_4,C_5,C_6\\}-free graphs. Combining this with branching on the forbidden structures claw,net,tent,allowbreak C_4,C_5, and C 6 enables us to get an O(kn 6 6 k ) time algorithm for Proper Interval Vertex Deletion, where k is the number of deleted vertices.

  19. 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.

  20. 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...

  1. 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.

  2. Predicting Strength and Fatigue for Suited and Unsuited Conditions from Empirical Data

    NASA Technical Reports Server (NTRS)

    Maida, James C.; Gonzalez, L. J.; Rajulu, S.; Russo, Dane M. (Technical Monitor)

    2001-01-01

    The need for longer and more labor-intensive extra-vehicular activities (EVA) is required for construction and maintenance of the International Space Station (ISS). Issues pertaining to human performance while wearing a space suit (EMU) for prolonged periods have become more important. This project was conducted to investigate how a pressurized Extra-vehicular Mobility Unit (EMU) affects human upper body joint strength and fatigue and how to predict it from computer models based on the data collected.

  3. Mechanistic prediction of fission product release under normal and accident conditions: key uncertainties that need better resolution

    SciTech Connect

    Rest, J.

    1983-09-01

    A theoretical model has been used for predicting the behavior of fission gas and volatile fission products (VFPs) in UO/sub 2/-base fuels during steady-state and transient conditions. This model represents an attempt to develop an efficient predictive capability for the full range of possible reactor operating conditions. Fission products released from the fuel are assumed to reach the fuel surface by successively diffusing (via atomic and gas-bubble mobility) from the grains to grain faces and then to the grain edges, where the fission products are released through a network of interconnected tunnels of fission-gas induced and fabricated porosity. The model provides for a multi-region calculation and uses only one size class to characterize a distribution of fission gas bubbles.

  4. Mechanistic prediction of fission-product release under normal and accident conditions: key uncertainties that need better resolution. [PWR; BWR

    SciTech Connect

    Rest, J.

    1983-09-01

    A theoretical model has been used for predicting the behavior of fission gas and volatile fission products (VFPs) in UO/sub 2/-base fuels during steady-state and transient conditions. This model represents an attempt to develop an efficient predictive capability for the full range of possible reactor operating conditions. Fission products released from the fuel are assumed to reach the fuel surface by successively diffusing (via atomic and gas-bubble mobility) from the grains to grain faces and then to the grain edges, where the fission products are released through a network of interconnected tunnels of fission-gas induced and fabricated porosity. The model provides for a multi-region calculation and uses only one size class to characterize a distribution of fission gas bubbles.

  5. 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.

  6. 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.

  7. The Effect of Initial Conditions and Discussion on Students' Predictions for Interactive Lecture Demonstrations

    NASA Astrophysics Data System (ADS)

    Marx, Jeffrey

    2008-10-01

    Over the past eight years at McDaniel College, students' Predictions for various Interactive Lecture Demonstrations (ILDs) have improved markedly. One explanation is that students have become increasingly sophisticated in their understanding of kinematics and dynamics. Another possible explanation is that the class as a whole is only slightly more sophisticated, and during the Discussion Phase of the ILD the correct Predication is very successfully transmitted within groups and between groups. The purpose of this paper is to support the proposition of this possible explanation. To begin to address this idea, I present an overview of and results from a preliminary, computer-based simulation of classroom discussion.

  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. 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…

  10. 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...