Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal.

PubMed

Mohebbi, Maryam; Ghassemian, Hassan

2011-08-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal. This method consists of three steps: preprocessing, feature extraction and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the RR-interval signal is extracted. In the next step, the recurrence plot (RP) of the RR-interval signal is obtained and five statistically significant features are extracted to characterize the basic patterns of the RP. These features consist of the recurrence rate, length of longest diagonal segments (L(max )), average length of the diagonal lines (L(mean)), entropy, and trapping time. Recurrence quantification analysis can reveal subtle aspects of dynamics not easily appreciated by other methods and exhibits characteristic patterns which are caused by the typical dynamical behavior. In the final step, a support vector machine (SVM)-based classifier is used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30 min ECG recordings that end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, positive predictivity and negative predictivity were 97%, 100%, 100%, and 96%, respectively. The proposed methodology presents better results than other existing approaches.

Extended lowered body temperature increases the effective CS-US interval in conditioned taste aversion for adult rats.

PubMed

Hinderliter, Charles F; Goodhart, Mark; Anderson, Matthew J; Misanin, James R

2002-06-01

Assuming body temperature correlates with metabolic activities, rate of body temperature recovery was manipulated to assess effects on long-trace conditioning in a conditioned taste-aversion paradigm. Following 10 min. access to a .1% saccharin solution and then 10 min. immersion in 0-0.5 degrees C water, two groups of 16 Wistar-derived, 81-113 day-old, male albino rats received either saline or lithium chloride injections 3 hr. later. These two groups were subdivided on basis of warming rate during the 3-hr. interval. Half of the rats recovered at room temperature (20 degrees to 21 degrees C), and half recovered in an incubator maintained at 30 degrees C. Maintaining a lowered body temperature between the conditioned stimulus and unconditioned stimulus allowed an association to be made at 3 hr., an interval that normally does not support conditioning. In contrast, lowering body temperature and then inducing a fast warming rate did not produce evidence of an aversion. It is suggested that maintaining a low body temperature over the interval between the presentation of the conditioned stimulus and unconditioned stimulus slows a metabolic clock that extends the measured interval at which associations can be made using conditioned taste-aversion procedures.

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

PubMed Central

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

2012-01-01

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

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

PubMed

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

2015-08-01

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

The effects of context and musical training on auditory temporal-interval discrimination.

PubMed

Banai, Karen; Fisher, Shirley; Ganot, Ron

2012-02-01

Non sensory factors such as stimulus context and musical experience are known to influence auditory frequency discrimination, but whether the context effect extends to auditory temporal processing remains unknown. Whether individual experiences such as musical training alter the context effect is also unknown. The goal of the present study was therefore to investigate the effects of stimulus context and musical experience on auditory temporal-interval discrimination. In experiment 1, temporal-interval discrimination was compared between fixed context conditions in which a single base temporal interval was presented repeatedly across all trials and variable context conditions in which one of two base intervals was randomly presented on each trial. Discrimination was significantly better in the fixed than in the variable context conditions. In experiment 2 temporal discrimination thresholds of musicians and non-musicians were compared across 3 conditions: a fixed context condition in which the target interval was presented repeatedly across trials, and two variable context conditions differing in the frequencies used for the tones marking the temporal intervals. Musicians outperformed non-musicians on all 3 conditions, but the effects of context were similar for the two groups. Overall, it appears that, like frequency discrimination, temporal-interval discrimination benefits from having a fixed reference. Musical experience, while improving performance, did not alter the context effect, suggesting that improved discrimination skills among musicians are probably not an outcome of more sensitive contextual facilitation or predictive coding mechanisms. Copyright © 2011 Elsevier B.V. All rights reserved.

On solving wave equations on fixed bounded intervals involving Robin boundary conditions with time-dependent coefficients

NASA Astrophysics Data System (ADS)

van Horssen, Wim T.; Wang, Yandong; Cao, Guohua

2018-06-01

In this paper, it is shown how characteristic coordinates, or equivalently how the well-known formula of d'Alembert, can be used to solve initial-boundary value problems for wave equations on fixed, bounded intervals involving Robin type of boundary conditions with time-dependent coefficients. A Robin boundary condition is a condition that specifies a linear combination of the dependent variable and its first order space-derivative on a boundary of the interval. Analytical methods, such as the method of separation of variables (SOV) or the Laplace transform method, are not applicable to those types of problems. The obtained analytical results by applying the proposed method, are in complete agreement with those obtained by using the numerical, finite difference method. For problems with time-independent coefficients in the Robin boundary condition(s), the results of the proposed method also completely agree with those as for instance obtained by the method of separation of variables, or by the finite difference method.

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…

Perceptual Responses to High- and Moderate-Intensity Interval Exercise in Adolescents.

PubMed

Malik, Adam A; Williams, Craig A; Weston, Kathryn L; Barker, Alan R

2018-05-01

Continuous high-intensity exercise is proposed to evoke unpleasant sensations as predicted by the dual-mode theory and may negatively impact on future exercise adherence. Previous studies support unpleasant sensations in affective responses during continuous high-intensity exercise, but the affect experience during high-intensity interval exercise (HIIE) involving brief bursts of high-intensity exercise separated by low-intensity activity is poorly understood in adolescents. We examined the acute affective, enjoyment, and perceived exertion responses to HIIE compared with moderate-intensity interval exercise (MIIE) in adolescents. Thirteen adolescent boys (mean ± SD: age, 14.0 ± 0.5 yr) performed two counterbalanced exercise conditions: 1) HIIE: 8 × 1-min work intervals at 90% maximal aerobic speed; and 2) MIIE: between 9 and 12 × 1-min work intervals at 90% ventilatory threshold where the number of intervals performed were distance-matched to HIIE. HIIE and MIIE work intervals were interspersed with 75 s active recovery at 4 km·h. Affect, enjoyment, and RPE were recorded before, during, and after exercise. Affect responses declined in both conditions but the fall was greater in HIIE than MIIE (P < 0.025, effect size [ES], 0.64 to 0.81). Affect remained positive at the end-work interval for both conditions (MIIE, 2.62 ± 1.50; HIIE, 1.15 ± 2.08 on feeling scale). No enjoyment differences were evident during HIIE and MIIE (P = 0.32), but HIIE elicited greater postexercise enjoyment compared with MIIE (P = 0.01, ES = 0.47). RPE was significantly higher during HIIE than MIIE across all work intervals (all P < 0.03, ES > 0.64). Despite elevated RPE, HIIE did not elicit prominent unpleasant feelings as predicted by the dual-mode theory and was associated with greater postexercise enjoyment responses than MIIE. This study demonstrates the feasibility of the application of HIIE as an alternative form of physical activity in adolescents.

Intervals for posttest probabilities: a comparison of 5 methods.

PubMed

Mossman, D; Berger, J O

2001-01-01

Several medical articles discuss methods of constructing confidence intervals for single proportions and the likelihood ratio, but scant attention has been given to the systematic study of intervals for the posterior odds, or the positive predictive value, of a test. The authors describe 5 methods of constructing confidence intervals for posttest probabilities when estimates of sensitivity, specificity, and the pretest probability of a disorder are derived from empirical data. They then evaluate each method to determine how well the intervals' coverage properties correspond to their nominal value. When the estimates of pretest probabilities, sensitivity, and specificity are derived from more than 80 subjects and are not close to 0 or 1, all methods generate intervals with appropriate coverage properties. When these conditions are not met, however, the best-performing method is an objective Bayesian approach implemented by a simple simulation using a spreadsheet. Physicians and investigators can generate accurate confidence intervals for posttest probabilities in small-sample situations using the objective Bayesian approach.

Cross-modal transfer of the conditioned eyeblink response during interstimulus interval discrimination training in young rats

PubMed Central

Brown, Kevin L.; Stanton, Mark E.

2008-01-01

Eyeblink classical conditioning (EBC) was observed across a broad developmental period with tasks utilizing two interstimulus intervals (ISIs). In ISI discrimination, two distinct conditioned stimuli (CSs; light and tone) are reinforced with a periocular shock unconditioned stimulus (US) at two different CS-US intervals. Temporal uncertainty is identical in design with the exception that the same CS is presented at both intervals. Developmental changes in conditioning have been reported in each task beyond ages when single-ISI learning is well developed. The present study sought to replicate and extend these previous findings by testing each task at four separate ages. Consistent with previous findings, younger rats (postnatal day – PD - 23 and 30) trained in ISI discrimination showed evidence of enhanced cross-modal influence of the short CS-US pairing upon long CS conditioning relative to older subjects. ISI discrimination training at PD43-47 yielded outcomes similar to those in adults (PD65-71). Cross-modal transfer effects in this task therefore appear to diminish between PD30 and PD43-47. Comparisons of ISI discrimination with temporal uncertainty indicated that cross-modal transfer in ISI discrimination at the youngest ages did not represent complete generalization across CSs. ISI discrimination undergoes a more protracted developmental emergence than single-cue EBC and may be a more sensitive indicator of developmental disorders involving cerebellar dysfunction. PMID:18726989

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.

Predicting Near-Term Water Quality from Satellite Observations of Watershed Conditions

NASA Astrophysics Data System (ADS)

Weiss, W. J.; Wang, L.; Hoffman, K.; West, D.; Mehta, A. V.; Lee, C.

2017-12-01

Despite the strong influence of watershed conditions on source water quality, most water utilities and water resource agencies do not currently have the capability to monitor watershed sources of contamination with great temporal or spatial detail. Typically, knowledge of source water quality is limited to periodic grab sampling; automated monitoring of a limited number of parameters at a few select locations; and/or monitoring relevant constituents at a treatment plant intake. While important, such observations are not sufficient to inform proactive watershed or source water management at a monthly or seasonal scale. Satellite remote sensing data on the other hand can provide a snapshot of an entire watershed at regular, sub-monthly intervals, helping analysts characterize watershed conditions and identify trends that could signal changes in source water quality. Accordingly, the authors are investigating correlations between satellite remote sensing observations of watersheds and source water quality, at a variety of spatial and temporal scales and lags. While correlations between remote sensing observations and direct in situ measurements of water quality have been well described in the literature, there are few studies that link remote sensing observations across a watershed with near-term predictions of water quality. In this presentation, the authors will describe results of statistical analyses and discuss how these results are being used to inform development of a desktop decision support tool to support predictive application of remote sensing data. Predictor variables under evaluation include parameters that describe vegetative conditions; parameters that describe climate/weather conditions; and non-remote sensing, in situ measurements. Water quality parameters under investigation include nitrogen, phosphorus, organic carbon, chlorophyll-a, and turbidity.

Prediction of small-for-gestational-age neonate by third-trimester fetal biometry and impact of ultrasound-delivery interval.

PubMed

Reboul, Q; Delabaere, A; Luo, Z C; Nuyt, A-M; Wu, Y; Chauleur, C; Fraser, W; Audibert, F

2017-03-01

To compare third-trimester ultrasound screening methods to predict small-for-gestational age (SGA), and to evaluate the impact of the ultrasound-delivery interval on screening performance. In this prospective study, data were collected from a multicenter singleton cohort study investigating the links between various exposures during pregnancy with birth outcome and later health in children. We included women, recruited in the first trimester, who had complete outcome data and had undergone third-trimester ultrasound examination. Demographic, clinical and biological variables were also collected from both parents. We compared prediction of delivery of a SGA neonate (birth weight < 10 th percentile) by the following methods: abdominal circumference (AC) Z-score based on Hadlock curves (Hadlock AC), on INTERGROWTH-21 st Project curves (Intergrowth AC) and on Salomon curves (Salomon AC); estimated fetal weight (EFW) Z-score based on Hadlock curves (Hadlock EFW) and on customized curves from Gardosi (Gardosi EFW); and fetal growth velocity based on change in AC between second and third trimesters (FGVAC). We also assessed the following ultrasound-delivery intervals: ≤ 4 weeks, ≤ 6 weeks and ≤ 10 weeks. Third-trimester ultrasound was performed in 1805 patients with complete outcome data, of whom 158 (8.8%) delivered a SGA neonate. Ultrasound examination was at a median gestational age of 32 (interquartile range, 31-33) weeks. The ultrasound-delivery interval was ≤ 4 weeks in 17.2% of cases, ≤ 6 weeks in 48.1% of cases and ≤ 10 weeks in 97.3% of cases. Areas under the receiver-operating characteristics curve (AUC) were 0.772 for Salomon AC, 0.768 for Hadlock EFW, 0.766 for Hadlock AC, 0.765 for Intergrowth AC, 0.708 for Gardosi EFW and 0.674 for FGVAC (all P < 0.0001). The screening method with the highest AUC for an ultrasound-delivery interval ≤ 4 weeks was Salomon AC (AUC, 0.856), ≤ 6 weeks was Hadlock AC (AUC, 0.824) and ≤ 10 weeks was Salomon AC

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

PubMed

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

2015-11-04

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

The influence of CS-US interval on several different indices of learning in appetitive conditioning

PubMed Central

Delamater, Andrew R.; Holland, Peter C.

2010-01-01

Four experiments examined the effects of varying the CS-US interval (and US density) on learning in an appetitive magazine approach task with rats. Learning was assessed with conditioned response (CR) measures, as well as measures of sensory-specific stimulus-outcome associations (Pavlovian-instrumental transfer, potentiated feeding, and US devaluation). The results from these studies indicate that there exists an inverse relation between CS-US interval and magazine approach CRs, but that sensory-specific stimulus-outcome associations are established over a wide range of relatively long, but not short, CS-US intervals. These data suggest that simple CR measures provide us with different information about what is learned than measures of the specific stimulus-outcome association, and that time is a more critical variable for the former than latter component of learning. PMID:18426304

Predicting Driver Behavior during the Yellow Interval Using Video Surveillance

PubMed Central

Li, Juan; Jia, Xudong; Shao, Chunfu

2016-01-01

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

Predicting Driver Behavior during the Yellow Interval Using Video Surveillance.

PubMed

Li, Juan; Jia, Xudong; Shao, Chunfu

2016-12-06

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

Evaluative conditioning increases with temporal contiguity. The influence of stimulus order and stimulus interval on evaluative conditioning.

PubMed

Gast, Anne; Langer, Sebastian; Sengewald, Marie-Ann

2016-10-01

Evaluative conditioning (EC) is a change in valence that is due to pairing a conditioned stimulus (CS) with another, typically valent, unconditioned stimulus (US). This paper investigates how basic presentation parameters moderate EC effects. In two studies we tested the effectiveness of different temporal relations of the CS and the US, that is, the order in which the stimuli were presented and the temporal distance between them. Both studies showed that the size of EC effects was independent of the presentation order of CS and US within a stimulus pair. Contrary to classical conditioning effects, EC effects are thus not most pronounced after CS-first presentations. Furthermore, as shown in Experiment 2, EC effects increased in magnitude as the temporal interval between CS and US presentations decreased. Experiment 1 showed largest EC effects in the condition with simultaneous presentations - which can be seen as the condition with the temporally closest presentation. In this Experiment stimuli were presented in two different modalities, which might have facilitated simultaneous processing. In Experiment 2, in which all stimuli were presented visually, this advantage of simultaneous presentation was not found. We discuss practical and theoretical implications of our findings. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction of Malaysian monthly GDP

NASA Astrophysics Data System (ADS)

Hin, Pooi Ah; Ching, Soo Huei; Yeing, Pan Wei

2015-12-01

The paper attempts to use a method based on multivariate power-normal distribution to predict the Malaysian Gross Domestic Product next month. Letting r(t) be the vector consisting of the month-t values on m selected macroeconomic variables, and GDP, we model the month-(t+1) GDP to be dependent on the present and l-1 past values r(t), r(t-1),…,r(t-l+1) via a conditional distribution which is derived from a [(m+1)l+1]-dimensional power-normal distribution. The 100(α/2)% and 100(1-α/2)% points of the conditional distribution may be used to form an out-of sample prediction interval. This interval together with the mean of the conditional distribution may be used to predict the month-(t+1) GDP. The mean absolute percentage error (MAPE), estimated coverage probability and average length of the prediction interval are used as the criterions for selecting the suitable lag value l-1 and the subset from a pool of 17 macroeconomic variables. It is found that the relatively better models would be those of which 2 ≤ l ≤ 3, and involving one or two of the macroeconomic variables given by Market Indicative Yield, Oil Prices, Exchange Rate and Import Trade.

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. Copyright © 2016 John Wiley & Sons, Ltd.

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

PubMed

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

2016-12-15

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

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

PubMed Central

2013-01-01

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

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.

Weighted regression analysis and interval estimators

Treesearch

Donald W. Seegrist

1974-01-01

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

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

PubMed

Waynforth, David

2015-10-01

Human birth interval length is indicative of the level of parental investment that a child will receive: a short interval following birth means that parental resources must be split with a younger sibling during a period when the older sibling remains highly dependent on their parents. From a life-history theoretical perspective, it is likely that there are evolved mechanisms that serve to maximize fitness depending on context. One context that would be expected to result in short birth intervals, and lowered parental investment, is after a child with low expected fitness is born. Here, data drawn from a longitudinal British birth cohort study were used to test whether birth intervals were shorter following the birth of a child with a long-term health problem. Data on the timing of 4543 births were analysed using discrete-time event history analysis. The results were consistent with the hypothesis: birth intervals were shorter following the birth of a child diagnosed by a medical professional with a severe but non-fatal medical condition. Covariates in the analysis were also significantly associated with birth interval length: births of twins or multiple births, and relationship break-up were associated with significantly longer birth intervals. © 2015 The Author(s).

Tp-Te interval predicts heart rate reduction after fingolimod administration in patients with multiple sclerosis.

PubMed

Tocci, Giuliano; Giuliani, Manuela; Canichella, Flaminia; Timpano, Jacopo; Presta, Vivianne; Francia, Pietro; Musumeci, Maria Beatrice; Fubelli, Federica; Pozzilli, Carlo; Volpe, Massimo; Ferrucci, Andrea

2016-10-15

FTY720 (Fingolimod) is an immunosuppressive drug, which provides favourable effects in patients with multiple sclerosis (MS), albeit it induces heart rate (HR) and blood pressure (BP) reductions. Therefore, we tested potential factors able to predict HR response in MS patients treated with fingolimod. We analysed patients with MS followed at our Neurology Outpatient Clinic from May 2013 to June 2015. All patients underwent BP measurements and 12-lead ECG before and 6-h after drug administration. At these time intervals, conventional and new ECG indexes for cardiac damage, including Tp-Te interval, were measured. Univariate and multivariate analyses were performed to test the outcome of HR reduction more than median difference between baseline and final observations. 69 outpatients with MS (46 males, age 35.1±9.4years, BP 119.0±12.7/73.0±9.3mmHg, HR 73.5±11.4bpm) were included. No relevant adverse reactions were reported. Fingolimod induced progressive systolic (P=0.024) and diastolic (P<0.001) BP, as well as HR (P<0.001) reductions compared to baseline. Prolonged PQ (150.4±19.5 vs. 157.0±19.5ms; P<0.001), QT (374.9±27.0 vs. 400.0±25.8ms; P<0.001), Tp-Te (1.8±0.3 vs. 1.9±0.3mm; P=0.021), and reduced QTc (414.4±24.4 vs. 404.5±24.5ms; P<0.001) intervals were also recorded at final observation. Baseline HR, QT and Tp-Te intervals provided prognostic information at univariate analysis, although Tp-Te interval resulted the best independent predictor for HR reduction at multivariate analysis [0.057 (0.005-0.660); P=0.022]. This study firstly demonstrates that prolonged Tp-Te interval may identify those MS patients treated with fingolimod at higher risk of having significant, asymptomatic HR reduction during clinical observation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Sequential Processing and the Matching-Stimulus Interval Effect in ERP Components: An Exploration of the Mechanism Using Multiple Regression

PubMed Central

Steiner, Genevieve Z.; Barry, Robert J.; Gonsalvez, Craig J.

2016-01-01

In oddball tasks, increasing the time between stimuli within a particular condition (target-to-target interval, TTI; nontarget-to-nontarget interval, NNI) systematically enhances N1, P2, and P300 event-related potential (ERP) component amplitudes. This study examined the mechanism underpinning these effects in ERP components recorded from 28 adults who completed a conventional three-tone oddball task. Bivariate correlations, partial correlations and multiple regression explored component changes due to preceding ERP component amplitudes and intervals found within the stimulus series, rather than constraining the task with experimentally constructed intervals, which has been adequately explored in prior studies. Multiple regression showed that for targets, N1 and TTI predicted N2, TTI predicted P3a and P3b, and Processing Negativity (PN), P3b, and TTI predicted reaction time. For rare nontargets, P1 predicted N1, NNI predicted N2, and N1 predicted Slow Wave (SW). Findings show that the mechanism is operating on separate stages of stimulus-processing, suggestive of either increased activation within a number of stimulus-specific pathways, or very long component generator recovery cycles. These results demonstrate the extent to which matching-stimulus intervals influence ERP component amplitudes and behavior in a three-tone oddball task, and should be taken into account when designing similar studies. PMID:27445774

Sequential Processing and the Matching-Stimulus Interval Effect in ERP Components: An Exploration of the Mechanism Using Multiple Regression.

PubMed

Steiner, Genevieve Z; Barry, Robert J; Gonsalvez, Craig J

2016-01-01

In oddball tasks, increasing the time between stimuli within a particular condition (target-to-target interval, TTI; nontarget-to-nontarget interval, NNI) systematically enhances N1, P2, and P300 event-related potential (ERP) component amplitudes. This study examined the mechanism underpinning these effects in ERP components recorded from 28 adults who completed a conventional three-tone oddball task. Bivariate correlations, partial correlations and multiple regression explored component changes due to preceding ERP component amplitudes and intervals found within the stimulus series, rather than constraining the task with experimentally constructed intervals, which has been adequately explored in prior studies. Multiple regression showed that for targets, N1 and TTI predicted N2, TTI predicted P3a and P3b, and Processing Negativity (PN), P3b, and TTI predicted reaction time. For rare nontargets, P1 predicted N1, NNI predicted N2, and N1 predicted Slow Wave (SW). Findings show that the mechanism is operating on separate stages of stimulus-processing, suggestive of either increased activation within a number of stimulus-specific pathways, or very long component generator recovery cycles. These results demonstrate the extent to which matching-stimulus intervals influence ERP component amplitudes and behavior in a three-tone oddball task, and should be taken into account when designing similar studies.

[The use of complex interval models for predicting activity of non-nucleoside reverse transcriptase activity].

PubMed

Burliaeva, E V; Tarkhov, A E; Burliaev, V V; Iurkevich, A M; Shvets, V I

2002-01-01

Searching of new anti-HIV agents is still crucial now. In general, researches are looking for inhibitors of certain HIV's vital enzymes, especially for reverse transcriptase (RT) inhibitors. Modern generation of anti-HIV agents represents non-nucleoside reverse transcriptase inhibitors (NNRTIs). They are much less toxic than nucleoside analogues and more chemically stable, thus being slower metabolized and emitted from the human body. Thus, search of new NNRTIs is actual today. Synthesis and study of new anti-HIV drugs is very expensive. So employment of the activity prediction techniques for such a search is very beneficial. This technique allows predicting the activities for newly proposed structures. It is based on the property model built by investigation of a series of known compounds with measured activity. This paper presents an approach of activity prediction based on "structure-activity" models designed to form a hypothesis about probably activity interval estimate. This hypothesis formed is based on structure descriptor domains, calculated for all energetically allowed conformers for each compound in the studied sef. Tetrahydroimidazobenzodiazipenone (TIBO) derivatives and phenylethyltiazolyltiourea (PETT) derivatives illustrated the predictive power of this method. The results are consistent with experimental data and allow to predict inhibitory activity of compounds, which were not included into the training set.

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

PubMed

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

2017-03-01

This study aimed to develop an aquatic decomposition scoring (ADS) method and investigated the predictive value of this method in estimating the postmortem submersion interval (PMSI) of bodies recovered from the North Sea. This method, consisting of an ADS item list and a pictorial reference atlas, showed a high interobserver agreement (Krippendorff's alpha ≥ 0.93) and hence proved to be valid. This scoring method was applied to data, collected from closed cases-cases in which the postmortal submersion interval (PMSI) was known-concerning bodies recovered from the North Sea from 1990 to 2013. Thirty-eight cases met the inclusion criteria and were scored by quantifying the observed total aquatic decomposition score (TADS). Statistical analysis demonstrated that TADS accurately predicts the PMSI (p < 0.001), confirming that the decomposition process in the North Sea is strongly correlated to time. © 2017 American Academy of Forensic Sciences.

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 (c) 2016 APA, all rights reserved).

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

PubMed Central

Doré, B.P.; Meksin, R.; Mather, M.; Hirst, W.; Ochsner, K.N

2016-01-01

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 1) the overall intensity of their future negative emotion, and 2) 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. PMID:27100309

Prediction of future asset prices

NASA Astrophysics Data System (ADS)

Seong, Ng Yew; Hin, Pooi Ah; Ching, Soo Huei

2014-12-01

This paper attempts to incorporate trading volumes as an additional predictor for predicting asset prices. Denoting r(t) as the vector consisting of the time-t values of the trading volume and price of a given asset, we model the time-(t+1) asset price to be dependent on the present and l-1 past values r(t), r(t-1), ....., r(t-1+1) via a conditional distribution which is derived from a (2l+1)-dimensional power-normal distribution. A prediction interval based on the 100(α/2)% and 100(1-α/2)% points of the conditional distribution is then obtained. By examining the average lengths of the prediction intervals found by using the composite indices of the Malaysia stock market for the period 2008 to 2013, we found that the value 2 appears to be a good choice for l. With the omission of the trading volume in the vector r(t), the corresponding prediction interval exhibits a slightly longer average length, showing that it might be desirable to keep trading volume as a predictor. From the above conditional distribution, the probability that the time-(t+1) asset price will be larger than the time-t asset price is next computed. When the probability differs from 0 (or 1) by less than 0.03, the observed time-(t+1) increase in price tends to be negative (or positive). Thus the above probability has a good potential of being used as a market indicator in technical analysis.

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.

How Children Use Examples to Make Conditional Predictions

ERIC Educational Resources Information Center

Kalish, Charles W.

2010-01-01

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

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…

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

PubMed Central

Grover, Anita; Benet, Leslie Z.

2013-01-01

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

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

PubMed

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

2015-12-01

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

Functional plasticity in the interposito-thalamo-cortical pathway during conditioning. Role of the interstimulus interval.

PubMed

Pananceau, M; Rispal-Padel, L

2000-06-01

In classic conditioning, the interstimulus interval (ISI) between the conditioned (CS) and unconditioned (US) stimulus is a critical parameter. The aim of the present experiment was to assess whether, during conditioning, modification of the CS-US interval could reliably produce changes in the functional properties of the interposito-thalamo-cortical pathways (INTCps). Five cats were prepared for chronic stimulation and recording from several brain regions along this pathway in awake animals. The CS was a weak electric shock applied on the interposed nucleus of the cerebellum in sites that initially elicited forelimb flexion (i.e., alpha motor responses) in three cats, and equal proportions of flexor and extensor responses in two cats. The US was an electric shock applied on the skin that elicited forelimb flexions. The motor and neurobiological effects of synchronous CS-US were compared with pairings in which the CS was applied 100 ms before US. Simultaneous and sequential application of CS and US produced different behavioral outcomes and resulted in different neural processes in the interposito-thalamo-cortical pathways (INTCps). The simultaneous presentation of stimuli only produced a small increase in excitability spreading to all the body representational zones of the primary motor cortex and a weak increase in the amplitude of the alpha motor response. In contrast, the sequential application led to a profound modification of the interposed output to neurons in the forelimb representation of the motor cortex. These robust neuronal correlates of conditioning were accompanied by a large facilitation of the alpha motor response (alpha-MR). There were also changes in the direction of misdirected alpha responses and an emergence of functionally appropriate, long-latency withdrawal forelimb flexion. These data revealed that, during conditioning, plastic changes within the thalamocortical connections are selectively induced by sequential information from central and

Setting the Revisit Interval in Primary Care

PubMed Central

Schwartz, Lisa M; Woloshin, Steven; Wasson, John H; Renfrew, Roger A; Welch, H Gilbert

1999-01-01

OBJECTIVE Although longitudinal care constitutes the bulk of primary care, physicians receive little guidance on the fundamental question of how to time follow-up visits. We sought to identify important predictors of the revisit interval and to describe the variability in how physicians set these intervals when caring for patients with common medical conditions. DESIGN Cross-sectional survey of physicians performed at the end of office visits for consecutive patients with hypertension, angina, diabetes, or musculoskeletal pain. PARTICIPANTS/SETTING One hundred sixty-four patients under the care of 11 primary care physicians in the Dartmouth Primary Care Cooperative Research Network. MEASUREMENTS The main outcome measures were the variability in mean revisit intervals across physicians and the proportion of explained variance by potential determinants of revisit intervals. We assessed the relation between the revisit interval (dependent variable) and three groups of independent variables, patient characteristics (e.g., age, physician perception of patient health), identification of individual physician, and physician characterization of the visit (e.g., routine visit, visit requiring a change in management, or visit occurring on a “hectic” day), using multiple regression that accounted for the natural grouping of patients within physician. MAIN RESULTS Revisit intervals ranged from 1 week to over 1 year. The most common intervals were 12 and 16 weeks. Physicians’ perception of fair-poor health status and visits involving a change in management were most strongly related to shorter revisit intervals. In multivariate analyses, patient characteristics explained about 18% of the variance in revisit intervals, and adding identification of the individual provider doubled the explained variance to about 40%. Physician characterization of the visit increased explained variance to 57%. The average revisit interval adjusted for patient characteristics for each of the 11

Modeling stream fish distributions using interval-censored detection times.

PubMed

Ferreira, Mário; Filipe, Ana Filipa; Bardos, David C; Magalhães, Maria Filomena; Beja, Pedro

2016-08-01

Controlling for imperfect detection is important for developing species distribution models (SDMs). Occupancy-detection models based on the time needed to detect a species can be used to address this problem, but this is hindered when times to detection are not known precisely. Here, we extend the time-to-detection model to deal with detections recorded in time intervals and illustrate the method using a case study on stream fish distribution modeling. We collected electrofishing samples of six fish species across a Mediterranean watershed in Northeast Portugal. Based on a Bayesian hierarchical framework, we modeled the probability of water presence in stream channels, and the probability of species occupancy conditional on water presence, in relation to environmental and spatial variables. We also modeled time-to-first detection conditional on occupancy in relation to local factors, using modified interval-censored exponential survival models. Posterior distributions of occupancy probabilities derived from the models were used to produce species distribution maps. Simulations indicated that the modified time-to-detection model provided unbiased parameter estimates despite interval-censoring. There was a tendency for spatial variation in detection rates to be primarily influenced by depth and, to a lesser extent, stream width. Species occupancies were consistently affected by stream order, elevation, and annual precipitation. Bayesian P-values and AUCs indicated that all models had adequate fit and high discrimination ability, respectively. Mapping of predicted occupancy probabilities showed widespread distribution by most species, but uncertainty was generally higher in tributaries and upper reaches. The interval-censored time-to-detection model provides a practical solution to model occupancy-detection when detections are recorded in time intervals. This modeling framework is useful for developing SDMs while controlling for variation in detection rates, as it

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

PubMed Central

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

2011-01-01

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

The predictive value of C-reactive protein (CRP) in acute pancreatitis - is interval change in CRP an additional indicator of severity?

PubMed

Stirling, Aaron D; Moran, Neil R; Kelly, Michael E; Ridgway, Paul F; Conlon, Kevin C

2017-10-01

Using revised Atlanta classification defined outcomes, we compare absolute values in C-reactive protein (CRP), with interval changes in CRP, for severity stratification in acute pancreatitis (AP). A retrospective study of all first incidence AP was conducted over a 5-year period. Interval change in CRP values from admission to day 1, 2 and 3 was compared against the absolute values. Receiver-operator characteristic (ROC) curve and likelihood ratios (LRs) were used to compare ability to predict severe and mild disease. 337 cases of first incidence AP were included in our analysis. ROC curve analysis demonstrated the second day as the most useful time for repeat CRP measurement. A CRP interval change >90 mg/dL at 48 h (+LR 2.15, -LR 0.26) was equivalent to an absolute value of >150 mg/dL within 48 h (+LR 2.32, -LR 0.25). The optimal cut-off for absolute CRP based on new, more stringent definition of severity was >190 mg/dL (+LR 2.72, -LR 0.24). Interval change in CRP is a comparable measure to absolute CRP in the prognostication of AP severity. This study suggests a rise of >90 mg/dL from admission or an absolute value of >190 mg/dL at 48 h predicts severe disease with the greatest accuracy. Copyright © 2017 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

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.

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. Copyright © 2015 Elsevier B.V. All rights reserved.

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

PubMed Central

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

2015-01-01

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

Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series

PubMed Central

Liu, Datong; Peng, Yu; Peng, Xiyuan

2018-01-01

Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector machine (RVM)) are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP), which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%). There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI) based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA) algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application. PMID:29587372

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

PubMed

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

2017-01-01

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

Effects of Training and Feedback on Accuracy of Predicting Rectosigmoid Neoplastic Lesions and Selection of Surveillance Intervals by Endoscopists Performing Optical Diagnosis of Diminutive Polyps.

PubMed

Vleugels, Jasper L A; Dijkgraaf, Marcel G W; Hazewinkel, Yark; Wanders, Linda K; Fockens, Paul; Dekker, Evelien

2018-05-01

Real-time differentiation of diminutive polyps (1-5 mm) during endoscopy could replace histopathology analysis. According to guidelines, implementation of optical diagnosis into routine practice would require it to identify rectosigmoid neoplastic lesions with a negative predictive value (NPV) of more than 90%, using histologic findings as a reference, and agreement with histology-based surveillance intervals for more than 90% of cases. We performed a prospective study with 39 endoscopists accredited to perform colonoscopies on participants with positive results from fecal immunochemical tests in the Bowel Cancer Screening Program at 13 centers in the Netherlands. Endoscopists were trained in optical diagnosis using a validated module (Workgroup serrAted polypS and Polyposis). After meeting predefined performance thresholds in the training program, the endoscopists started a 1-year program (continuation phase) in which they performed narrow band imaging analyses during colonoscopies of participants in the screening program and predicted histological findings with confidence levels. The endoscopists were randomly assigned to groups that received feedback or no feedback on the accuracy of their predictions. Primary outcome measures were endoscopists' abilities to identify rectosigmoid neoplastic lesions (using histology as a reference) with NPVs of 90% or more, and selecting surveillance intervals that agreed with those determined by histology for at least 90% of cases. Of 39 endoscopists initially trained, 27 (69%) completed the training program. During the continuation phase, these 27 endoscopists performed 3144 colonoscopies in which 4504 diminutive polyps were removed. The endoscopists identified neoplastic lesions with a pooled NPV of 90.8% (95% confidence interval 88.6-92.6); their proposed surveillance intervals agreed with those determined by histologic analysis for 95.4% of cases (95% confidence interval 94.0-96.6). Findings did not differ between the group

Ventricular Cycle Length Characteristics Estimative of Prolonged RR Interval during Atrial Fibrillation

PubMed Central

CIACCIO, EDWARD J.; BIVIANO, ANGELO B.; GAMBHIR, ALOK; EINSTEIN, ANDREW J.; GARAN, HASAN

2014-01-01

Background When atrial fibrillation (AF) is incessant, imaging during a prolonged ventricular RR interval may improve image quality. It was hypothesized that long RR intervals could be predicted from preceding RR values. Methods From the PhysioNet database, electrocardiogram RR intervals were obtained from 74 persistent AF patients. An RR interval lengthened by at least 250 ms beyond the immediately preceding RR interval (termed T0 and T1, respectively) was considered prolonged. A two-parameter scatterplot was used to predict the occurrence of a prolonged interval T0. The scatterplot parameters were: (1) RR variability (RRv) estimated as the average second derivative from 10 previous pairs of RR differences, T13–T2, and (2) Tm–T1, the difference between Tm, the mean from T13 to T2, and T1. For each patient, scatterplots were constructed using preliminary data from the first hour. The ranges of parameters 1 and 2 were adjusted to maximize the proportion of prolonged RR intervals within range. These constraints were used for prediction of prolonged RR in test data collected during the second hour. Results The mean prolonged event was 1.0 seconds in duration. Actual prolonged events were identified with a mean positive predictive value (PPV) of 80% in the test set. PPV was >80% in 36 of 74 patients. An average of 10.8 prolonged RR intervals per 60 minutes was correctly identified. Conclusions A method was developed to predict prolonged RR intervals using two parameters and prior statistical sampling for each patient. This or similar methodology may help improve cardiac imaging in many longstanding persistent AF patients. PMID:23998759

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input

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.

Novel transformation-based response prediction of shear building using interval neural network

NASA Astrophysics Data System (ADS)

Chakraverty, S.; Sahoo, Deepti Moyi

2017-04-01

Present paper uses powerful technique of interval neural network (INN) to simulate and estimate structural response of multi-storey shear buildings subject to earthquake motion. The INN is first trained for a real earthquake data, viz., the ground acceleration as input and the numerically generated responses of different floors of multi-storey buildings as output. Till date, no model exists to handle positive and negative data in the INN. As such here, the bipolar data in [ -1, 1] are converted first to unipolar form, i.e., to [0, 1] by means of a novel transformation for the first time to handle the above training patterns in normalized form. Once the training is done, again the unipolar data are converted back to its bipolar form by using the inverse transformation. The trained INN architecture is then used to simulate and test the structural response of different floors for various intensity earthquake data and it is found that the predicted responses given by INN model are good for practical purposes.

Application of Interval Predictor Models to Space Radiation Shielding

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy,Daniel P.; Norman, Ryan B.; Blattnig, Steve R.

2016-01-01

This paper develops techniques for predicting the uncertainty range of an output variable given input-output data. These models are called Interval Predictor Models (IPM) because they yield an interval valued function of the input. This paper develops IPMs having a radial basis structure. This structure enables the formal description of (i) the uncertainty in the models parameters, (ii) the predicted output interval, and (iii) the probability that a future observation would fall in such an interval. In contrast to other metamodeling techniques, this probabilistic certi cate of correctness does not require making any assumptions on the structure of the mechanism from which data are drawn. Optimization-based strategies for calculating IPMs having minimal spread while containing all the data are developed. Constraints for bounding the minimum interval spread over the continuum of inputs, regulating the IPMs variation/oscillation, and centering its spread about a target point, are used to prevent data over tting. Furthermore, we develop an approach for using expert opinion during extrapolation. This metamodeling technique is illustrated using a radiation shielding application for space exploration. In this application, we use IPMs to describe the error incurred in predicting the ux of particles resulting from the interaction between a high-energy incident beam and a target.

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.

The genetic relationship between commencement of luteal activity and calving interval, body condition score, production, and linear type traits in Holstein-Friesian dairy cattle.

PubMed

Royal, M D; Pryce, J E; Woolliams, J A; Flint, A P F

2002-11-01

The decline of fertility in the UK dairy herd and the unfavorable genetic correlation (r(a)) between fertility and milk yield has necessitated the broadening of breeding goals to include fertility. The coefficient of genetic variation present in fertility is of similar magnitude to that present in production traits; however, traditional measurements of fertility (such as calving interval, days open, nonreturn rate) have low heritability (h2 < 0.05), and recording is often poor, hindering identification of genetically superior animals. An alternative approach is to use endocrine measurements of fertility such as interval to commencement of luteal activity postpartum (CLA), which has a higher h2 (0.16 to 0.23) and is free from management bias. Although CLA has favorable phenotypic correlations with traditional measures of fertility, if it is to be used in a selection index, the genetic correlation (ra) of this trait with fertility and other components of the index must be estimated. The aim of the analyses reported here was to obtain information on the ra between lnCLA and calving interval (CI), average body condition score (BCS; one to nine, an indicator of energy balance estimated from records taken at different months of lactation), production and a number of linear type traits. Genetic models were fitted using ASREML, and r(a) were inferred from genetic regression of lnCLA on sire-predicted transmitting abilities (PTA) for the trait concerned by multiplying the regression coefficient (b) by the ratio of the genetic standard deviations. The inferred r(a) between lnCLA and CI and average BCS were 0.36 and -0.84, respectively. Genetic correlations between InCLA and milk fat and protein yields were all positive and ranged between 0.33 and 0.69. Genetic correlations between InCLA and linear type traits reflecting body structure ranged from -0.25 to 0.15, and between udder characteristics they ranged from -0.16 to 0.05. Thus, incorporation of endocrine parameters of

A model of interval timing by neural integration.

PubMed

Simen, Patrick; Balci, Fuat; de Souza, Laura; Cohen, Jonathan D; Holmes, Philip

2011-06-22

We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes, that correlations among them can be largely cancelled by balancing excitation and inhibition, that neural populations can act as integrators, and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys, and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule's predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior.

A model of interval timing by neural integration

PubMed Central

Simen, Patrick; Balci, Fuat; deSouza, Laura; Cohen, Jonathan D.; Holmes, Philip

2011-01-01

We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes; that correlations among them can be largely cancelled by balancing excitation and inhibition; that neural populations can act as integrators; and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule’s predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior. PMID:21697374

Odor-context effects in free recall after a short retention interval: a new methodology for controlling adaptation.

PubMed

Isarida, Takeo; Sakai, Tetsuya; Kubota, Takayuki; Koga, Miho; Katayama, Yu; Isarida, Toshiko K

2014-04-01

The present study investigated context effects of incidental odors in free recall after a short retention interval (5 min). With a short retention interval, the results are not confounded by extraneous odors or encounters with the experimental odor and possible rehearsal during a long retention interval. A short study time condition (4 s per item), predicted not to be affected by adaptation to the odor, and a long study time condition (8 s per item) were used. Additionally, we introduced a new method for recovery from adaptation, where a dissimilar odor was briefly presented at the beginning of the retention interval, and we demonstrated the effectiveness of this technique. An incidental learning paradigm was used to prevent overshadowing from confounding the results. In three experiments, undergraduates (N = 200) incidentally studied words presented one-by-one and received a free recall test. Two pairs of odors and a third odor having different semantic-differential characteristics were selected from 14 familiar odors. One of the odors was presented during encoding, and during the test, the same odor (same-context condition) or the other odor within the pair (different-context condition) was presented. Without using a recovery-from-adaptation method, a significant odor-context effect appeared in the 4-s/item condition, but not in the 8-s/item condition. Using the recovery-from-adaptation method, context effects were found for both the 8- and the 4-s/item conditions. The size of the recovered odor-context effect did not change with study time. There were no serial position effects. Implications of the present findings are discussed.

Retention interval affects visual short-term memory encoding.

PubMed

Bankó, Eva M; Vidnyánszky, Zoltán

2010-03-01

Humans can efficiently store fine-detailed facial emotional information in visual short-term memory for several seconds. However, an unresolved question is whether the same neural mechanisms underlie high-fidelity short-term memory for emotional expressions at different retention intervals. Here we show that retention interval affects the neural processes of short-term memory encoding using a delayed facial emotion discrimination task. The early sensory P100 component of the event-related potentials (ERP) was larger in the 1-s interstimulus interval (ISI) condition than in the 6-s ISI condition, whereas the face-specific N170 component was larger in the longer ISI condition. Furthermore, the memory-related late P3b component of the ERP responses was also modulated by retention interval: it was reduced in the 1-s ISI as compared with the 6-s condition. The present findings cannot be explained based on differences in sensory processing demands or overall task difficulty because there was no difference in the stimulus information and subjects' performance between the two different ISI conditions. These results reveal that encoding processes underlying high-precision short-term memory for facial emotional expressions are modulated depending on whether information has to be stored for one or for several seconds.

Quantifying uncertainty on sediment loads using bootstrap confidence intervals

NASA Astrophysics Data System (ADS)

Slaets, Johanna I. F.; Piepho, Hans-Peter; Schmitter, Petra; Hilger, Thomas; Cadisch, Georg

2017-01-01

Load estimates are more informative than constituent concentrations alone, as they allow quantification of on- and off-site impacts of environmental processes concerning pollutants, nutrients and sediment, such as soil fertility loss, reservoir sedimentation and irrigation channel siltation. While statistical models used to predict constituent concentrations have been developed considerably over the last few years, measures of uncertainty on constituent loads are rarely reported. Loads are the product of two predictions, constituent concentration and discharge, integrated over a time period, which does not make it straightforward to produce a standard error or a confidence interval. In this paper, a linear mixed model is used to estimate sediment concentrations. A bootstrap method is then developed that accounts for the uncertainty in the concentration and discharge predictions, allowing temporal correlation in the constituent data, and can be used when data transformations are required. The method was tested for a small watershed in Northwest Vietnam for the period 2010-2011. The results showed that confidence intervals were asymmetric, with the highest uncertainty in the upper limit, and that a load of 6262 Mg year-1 had a 95 % confidence interval of (4331, 12 267) in 2010 and a load of 5543 Mg an interval of (3593, 8975) in 2011. Additionally, the approach demonstrated that direct estimates from the data were biased downwards compared to bootstrap median estimates. These results imply that constituent loads predicted from regression-type water quality models could frequently be underestimating sediment yields and their environmental impact.

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

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.

Fixed-interval performance and self-control in children.

PubMed Central

Darcheville, J C; Rivière, V; Wearden, J H

1992-01-01

Operant responses of 16 children (mean age 6 years and 1 month) were reinforced according to different fixed-interval schedules (with interreinforcer intervals of 20, 30, or 40 s) in which the reinforcers were either 20-s or 40-s presentations of a cartoon. In another procedure, they received training on a self-control paradigm in which both reinforcer delay (0.5 s or 40 s) and reinforcer duration (20 s or 40 s of cartoons) varied, and subjects were offered a choice between various combinations of delay and duration. Individual differences in behavior under the self-control procedure were precisely mirrored by individual differences under the fixed-interval schedule. Children who chose the smaller immediate reinforcer on the self-control procedure (impulsive) produced short postreinforcement pauses and high response rates in the fixed-interval conditions, and both measures changed little with changes in fixed-interval value. Conversely, children who chose the larger delayed reinforcer in the self-control condition (the self-controlled subjects) exhibited lower response rates and long postreinforcement pauses, which changed systematically with changes in the interval, in their fixed-interval performances. PMID:1573372

Temporal-difference prediction errors and Pavlovian fear conditioning: role of NMDA and opioid receptors.

PubMed

Cole, Sindy; McNally, Gavan P

2007-10-01

Three experiments studied temporal-difference (TD) prediction errors during Pavlovian fear conditioning. In Stage I, rats received conditioned stimulus A (CSA) paired with shock. In Stage II, they received pairings of CSA and CSB with shock that blocked learning to CSB. In Stage III, a serial overlapping compound, CSB --> CSA, was followed by shock. The change in intratrial durations supported fear learning to CSB but reduced fear of CSA, revealing the operation of TD prediction errors. N-methyl- D-aspartate (NMDA) receptor antagonism prior to Stage III prevented learning, whereas opioid receptor antagonism selectively affected predictive learning. These findings support a role for TD prediction errors in fear conditioning. They suggest that NMDA receptors contribute to fear learning by acting on the product of predictive error, whereas opioid receptors contribute to predictive error. (PsycINFO Database Record (c) 2007 APA, all rights reserved).

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

PubMed

Klopčič, M; Koops, W J; Kuipers, A

2013-09-01

The milk production of a dairy cow is characterized by lactation production, which is calculated from daily milk yields (DMY) during lactation. The DMY is calculated from one or more milkings a day collected at the farm. Various milking systems are in use today, resulting in one or many recorded milk yields a day, from which different calculations are used to determine DMY. The primary objective of this study was to develop a mathematical function that described milk production of a dairy cow in relation to the interval between 2 milkings. The function was partly based on the biology of the milk production process. This function, called the 3K-function, was able to predict milk production over an interval of 12h, so DMY was twice this estimate. No external information is needed to incorporate this function in methods to predict DMY. Application of the function on data from different milking systems showed a good fit. This function could be a universal tool to predict DMY for a variety of milking systems, and it seems especially useful for data from robotic milking systems. Further study is needed to evaluate the function under a wide range of circumstances, and to see how it can be incorporated in existing milk recording systems. A secondary objective of using the 3K-function was to compare how much DMY based on different milking systems differed from that based on a twice-a-day milking. Differences were consistent with findings in the literature. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

An actual load forecasting methodology by interval grey modeling based on the fractional calculus.

PubMed

Yang, Yang; Xue, Dingyü

2017-07-17

The operation processes for thermal power plant are measured by the real-time data, and a large number of historical interval data can be obtained from the dataset. Within defined periods of time, the interval information could provide important information for decision making and equipment maintenance. Actual load is one of the most important parameters, and the trends hidden in the historical data will show the overall operation status of the equipments. However, based on the interval grey parameter numbers, the modeling and prediction process is more complicated than the one with real numbers. In order not lose any information, the geometric coordinate features are used by the coordinates of area and middle point lines in this paper, which are proved with the same information as the original interval data. The grey prediction model for interval grey number by the fractional-order accumulation calculus is proposed. Compared with integer-order model, the proposed method could have more freedom with better performance for modeling and prediction, which can be widely used in the modeling process and prediction for the small amount interval historical industry sequence samples. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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…

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

PubMed

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

2006-04-01

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

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.

Single-channel autocorrelation functions: the effects of time interval omission.

PubMed Central

Ball, F G; Sansom, M S

1988-01-01

We present a general mathematical framework for analyzing the dynamic aspects of single channel kinetics incorporating time interval omission. An algorithm for computing model autocorrelation functions, incorporating time interval omission, is described. We show, under quite general conditions, that the form of these autocorrelations is identical to that which would be obtained if time interval omission was absent. We also show, again under quite general conditions, that zero correlations are necessarily a consequence of the underlying gating mechanism and not an artefact of time interval omission. The theory is illustrated by a numerical study of an allosteric model for the gating mechanism of the locust muscle glutamate receptor-channel. PMID:2455553

Aural-Nondetectability Model Predictions for Night-Vision Goggles across Ambient Lighting Conditions

DTIC Science & Technology

2015-12-01

ARL-TR-7564 ● DEC 2015 US Army Research Laboratory Aural-Nondetectability Model Predictions for Night -Vision Goggles across...ARL-TR-7564 ● DEC 2015 US Army Research Laboratory Aural-Nondetectability Model Predictions for Night -Vision Goggles across Ambient...May 2015–30 Sep 2015 4. TITLE AND SUBTITLE Aural-Nondetectability Model Predictions for Night -Vision Goggles across Ambient Lighting Conditions 5a

Context conditioning and behavioral avoidance in a virtual reality environment: effect of predictability.

PubMed

Grillon, Christian; Baas, Johanna M P; Cornwell, Brian; Johnson, Linda

2006-10-01

Sustained anxiety can be modeled using context conditioning, which can be studied in a virtual reality environment. Unpredictable stressors increase context conditioning in animals. This study examined context conditioning to predictable and unpredictable shocks in humans using behavioral avoidance, potentiated startle, and subjective reports of anxiety. Subjects were guided through three virtual rooms (no-shock, predictable, unpredictable contexts). Eight-sec duration colored lights served as conditioned stimuli (CS). During acquisition, no shock was administered in the no-shock context. Shocks were paired with the CS in the predictable context and were administered randomly in the unpredictable context. No shock was administered during extinction. Startle stimuli were delivered during CS and between CS to assess cued and context conditioning, respectively. To assess avoidance, subjects freely navigated into two of the three contexts to retrieve money. Startle between CS was potentiated in the unpredictable context compared to the two other contexts. Following acquisition, subjects showed a strong preference for the no-shock context and avoidance of the unpredictable context. Consistent with animal data, context conditioning is increased by unpredictability. These data support virtual reality as a tool to extend research on physiological and behavioral signs of fear and anxiety in humans.

Reference intervals and allometric scaling of two-dimensional echocardiographic measurements in 150 healthy cats.

PubMed

Karsten, Schober; Stephanie, Savino; Vedat, Yildiz

2017-11-10

The objective of the study was to evaluate the effects of body weight (BW), breed, and sex on two-dimensional (2D) echocardiographic measures, reference ranges, and prediction intervals using allometrically-scaled data of left atrial (LA) and left ventricular (LV) size and LV wall thickness in healthy cats. Study type was retrospective, observational, and clinical cohort. 150 healthy cats were enrolled and 2D echocardiograms analyzed. LA diameter, LV wall thickness, and LV dimension were quantified using three different imaging views. The effect of BW, breed, sex, age, and interaction (BW*sex) on echocardiographic variables was assessed using univariate and multivariate regression and linear mixed model analysis. Standard (using raw data) and allometrically scaled (Y=a × M b ) reference intervals and prediction intervals were determined. BW had a significant (P<0.05) independent effect on 2D variables whereas breed, sex, and age did not. There were clinically relevant differences between reference intervals using mean ± 2SD of raw data and mean and 95% prediction interval of allometrically-scaled variables, most prominent in larger (>6 kg) and smaller (<3 kg) cats. A clinically relevant difference between thickness of the interventricular septum (IVS) and dimension of the LV posterior wall (LVPW) was identified. In conclusion, allometric scaling and BW-based 95% prediction intervals should be preferred over conventional 2D echocardiographic reference intervals in cats, in particular in small and large cats. These results are particularly relevant to screening examinations for feline hypertrophic cardiomyopathy.

Assessing predictability of a hydrological stochastic-dynamical system

NASA Astrophysics Data System (ADS)

Gelfan, Alexander

2014-05-01

The water cycle includes the processes with different memory that creates potential for predictability of hydrological system based on separating its long and short memory components and conditioning long-term prediction on slower evolving components (similar to approaches in climate prediction). In the face of the Panta Rhei IAHS Decade questions, it is important to find a conceptual approach to classify hydrological system components with respect to their predictability, define predictable/unpredictable patterns, extend lead-time and improve reliability of hydrological predictions based on the predictable patterns. Representation of hydrological systems as the dynamical systems subjected to the effect of noise (stochastic-dynamical systems) provides possible tool for such conceptualization. A method has been proposed for assessing predictability of hydrological system caused by its sensitivity to both initial and boundary conditions. The predictability is defined through a procedure of convergence of pre-assigned probabilistic measure (e.g. variance) of the system state to stable value. The time interval of the convergence, that is the time interval during which the system losses memory about its initial state, defines limit of the system predictability. The proposed method was applied to assess predictability of soil moisture dynamics in the Nizhnedevitskaya experimental station (51.516N; 38.383E) located in the agricultural zone of the central European Russia. A stochastic-dynamical model combining a deterministic one-dimensional model of hydrothermal regime of soil with a stochastic model of meteorological inputs was developed. The deterministic model describes processes of coupled heat and moisture transfer through unfrozen/frozen soil and accounts for the influence of phase changes on water flow. The stochastic model produces time series of daily meteorological variables (precipitation, air temperature and humidity), whose statistical properties are similar

Why trace and delay conditioning are sometimes (but not always) hippocampal dependent: A computational model

PubMed Central

Moustafa, Ahmed A.; Wufong, Ella; Servatius, Richard J.; Pang, Kevin C. H.; Gluck, Mark A.; Myers, Catherine E.

2013-01-01

A recurrent-network model provides a unified account of the hippocampal region in mediating the representation of temporal information in classical eyeblink conditioning. Much empirical research is consistent with a general conclusion that delay conditioning (in which the conditioned stimulus CS and unconditioned stimulus US overlap and co-terminate) is independent of the hippocampal system, while trace conditioning (in which the CS terminates before US onset) depends on the hippocampus. However, recent studies show that, under some circumstances, delay conditioning can be hippocampal-dependent and trace conditioning can be spared following hippocampal lesion. Here, we present an extension of our prior trial-level models of hippocampal function and stimulus representation that can explain these findings within a unified framework. Specifically, the current model includes adaptive recurrent collateral connections that aid in the representation of intra-trial temporal information. With this model, as in our prior models, we argue that the hippocampus is not specialized for conditioned response timing, but rather is a general-purpose system that learns to predict the next state of all stimuli given the current state of variables encoded by activity in recurrent collaterals. As such, the model correctly predicts that hippocampal involvement in classical conditioning should be critical not only when there is an intervening trace interval, but also when there is a long delay between CS onset and US onset. Our model simulates empirical data from many variants of classical conditioning, including delay and trace paradigms in which the length of the CS, the inter-stimulus interval, or the trace interval is varied. Finally, we discuss model limitations, future directions, and several novel empirical predictions of this temporal processing model of hippocampal function and learning. PMID:23178699

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.

Recurrence interval analysis of trading volumes

NASA Astrophysics Data System (ADS)

Ren, Fei; Zhou, Wei-Xing

2010-06-01

We study the statistical properties of the recurrence intervals τ between successive trading volumes exceeding a certain threshold q . The recurrence interval analysis is carried out for the 20 liquid Chinese stocks covering a period from January 2000 to May 2009, and two Chinese indices from January 2003 to April 2009. Similar to the recurrence interval distribution of the price returns, the tail of the recurrence interval distribution of the trading volumes follows a power-law scaling, and the results are verified by the goodness-of-fit tests using the Kolmogorov-Smirnov (KS) statistic, the weighted KS statistic and the Cramér-von Mises criterion. The measurements of the conditional probability distribution and the detrended fluctuation function show that both short-term and long-term memory effects exist in the recurrence intervals between trading volumes. We further study the relationship between trading volumes and price returns based on the recurrence interval analysis method. It is found that large trading volumes are more likely to occur following large price returns, and the comovement between trading volumes and price returns is more pronounced for large trading volumes.

Recurrence interval analysis of trading volumes.

PubMed

Ren, Fei; Zhou, Wei-Xing

2010-06-01

We study the statistical properties of the recurrence intervals τ between successive trading volumes exceeding a certain threshold q. The recurrence interval analysis is carried out for the 20 liquid Chinese stocks covering a period from January 2000 to May 2009, and two Chinese indices from January 2003 to April 2009. Similar to the recurrence interval distribution of the price returns, the tail of the recurrence interval distribution of the trading volumes follows a power-law scaling, and the results are verified by the goodness-of-fit tests using the Kolmogorov-Smirnov (KS) statistic, the weighted KS statistic and the Cramér-von Mises criterion. The measurements of the conditional probability distribution and the detrended fluctuation function show that both short-term and long-term memory effects exist in the recurrence intervals between trading volumes. We further study the relationship between trading volumes and price returns based on the recurrence interval analysis method. It is found that large trading volumes are more likely to occur following large price returns, and the comovement between trading volumes and price returns is more pronounced for large trading volumes.

Volatility return intervals analysis of the Japanese market

NASA Astrophysics Data System (ADS)

Jung, W.-S.; Wang, F. Z.; Havlin, S.; Kaizoji, T.; Moon, H.-T.; Stanley, H. E.

2008-03-01

We investigate scaling and memory effects in return intervals between price volatilities above a certain threshold q for the Japanese stock market using daily and intraday data sets. We find that the distribution of return intervals can be approximated by a scaling function that depends only on the ratio between the return interval τ and its mean <τ>. We also find memory effects such that a large (or small) return interval follows a large (or small) interval by investigating the conditional distribution and mean return interval. The results are similar to previous studies of other markets and indicate that similar statistical features appear in different financial markets. We also compare our results between the period before and after the big crash at the end of 1989. We find that scaling and memory effects of the return intervals show similar features although the statistical properties of the returns are different.

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.

Can working memory predict target-to-target interval effects in the P300?

PubMed

Steiner, Genevieve Z; Barry, Robert J; Gonsalvez, Craig J

2013-09-01

It has been suggested that the P300 component of the ERP is an electrophysiological index of memory-updating processes associated with task-relevant stimuli. Component magnitude varies with the time separating target stimuli (target-to-target interval: TTI), with longer TTIs eliciting larger P300 amplitudes. According to the template-update perspective, TTI effects observable in the P300 reflect the updating of stimulus-templates in working memory (WM). The current study explored whether young adults' memory-task ability could predict TTI effects in P300. EEG activity was recorded from 50 university students (aged 18-25 years) while they completed an auditory equiprobable Go/NoGo task with manipulations of TTIs. Participants also completed a CogState® battery and were sorted according to their WM score. ERPs were analysed using a temporal PCA. Two P300 components, P3b and the Slow Wave, were found to linearly increase in amplitude to longer TTIs. This TTI effect differed between groups only for the P3b component: The high WM group showed a steeper increase in P3b amplitude with TTI than the low WM group. These results suggest that TTI effects in P300 are directly related to WM processes. © 2013.

Intertrial interval duration and learning in autistic children.

PubMed Central

Koegel, R L; Dunlap, G; Dyer, K

1980-01-01

This study investigated the influence of intertrial interval duration on the performance of autistic children during teaching situations. The children were taught under the same conditions existing in their regular programs, except that the length of time between trials was systematically manipulated. With both multiple baseline and repeated reversal designs, two lengths of intertrial interval were employed; short intervals with the SD for any given trial presented approximately one second following the reinforcer for the previous trial versus long intervals with the SD presented four or more seconds following the reinforcer for the previous trial. The results showed that: (1) the short intertrial intervals always produced higher levels of correct responding than the long intervals; and (2) there were improving trends in performance and rapid acquisition with the short intertrial intervals, in contrast to minimal or no change with the long intervals. The results are discussed in terms of utilizing information about child and task characteristics in terms of selecting optimal intervals. The data suggest that manipulations made between trials have a large influence on autistic children's learning. PMID:7364701

Usefulness of Electrocardiographic QT Interval to Predict Left Ventricular Diastolic Dysfunction

PubMed Central

Wilcox, Jane E.; Rosenberg, Jonathan; Vallakati, Ajay; Gheorghiade, Mihai; Shah, Sanjiv J.

2013-01-01

Whether a normal electrocardiogram excludes left ventricular (LV) diastolic dysfunction (DD) and whether electrocardiographic parameters are associated with DD is unknown. We therefore sought to investigate the relation between electrocardiographic parameters and DD. We first evaluated 75 consecutive patients referred for echocardiography for clinical suspicion of heart failure (phase 1). Electrocardiography and comprehensive echocardiography were performed on all patients and were analyzed separately in a blinded fashion. Receiver operating characteristic curves and multivariate regression analyses were used to determine which electrocardiographic parameters were most closely associated with DD. Next, we prospectively validated our results in 100 consecutive, unselected patients undergoing echocardiography (phase 2). In phase 1 of our study, the mean age was 59 ± 14 years, 41% were women, 31% had coronary disease, 53% had hypertension, and 25% had diabetes. The mean ejection fraction was 54 ± 15%, and 64% had DD. Of all the electrocardiographic parameters, the QTc interval was most closely associated with DD. QTc was inversely associated with E′ velocity (r = −0.54, p <0.0001), and the area under the receiver operating characteristic curve for QTc as a predictor of DD was 0.82. QTc prolongation was independently associated with reduced E′ velocity (p = 0.021 after adjustment for age, gender, medications, QRS duration, and ejection fraction). In phase 2 of our study QTc was the electrocardiographic parameter most associated with reduced E′ velocity (435 ± 31 vs 419 ± 24 ms; p = 0.004), confirming our phase 1 study findings. In conclusion, QTc prolongation was the electrocardiographic marker most predictive of DD and was independently associated with DD. PMID:21907948

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.

Predicting thermal reference conditions for USA streams and rivers

USGS Publications Warehouse

Hill, Ryan A.; Hawkins, Charles P.; Carlisle, Daren M.

2013-01-01

Temperature is a primary driver of the structure and function of stream ecosystems. However, the lack of stream temperature (ST) data for the vast majority of streams and rivers severely compromises our ability to describe patterns of thermal variation among streams, test hypotheses regarding the effects of temperature on macroecological patterns, and assess the effects of altered STs on ecological resources. Our goal was to develop empirical models that could: 1) quantify the effects of stream and watershed alteration (SWA) on STs, and 2) accurately and precisely predict natural (i.e., reference condition) STs in conterminous USA streams and rivers. We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual ST. To build reference-condition models (RCMs), we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. We first created a set of dirty models (DMs) that related STs to both natural factors (e.g., climate, watershed area, topography) and measures of SWA, i.e., reservoirs, urbanization, and agriculture. The 3 models performed well (r2 = 0.84–0.94, residual mean square error [RMSE] = 1.2–2.0°C). For each DM, we used partial dependence plots to identify SWA thresholds below which response in ST was minimal. We then used data from only the sites with upstream SWA below these thresholds to build RCMs with only natural factors as predictors (r2 = 0.87–0.95, RMSE = 1.1–1.9°C). Use of only reference-quality sites caused RCMs to suffer modest loss of predictor space and spatial coverage, but this loss was associated with parts of ST response curves that were flat and, therefore, not responsive to further variation in predictor space. We then compared predictions made with the RCMs to predictions made with the DMs with SWA set to 0. For most

Interval Predictor Models with a Formal Characterization of Uncertainty and Reliability

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.

2014-01-01

This paper develops techniques for constructing empirical predictor models based on observations. By contrast to standard models, which yield a single predicted output at each value of the model's inputs, Interval Predictors Models (IPM) yield an interval into which the unobserved output is predicted to fall. The IPMs proposed prescribe the output as an interval valued function of the model's inputs, render a formal description of both the uncertainty in the model's parameters and of the spread in the predicted output. Uncertainty is prescribed as a hyper-rectangular set in the space of model's parameters. The propagation of this set through the empirical model yields a range of outputs of minimal spread containing all (or, depending on the formulation, most) of the observations. Optimization-based strategies for calculating IPMs and eliminating the effects of outliers are proposed. Outliers are identified by evaluating the extent by which they degrade the tightness of the prediction. This evaluation can be carried out while the IPM is calculated. When the data satisfies mild stochastic assumptions, and the optimization program used for calculating the IPM is convex (or, when its solution coincides with the solution to an auxiliary convex program), the model's reliability (that is, the probability that a future observation would be within the predicted range of outputs) can be bounded rigorously by a non-asymptotic formula.

Protein 8-class secondary structure prediction using conditional neural fields.

PubMed

Wang, Zhiyong; Zhao, Feng; Peng, Jian; Xu, Jinbo

2011-10-01

Compared with the protein 3-class secondary structure (SS) prediction, the 8-class prediction gains less attention and is also much more challenging, especially for proteins with few sequence homologs. This paper presents a new probabilistic method for 8-class SS prediction using conditional neural fields (CNFs), a recently invented probabilistic graphical model. This CNF method not only models the complex relationship between sequence features and SS, but also exploits the interdependency among SS types of adjacent residues. In addition to sequence profiles, our method also makes use of non-evolutionary information for SS prediction. Tested on the CB513 and RS126 data sets, our method achieves Q8 accuracy of 64.9 and 64.7%, respectively, which are much better than the SSpro8 web server (51.0 and 48.0%, respectively). Our method can also be used to predict other structure properties (e.g. solvent accessibility) of a protein or the SS of RNA. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

ERIC Educational Resources Information Center

Ladd, Helen F.

2009-01-01

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

A predictive score for optimal cytoreduction at interval debulking surgery in epithelial ovarian cancer: a two- centers experience.

PubMed

Ghisoni, Eleonora; Katsaros, Dionyssios; Maggiorotto, Furio; Aglietta, Massimo; Vaira, Marco; De Simone, Michele; Mittica, Gloria; Giannone, Gaia; Robella, Manuela; Genta, Sofia; Lucchino, Fabiola; Marocco, Francesco; Borella, Fulvio; Valabrega, Giorgio; Ponzone, Riccardo

2018-05-30

Optimal cytoreduction (macroscopic Residual Tumor, RT = 0) is the best survival predictor factor in epithelial ovarian cancer (EOC). It doesn't exist a consolidated criteria to predict optimal surgical resection at interval debulking surgery (IDS). The aim of this study is to develop a predictive model of complete cytoreduction at IDS. We, retrospectively, analyzed 93 out of 432 patients, with advanced EOC, underwent neoadjuvant chemotherapy (NACT) and IDS from January 2010 to December 2016 in two referral cancer centers. The correlation between clinical-pathological variables and residual disease at IDS has been investigated with univariate and multivariate analysis. A predictive score of cytoreduction (PSC) has been created by combining all significant variables. The performance of each single variable and PSC has been reported and the correlation of all significant variables with progression free survival (PFS) has been assessed. At IDS, 65 patients (69,8%) had complete cytoreduction with no residual disease (R = 0). Three criteria independently predicted R > 0: age ≥ 60 years (p = 0.014), CA-125 before NACT > 550 UI/dl (p = 0.044), and Peritoneal Cancer Index (PCI) > 16 (p < 0.001). A PSC ≥ 3 has been associated with a better accuracy (85,8%), limiting the number of incomplete surgeries to 16,5%. Moreover, a PCI > 16, a PSC ≥ 3 and the presence of R > 0 after IDS were all significantly associated with shorter PFS (p < 0.001, p < 0.001 and p = 0.004 respectively). Our PSC predicts, in a large number of patients, complete cytoreduction at IDS, limiting the rate of futile extensive surgeries in case of presence of residual tumor (R > 0). The PSC should be prospectively validated in a larger series of EOC patients undergoing NACT-IDS.

Modeling Relationships Between Flight Crew Demographics and Perceptions of Interval Management

NASA Technical Reports Server (NTRS)

Remy, Benjamin; Wilson, Sara R.

2016-01-01

The Interval Management Alternative Clearances (IMAC) human-in-the-loop simulation experiment was conducted to assess interval management system performance and participants' acceptability and workload while performing three interval management clearance types. Twenty-four subject pilots and eight subject controllers flew ten high-density arrival scenarios into Denver International Airport during two weeks of data collection. This analysis examined the possible relationships between subject pilot demographics on reported perceptions of interval management in IMAC. Multiple linear regression models were created with a new software tool to predict subject pilot questionnaire item responses from demographic information. General patterns were noted across models that may indicate flight crew demographics influence perceptions of interval management.

The 32nd CDC: System identification using interval dynamic models

NASA Technical Reports Server (NTRS)

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

1992-01-01

Motivated by the recent explosive development of results in the area of parametric robust control, a new technique to identify a family of uncertain systems is identified. The new technique takes the frequency domain input and output data obtained from experimental test signals and produces an 'interval transfer function' that contains the complete frequency domain behavior with respect to the test signals. This interval transfer function is one of the key concepts in the parametric robust control approach and identification with such an interval model allows one to predict the worst case performance and stability margins using recent results on interval systems. The algorithm is illustrated by applying it to an 18 bay Mini-Mast truss structure.

On the accuracy and reliability of predictions by control-system theory.

PubMed

Bourbon, W T; Copeland, K E; Dyer, V R; Harman, W K; Mosley, B L

1990-12-01

In three experiments we used control-system theory (CST) to predict the results of tracking tasks on which people held a handle to keep a cursor even with a target on a computer screen. 10 people completed a total of 104 replications of the task. In each experiment, there were two conditions: in one, only the handle affected the position of the cursor; in the other, a random disturbance also affected the cursor. From a person's performance during Condition 1, we derived constants used in the CST model to predict the results of Condition 2. In two experiments, predictions occurred a few minutes before Condition 2; in one experiment, the delay was 1 yr. During a 1-min. experimental run, the positions of handle and cursor, produced by the person, were each sampled 1800 times, once every 1/30 sec. During a modeling run, the model predicted the positions of the handle and target for each of the 1800 intervals sampled in the experimental run. In 104 replications, the mean correlation between predicted and actual positions of the handle was .996; SD = .002.

The QT Interval and Risk of Incident Atrial Fibrillation

PubMed Central

Mandyam, Mala C.; Soliman, Elsayed Z.; Alonso, Alvaro; Dewland, Thomas A.; Heckbert, Susan R.; Vittinghoff, Eric; Cummings, Steven R.; Ellinor, Patrick T.; Chaitman, Bernard R.; Stocke, Karen; Applegate, William B.; Arking, Dan E.; Butler, Javed; Loehr, Laura R.; Magnani, Jared W.; Murphy, Rachel A.; Satterfield, Suzanne; Newman, Anne B.; Marcus, Gregory M.

2013-01-01

BACKGROUND Abnormal atrial repolarization is important in the development of atrial fibrillation (AF), but no direct measurement is available in clinical medicine. OBJECTIVE To determine whether the QT interval, a marker of ventricular repolarization, could be used to predict incident AF. METHODS We examined a prolonged QT corrected by the Framingham formula (QTFram) as a predictor of incident AF in the Atherosclerosis Risk in Communities (ARIC) study. The Cardiovascular Health Study (CHS) and Health, Aging, and Body Composition (Health ABC) study were used for validation. Secondary predictors included QT duration as a continuous variable, a short QT interval, and QT intervals corrected by other formulae. RESULTS Among 14,538 ARIC participants, a prolonged QTFram predicted a roughly two-fold increased risk of AF (hazard ratio [HR] 2.05, 95% confidence interval [CI] 1.42–2.96, p<0.001). No substantive attenuation was observed after adjustment for age, race, sex, study center, body mass index, hypertension, diabetes, coronary disease, and heart failure. The findings were validated in CHS and Health ABC and were similar across various QT correction methods. Also in ARIC, each 10-ms increase in QTFram was associated with an increased unadjusted (HR 1.14, 95%CI 1.10–1.17, p<0.001) and adjusted (HR 1.11, 95%CI 1.07–1.14, p<0.001) risk of AF. Findings regarding a short QT were inconsistent across cohorts. CONCLUSIONS A prolonged QT interval is associated with an increased risk of incident AF. PMID:23872693

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.

Applying linear discriminant analysis to predict groundwater redox conditions conducive to denitrification

NASA Astrophysics Data System (ADS)

Wilson, S. R.; Close, M. E.; Abraham, P.

2018-01-01

Diffuse nitrate losses from agricultural land pollute groundwater resources worldwide, but can be attenuated under reducing subsurface conditions. In New Zealand, the ability to predict where groundwater denitrification occurs is important for understanding the linkage between land use and discharges of nitrate-bearing groundwater to streams. This study assesses the application of linear discriminant analysis (LDA) for predicting groundwater redox status for Southland, a major dairy farming region in New Zealand. Data cases were developed by assigning a redox status to samples derived from a regional groundwater quality database. Pre-existing regional-scale geospatial databases were used as training variables for the discriminant functions. The predictive accuracy of the discriminant functions was slightly improved by optimising the thresholds between sample depth classes. The models predict 23% of the region as being reducing at shallow depths (<15 m), and 37% at medium depths (15-75 m). Predictions were made at a sub-regional level to determine whether improvements could be made with discriminant functions trained by local data. The results indicated that any gains in predictive success were offset by loss of confidence in the predictions due to the reduction in the number of samples used. The regional scale model predictions indicate that subsurface reducing conditions predominate at low elevations on the coastal plains where poorly drained soils are widespread. Additional indicators for subsurface denitrification are a high carbon content of the soil, a shallow water table, and low-permeability clastic sediments. The coastal plains are an area of widespread groundwater discharge, and the soil and hydrology characteristics require the land to be artificially drained to render the land suitable for farming. For the improvement of water quality in coastal areas, it is therefore important that land and water management efforts focus on understanding hydrological

Characterization of minimal sequences associated with self-similar interval exchange maps

NASA Astrophysics Data System (ADS)

Cobo, Milton; Gutiérrez-Romo, Rodolfo; Maass, Alejandro

2018-04-01

The construction of affine interval exchange maps (IEMs) with wandering intervals that are semi-conjugate to a given self-similar IEM is strongly related to the existence of the so-called minimal sequences associated with local potentials, which are certain elements of the substitution subshift arising from the given IEM. In this article, under the condition called unique representation property, we characterize such minimal sequences for potentials coming from non-real eigenvalues of the substitution matrix. We also give conditions on the slopes of the affine extensions of a self-similar IEM that determine whether it exhibits a wandering interval or not.

Cognitive Mechanisms Underlying Action Prediction in Children and Adults with Autism Spectrum Condition

ERIC Educational Resources Information Center

Schuwerk, Tobias; Sodian, Beate; Paulus, Markus

2016-01-01

Recent research suggests that impaired action prediction is at the core of social interaction deficits in autism spectrum condition (ASC). Here, we targeted two cognitive mechanisms that are thought to underlie the prediction of others' actions: statistical learning and efficiency considerations. We measured proactive eye movements of 10-year-old…

Military Applicability of Interval Training for Health and Performance.

PubMed

Gibala, Martin J; Gagnon, Patrick J; Nindl, Bradley C

2015-11-01

Militaries from around the globe have predominantly used endurance training as their primary mode of aerobic physical conditioning, with historical emphasis placed on the long distance run. In contrast to this traditional exercise approach to training, interval training is characterized by brief, intermittent bouts of intense exercise, separated by periods of lower intensity exercise or rest for recovery. Although hardly a novel concept, research over the past decade has shed new light on the potency of interval training to elicit physiological adaptations in a time-efficient manner. This work has largely focused on the benefits of low-volume interval training, which involves a relatively small total amount of exercise, as compared with the traditional high-volume approach to training historically favored by militaries. Studies that have directly compared interval and moderate-intensity continuous training have shown similar improvements in cardiorespiratory fitness and the capacity for aerobic energy metabolism, despite large differences in total exercise and training time commitment. Interval training can also be applied in a calisthenics manner to improve cardiorespiratory fitness and strength, and this approach could easily be incorporated into a military conditioning environment. Although interval training can elicit physiological changes in men and women, the potential for sex-specific adaptations in the adaptive response to interval training warrants further investigation. Additional work is needed to clarify adaptations occurring over the longer term; however, interval training deserves consideration from a military applicability standpoint as a time-efficient training strategy to enhance soldier health and performance. There is value for military leaders in identifying strategies that reduce the time required for exercise, but nonetheless provide an effective training stimulus.

Smoke composition and predicting relationships for international commercial cigarettes smoked with three machine-smoking conditions.

PubMed

Counts, M E; Morton, M J; Laffoon, S W; Cox, R H; Lipowicz, P J

2005-04-01

The study objectives were to determine the effects of smoking machine puffing parameters on mainstream smoke composition and to express those effects as predicting relationships. Forty-eight commercial Philip Morris USA and Philip Morris International cigarettes from international markets and the 1R4F reference cigarette were machine-smoked using smoking conditions defined by the International Organization of Standardization (ISO), the Massachusetts Department of Public Health (MDPH), and Health Canada (HC). Cigarette tobacco fillers were analyzed for nitrate, nicotine, tobacco-specific nitrosamines (TSNA), and ammonia. Mainstream yields for tar and 44 individual smoke constituents and "smoke pH" were determined. Cigarette constituent yields typically increased in the order ISOPredicting relationships were developed between ISO tar and ISO, MDPH, and HC constituent yields and between MDPH tar and HC tar and respective smoking condition yields. MDPH and HC constituent yields could be predicted with similar reliability using ISO tar or the corresponding smoking-condition tar. The reliability of the relationships varied from strong to weak, depending on particular constituents. Weak predicting relationships for nitrogen oxides and TSNA's, for example, were improved with inclusion of tobacco filler composition factors. "Smoke pH" was similar for all cigarettes at any one smoking condition, and overall marginally lower at HC conditions than at ISO or MDPH conditions.

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

USDA-ARS?s Scientific Manuscript database

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

Confidence intervals for correlations when data are not normal.

PubMed

Bishara, Anthony J; Hittner, James B

2017-02-01

With nonnormal data, the typical confidence interval of the correlation (Fisher z') may be inaccurate. The literature has been unclear as to which of several alternative methods should be used instead, and how extreme a violation of normality is needed to justify an alternative. Through Monte Carlo simulation, 11 confidence interval methods were compared, including Fisher z', two Spearman rank-order methods, the Box-Cox transformation, rank-based inverse normal (RIN) transformation, and various bootstrap methods. Nonnormality often distorted the Fisher z' confidence interval-for example, leading to a 95 % confidence interval that had actual coverage as low as 68 %. Increasing the sample size sometimes worsened this problem. Inaccurate Fisher z' intervals could be predicted by a sample kurtosis of at least 2, an absolute sample skewness of at least 1, or significant violations of normality hypothesis tests. Only the Spearman rank-order and RIN transformation methods were universally robust to nonnormality. Among the bootstrap methods, an observed imposed bootstrap came closest to accurate coverage, though it often resulted in an overly long interval. The results suggest that sample nonnormality can justify avoidance of the Fisher z' interval in favor of a more robust alternative. R code for the relevant methods is provided in supplementary materials.

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

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

PubMed

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

2006-12-01

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

Interval Training

MedlinePlus

Healthy Lifestyle Fitness Interval training can help you get the most out of your workout. By Mayo Clinic Staff Are you ready to shake ... spending more time at the gym? Consider aerobic interval training. Once the domain of elite athletes, interval training ...

Fractal analyses reveal independent complexity and predictability of gait

PubMed Central

Dierick, Frédéric; Nivard, Anne-Laure

2017-01-01

Locomotion is a natural task that has been assessed for decades and used as a proxy to highlight impairments of various origins. So far, most studies adopted classical linear analyses of spatio-temporal gait parameters. Here, we use more advanced, yet not less practical, non-linear techniques to analyse gait time series of healthy subjects. We aimed at finding more sensitive indexes related to spatio-temporal gait parameters than those previously used, with the hope to better identify abnormal locomotion. We analysed large-scale stride interval time series and mean step width in 34 participants while altering walking direction (forward vs. backward walking) and with or without galvanic vestibular stimulation. The Hurst exponent α and the Minkowski fractal dimension D were computed and interpreted as indexes expressing predictability and complexity of stride interval time series, respectively. These holistic indexes can easily be interpreted in the framework of optimal movement complexity. We show that α and D accurately capture stride interval changes in function of the experimental condition. Walking forward exhibited maximal complexity (D) and hence, adaptability. In contrast, walking backward and/or stimulation of the vestibular system decreased D. Furthermore, walking backward increased predictability (α) through a more stereotyped pattern of the stride interval and galvanic vestibular stimulation reduced predictability. The present study demonstrates the complementary power of the Hurst exponent and the fractal dimension to improve walking classification. Our developments may have immediate applications in rehabilitation, diagnosis, and classification procedures. PMID:29182659

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

PubMed

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

2014-01-01

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

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.

Multidimensional assessment of patient condition and mutational analysis in peripheral blood, as tools to improve outcome prediction in myelodysplastic syndromes: A prospective study of the Spanish MDS group.

PubMed

Ramos, Fernando; Robledo, Cristina; Pereira, Arturo; Pedro, Carmen; Benito, Rocío; de Paz, Raquel; Del Rey, Mónica; Insunza, Andrés; Tormo, Mar; Díez-Campelo, María; Xicoy, Blanca; Salido, Eduardo; Sánchez-Del-Real, Javier; Arenillas, Leonor; Florensa, Lourdes; Luño, Elisa; Del Cañizo, Consuelo; Sanz, Guillermo F; María Hernández-Rivas, Jesús

2017-09-01

The International Prognostic Scoring System and its revised form (IPSS-R) are the most widely used indices for prognostic assessment of patients with myelodysplastic syndromes (MDS), but can only partially account for the observed variation in patient outcomes. This study aimed to evaluate the relative contribution of patient condition and mutational status in peripheral blood when added to the IPSS-R, for estimating overall survival and the risk of leukemic transformation in patients with MDS. A prospective cohort (2006-2015) of 200 consecutive patients with MDS were included in the study series and categorized according to the IPSS-R. Patients were further stratified according to patient condition (assessed using the multidimensional Lee index for older adults) and genetic mutations (peripheral blood samples screened using next-generation sequencing). The change in likelihood-ratio was tested in Cox models after adding individual covariates. The addition of the Lee index to the IPSS-R significantly improved prediction of overall survival [hazard ratio (HR) 3.02, 95% confidence interval (CI) 1.96-4.66, P < 0.001), and mutational analysis significantly improved prediction of leukemic evolution (HR 2.64, 1.56-4.46, P < 0.001). Non-leukemic death was strongly linked to patient condition (HR 2.71, 1.72-4.25, P < 0.001), but not to IPSS-R score (P = 0.35) or mutational status (P = 0.75). Adjustment for exposure to disease-modifying therapy, evaluated as a time-dependent covariate, had no effect on the proposed model's predictive ability. In conclusion, patient condition, assessed by the multidimensional Lee index and patient mutational status can improve the prediction of clinical outcomes of patients with MDS already stratified by IPSS-R. © 2017 Wiley Periodicals, Inc.

Arc Jet Facility Test Condition Predictions Using the ADSI Code

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

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…

A simple, physiologically-based model of sea turtle remigration intervals and nesting population dynamics: Effects of temperature.

PubMed

Neeman, Noga; Spotila, James R; O'Connor, Michael P

2015-09-07

Variation in the yearly number of sea turtles nesting at rookeries can interfere with population estimates and obscure real population dynamics. Previous theoretical models suggested that this variation in nesting numbers may be driven by changes in resources at the foraging grounds. We developed a physiologically-based model that uses temperatures at foraging sites to predict foraging conditions, resource accumulation, remigration probabilities, and, ultimately, nesting numbers for a stable population of sea turtles. We used this model to explore several scenarios of temperature variation at the foraging grounds, including one-year perturbations and cyclical temperature oscillations. We found that thermally driven resource variation can indeed synchronize nesting in groups of turtles, creating cohorts, but that these cohorts tend to break down over 5-10 years unless regenerated by environmental conditions. Cohorts were broken down faster at lower temperatures. One-year perturbations of low temperature had a synchronizing effect on nesting the following year, while high temperature perturbations tended to delay nesting in a less synchronized way. Cyclical temperatures lead to cyclical responses both in nesting numbers and remigration intervals, with the amplitude and lag of the response depending on the duration of the cycle. Overall, model behavior is consistent with observations at nesting beaches. Future work should focus on refining the model to fit particular nesting populations and testing further whether or not it may be used to predict observed nesting numbers and remigration intervals. Copyright © 2015 Elsevier Ltd. All rights reserved.

An interval model updating strategy using interval response surface models

NASA Astrophysics Data System (ADS)

Fang, Sheng-En; Zhang, Qiu-Hu; Ren, Wei-Xin

2015-08-01

Stochastic model updating provides an effective way of handling uncertainties existing in real-world structures. In general, probabilistic theories, fuzzy mathematics or interval analyses are involved in the solution of inverse problems. However in practice, probability distributions or membership functions of structural parameters are often unavailable due to insufficient information of a structure. At this moment an interval model updating procedure shows its superiority in the aspect of problem simplification since only the upper and lower bounds of parameters and responses are sought. To this end, this study develops a new concept of interval response surface models for the purpose of efficiently implementing the interval model updating procedure. The frequent interval overestimation due to the use of interval arithmetic can be maximally avoided leading to accurate estimation of parameter intervals. Meanwhile, the establishment of an interval inverse problem is highly simplified, accompanied by a saving of computational costs. By this means a relatively simple and cost-efficient interval updating process can be achieved. Lastly, the feasibility and reliability of the developed method have been verified against a numerical mass-spring system and also against a set of experimentally tested steel plates.

Prediction of Flows about Forebodies at High-Angle-of-Attack Dynamic Conditions

NASA Technical Reports Server (NTRS)

Fremaux, C. M.; vanDam, C. P.; Saephan, S.; DalBello, T.

2003-01-01

A Reynolds-average Navier Stokes method developed for rotorcraft type of flow problems is applied for predicting the forces and moments of forebody models at high-angle-of-attack dynamic conditions and for providing insight into the flow characteristics at these conditions. Wind-tunnel results from rotary testing on generic forebody models conducted by NASA Langley and DERA are used for comparison. This paper focuses on the steady-state flow problem.

Different target-discrimination times can be followed by the same saccade-initiation timing in different stimulus conditions during visual searches

PubMed Central

Tanaka, Tomohiro; Nishida, Satoshi

2015-01-01

The neuronal processes that underlie visual searches can be divided into two stages: target discrimination and saccade preparation/generation. This predicts that the length of time of the prediscrimination stage varies according to the search difficulty across different stimulus conditions, whereas the length of the latter postdiscrimination stage is stimulus invariant. However, recent studies have suggested that the length of the postdiscrimination interval changes with different stimulus conditions. To address whether and how the visual stimulus affects determination of the postdiscrimination interval, we recorded single-neuron activity in the lateral intraparietal area (LIP) when monkeys (Macaca fuscata) performed a color-singleton search involving four stimulus conditions that differed regarding luminance (Bright vs. Dim) and target-distractor color similarity (Easy vs. Difficult). We specifically focused on comparing activities between the Bright-Difficult and Dim-Easy conditions, in which the visual stimuli were considerably different, but the mean reaction times were indistinguishable. This allowed us to examine the neuronal activity when the difference in the degree of search speed between different stimulus conditions was minimal. We found that not only prediscrimination but also postdiscrimination intervals varied across stimulus conditions: the postdiscrimination interval was longer in the Dim-Easy condition than in the Bright-Difficult condition. Further analysis revealed that the postdiscrimination interval might vary with stimulus luminance. A computer simulation using an accumulation-to-threshold model suggested that the luminance-related difference in visual response strength at discrimination time could be the cause of different postdiscrimination intervals. PMID:25995344

Music enhances performance and perceived enjoyment of sprint interval exercise.

PubMed

Stork, Matthew J; Kwan, Matthew Y W; Gibala, Martin J; Martin Ginis, Kathleen A

2015-05-01

Interval exercise training can elicit physiological adaptations similar to those of traditional endurance training, but with reduced time. However, the intense nature of specific protocols, particularly the "all-out" efforts characteristic of sprint interval training (SIT), may be perceived as being aversive. The purpose of this study was to determine whether listening to self-selected music can reduce the potential aversiveness of an acute session of SIT by improving affect, motivation, and enjoyment, and to examine the effects of music on performance. Twenty moderately active adults (22 ± 4 yr) unfamiliar with interval exercise completed an acute session of SIT under two different conditions: music and no music. The exercise consisted of four 30-s "all-out" Wingate Anaerobic Test bouts on a cycle ergometer, separated by 4 min of rest. Peak and mean power output, RPE, affect, task motivation, and perceived enjoyment of the exercise were measured. Mixed-effects models were used to evaluate changes in dependent measures over time and between the two conditions. Peak and mean power over the course of the exercise session were higher in the music condition (coefficient = 49.72 [SE = 13.55] and coefficient = 23.65 [SE = 11.30]; P < 0.05). A significant time by condition effect emerged for peak power (coefficient = -12.31 [SE = 4.95]; P < 0.05). There were no between-condition differences in RPE, affect, or task motivation. Perceived enjoyment increased over time and was consistently higher in the music condition (coefficient = 7.00 [SE = 3.05]; P < 0.05). Music enhances in-task performance and enjoyment of an acute bout of SIT. Listening to music during intense interval exercise may be an effective strategy for facilitating participation in, and adherence to, this form of training.

MEETING DATA QUALITY OBJECTIVES WITH INTERVAL INFORMATION

EPA Science Inventory

Immunoassay test kits are promising technologies for measuring analytes under field conditions. Frequently, these field-test kits report the analyte concentrations as falling in an interval between minimum and maximum values. Many project managers use field-test kits only for scr...

The effects of interstimulus interval on event-related indices of attention: an auditory selective attention test of perceptual load theory.

PubMed

Gomes, Hilary; Barrett, Sophia; Duff, Martin; Barnhardt, Jack; Ritter, Walter

2008-03-01

We examined the impact of perceptual load by manipulating interstimulus interval (ISI) in two auditory selective attention studies that varied in the difficulty of the target discrimination. In the paradigm, channels were separated by frequency and target/deviant tones were softer in intensity. Three ISI conditions were presented: fast (300ms), medium (600ms) and slow (900ms). Behavioral (accuracy and RT) and electrophysiological measures (Nd, P3b) were observed. In both studies, participants evidenced poorer accuracy during the fast ISI condition than the slow suggesting that ISI impacted task difficulty. However, none of the three measures of processing examined, Nd amplitude, P3b amplitude elicited by unattended deviant stimuli, or false alarms to unattended deviants, were impacted by ISI in the manner predicted by perceptual load theory. The prediction based on perceptual load theory, that there would be more processing of irrelevant stimuli under conditions of low as compared to high perceptual load, was not supported in these auditory studies. Task difficulty/perceptual load impacts the processing of irrelevant stimuli in the auditory modality differently than predicted by perceptual load theory, and perhaps differently than in the visual modality.

Predicted Weather Display and Decision Support Interface for Flight Deck

NASA Technical Reports Server (NTRS)

Johnson, Walter W. (Inventor); Wong, Dominic G. (Inventor); Koteskey, Robert W. (Inventor); Wu, Shu-Chieh (Inventor)

2017-01-01

A system and method for providing visual depictions of a predictive weather forecast for in-route vehicle trajectory planning. The method includes displaying weather information on a graphical display, displaying vehicle position information on the graphical display, selecting a predictive interval, displaying predictive weather information for the predictive interval on the graphical display, and displaying predictive vehicle position information for the predictive interval on the graphical display, such that the predictive vehicle position information is displayed relative to the predictive weather information, for in-route trajectory planning.

Conditioned [corrected] stimulus informativeness governs conditioned stimulus-unconditioned stimulus associability.

PubMed

Ward, Ryan D; Gallistel, C R; Jensen, Greg; Richards, Vanessa L; Fairhurst, Stephen; Balsam, Peter D

2012-07-01

In a conditioning protocol, the onset of the conditioned stimulus ([CS]) provides information about when to expect reinforcement (unconditioned stimulus [US]). There are two sources of information from the CS in a delay conditioning paradigm in which the CS-US interval is fixed. The first depends on the informativeness, the degree to which CS onset reduces the average expected time to onset of the next US. The second depends only on how precisely a subject can represent a fixed-duration interval (the temporal Weber fraction). In three experiments with mice, we tested the differential impact of these two sources of information on rate of acquisition of conditioned responding (CS-US associability). In Experiment 1, we showed that associability (the inverse of trials to acquisition) increased in proportion to informativeness. In Experiment 2, we showed that fixing the duration of the US-US interval or the CS-US interval or both had no effect on associability. In Experiment 3, we equated the increase in information produced by varying the C/T ratio with the increase produced by fixing the duration of the CS-US interval. Associability increased with increased informativeness, but, as in Experiment 2, fixing the CS-US duration had no effect on associability. These results are consistent with the view that CS-US associability depends on the increased rate of reward signaled by CS onset. The results also provide further evidence that conditioned responding is temporally controlled when it emerges.

Conditional power and predictive power based on right censored data with supplementary auxiliary information.

PubMed

Sun, Libo; Wan, Ying

2018-04-22

Conditional power and predictive power provide estimates of the probability of success at the end of the trial based on the information from the interim analysis. The observed value of the time to event endpoint at the interim analysis could be biased for the true treatment effect due to early censoring, leading to a biased estimate of conditional power and predictive power. In such cases, the estimates and inference for this right censored primary endpoint are enhanced by incorporating a fully observed auxiliary variable. We assume a bivariate normal distribution of the transformed primary variable and a correlated auxiliary variable. Simulation studies are conducted that not only shows enhanced conditional power and predictive power but also can provide the framework for a more efficient futility interim analysis in terms of an improved accuracy in estimator, a smaller inflation in type II error and an optimal timing for such analysis. We also illustrated the new approach by a real clinical trial example. Copyright © 2018 John Wiley & Sons, Ltd.

Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction

PubMed Central

Howard, Réka; Carriquiry, Alicia L.; Beavis, William D.

2017-01-01

An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits comprised of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability). Possible values for these factors and the number of combinations of the factor levels that influence the performance of GP methods can be large. Thus, efficient methods for identifying combinations of factor levels that produce most accurate GPs is needed. Herein, we employ response surface methods (RSMs) to find the experimental conditions that produce the most accurate GPs. We illustrate RSM with an example of simulated doubled haploid populations and identify the combination of factors that maximize the difference between prediction accuracies of best linear unbiased prediction (BLUP) and support vector machine (SVM) GP methods. The greatest impact on the response is due to the genetic architecture of the population, heritability of the trait, and the sample size. When epistasis is responsible for all of the genotypic variance and heritability is equal to one and the sample size of the training population is large, the advantage of using the SVM method vs. the BLUP method is greatest. However, except for values close to the maximum, most of the response surface shows little difference between the methods. We also determined that the conditions resulting in the greatest prediction accuracy for BLUP occurred when genetic architecture consists solely of additive effects, and heritability is equal to one. PMID

Familiar Tonal Context Improves Accuracy of Pitch Interval Perception.

PubMed

Graves, Jackson E; Oxenham, Andrew J

2017-01-01

A fundamental feature of everyday music perception is sensitivity to familiar tonal structures such as musical keys. Many studies have suggested that a tonal context can enhance the perception and representation of pitch. Most of these studies have measured response time, which may reflect expectancy as opposed to perceptual accuracy. We instead used a performance-based measure, comparing participants' ability to discriminate between a "small, in-tune" interval and a "large, mistuned" interval in conditions that involved familiar tonal relations (diatonic, or major, scale notes), unfamiliar tonal relations (whole-tone or mistuned-diatonic scale notes), repetition of a single pitch, or no tonal context. The context was established with a brief sequence of tones in Experiment 1 (melodic context), and a cadence-like two-chord progression in Experiment 2 (harmonic context). In both experiments, performance significantly differed across the context conditions, with a diatonic context providing a significant advantage over no context; however, no correlation with years of musical training was observed. The diatonic tonal context also provided an advantage over the whole-tone scale context condition in Experiment 1 (melodic context), and over the mistuned scale or repetition context conditions in Experiment 2 (harmonic context). However, the relatively small benefit to performance suggests that the main advantage of tonal context may be priming of expected stimuli, rather than enhanced accuracy of pitch interval representation.

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

PubMed

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

2017-05-02

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

Variations in rupture process with recurrence interval in a repeated small earthquake

USGS Publications Warehouse

Vidale, J.E.; Ellsworth, W.L.; Cole, A.; Marone, Chris

1994-01-01

In theory and in laboratory experiments, friction on sliding surfaces such as rock, glass and metal increases with time since the previous episode of slip. This time dependence is a central pillar of the friction laws widely used to model earthquake phenomena. On natural faults, other properties, such as rupture velocity, porosity and fluid pressure, may also vary with the recurrence interval. Eighteen repetitions of the same small earthquake, separated by intervals ranging from a few days to several years, allow us to test these laboratory predictions in situ. The events with the longest time since the previous earthquake tend to have about 15% larger seismic moment than those with the shortest intervals, although this trend is weak. In addition, the rupture durations of the events with the longest recurrence intervals are more than a factor of two shorter than for the events with the shortest intervals. Both decreased duration and increased friction are consistent with progressive fault healing during the time of stationary contact.In theory and in laboratory experiments, friction on sliding surfaces such as rock, glass and metal increases with time since the previous episode of slip. This time dependence is a central pillar of the friction laws widely used to model earthquake phenomena. On natural faults, other properties, such as rupture velocity, porosity and fluid pressure, may also vary with the recurrence interval. Eighteen repetitions of the same small earthquake, separated by intervals ranging from a few days to several years, allow us to test these laboratory predictions in situ. The events with the longest time since the previous earthquake tend to have about 15% larger seismic moment than those with the shortest intervals, although this trend is weak. In addition, the rupture durations of the events with the longest recurrence intervals are more than a factor of two shorter than for the events with the shortest intervals. Both decreased duration and

Binary Interval Search: a scalable algorithm for counting interval intersections.

PubMed

Layer, Ryan M; Skadron, Kevin; Robins, Gabriel; Hall, Ira M; Quinlan, Aaron R

2013-01-01

The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery. We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals. https://github.com/arq5x/bits.

The Use of Commercial Remote Sensing Predicting Helicopter Brownout Conditions

DTIC Science & Technology

2007-09-01

and is not partial to the number of times an individual can visit the site and update or supply new information. (Shewfelt 2006) (U) An example of...leaves as well as promote new growth of more green vegetation.(Shewfelt 2006) This green leaf vegetation relation to moisture plays a vital role in...Sensing in Predicting Helicopter Brownout Conditions. September 2006 (Top Secret). Tan, Kim H., First Edition, Enviromental Soil Science Marcel

A sequential solution for anisotropic total variation image denoising with interval constraints

NASA Astrophysics Data System (ADS)

Xu, Jingyan; Noo, Frédéric

2017-09-01

We show that two problems involving the anisotropic total variation (TV) and interval constraints on the unknown variables admit, under some conditions, a simple sequential solution. Problem 1 is a constrained TV penalized image denoising problem; problem 2 is a constrained fused lasso signal approximator. The sequential solution entails finding first the solution to the unconstrained problem, and then applying a thresholding to satisfy the constraints. If the interval constraints are uniform, this sequential solution solves problem 1. If the interval constraints furthermore contain zero, the sequential solution solves problem 2. Here uniform interval constraints refer to all unknowns being constrained to the same interval. A typical example of application is image denoising in x-ray CT, where the image intensities are non-negative as they physically represent linear attenuation coefficient in the patient body. Our results are simple yet seem unknown; we establish them using the Karush-Kuhn-Tucker conditions for constrained convex optimization.

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

Treesearch

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

2009-01-01

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

A preliminary study on postmortem interval estimation of suffocated rats by GC-MS/MS-based plasma metabolic profiling.

PubMed

Sato, Takako; Zaitsu, Kei; Tsuboi, Kento; Nomura, Masakatsu; Kusano, Maiko; Shima, Noriaki; Abe, Shuntaro; Ishii, Akira; Tsuchihashi, Hitoshi; Suzuki, Koichi

2015-05-01

Estimation of postmortem interval (PMI) is an important goal in judicial autopsy. Although many approaches can estimate PMI through physical findings and biochemical tests, accurate PMI calculation by these conventional methods remains difficult because PMI is readily affected by surrounding conditions, such as ambient temperature and humidity. In this study, Sprague-Dawley (SD) rats (10 weeks) were sacrificed by suffocation, and blood was collected by dissection at various time intervals (0, 3, 6, 12, 24, and 48 h; n = 6) after death. A total of 70 endogenous metabolites were detected in plasma by gas chromatography-tandem mass spectrometry (GC-MS/MS). Each time group was separated from each other on the principal component analysis (PCA) score plot, suggesting that the various endogenous metabolites changed with time after death. To prepare a prediction model of a PMI, a partial least squares (or projection to latent structure, PLS) regression model was constructed using the levels of significantly different metabolites determined by variable importance in the projection (VIP) score and the Kruskal-Wallis test (P < 0.05). Because the constructed PLS regression model could successfully predict each PMI, this model was validated with another validation set (n = 3). In conclusion, plasma metabolic profiling demonstrated its ability to successfully estimate PMI under a certain condition. This result can be considered to be the first step for using the metabolomics method in future forensic casework.

Modeling long period swell in Southern California: Practical boundary conditions from buoy observations and global wave model predictions

NASA Astrophysics Data System (ADS)

Crosby, S. C.; O'Reilly, W. C.; Guza, R. T.

2016-02-01

Accurate, unbiased, high-resolution (in space and time) nearshore wave predictions are needed to drive models of beach erosion, coastal flooding, and alongshore transport of sediment, biota and pollutants. On highly sheltered shorelines, wave predictions are sensitive to the directions of onshore propagating waves, and nearshore model prediction error is often dominated by uncertainty in offshore boundary conditions. Offshore islands and shoals, and coastline curvature, create complex sheltering patterns over the 250km span of southern California (SC) shoreline. Here, regional wave model skill in SC was compared for different offshore boundary conditions created using offshore buoy observations and global wave model hindcasts (National Oceanographic and Atmospheric Administration Wave Watch 3, WW3). Spectral ray-tracing methods were used to transform incident offshore swell (0.04-0.09Hz) energy at high directional resolution (1-deg). Model skill is assessed for predictions (wave height, direction, and alongshore radiation stress) at 16 nearshore buoy sites between 2000 and 2009. Model skill using buoy-derived boundary conditions is higher than with WW3-derived boundary conditions. Buoy-driven nearshore model results are similar with various assumptions about the true offshore directional distribution (maximum entropy, Bayesian direct, and 2nd derivative smoothness). Two methods combining offshore buoy observations with WW3 predictions in the offshore boundary condition did not improve nearshore skill above buoy-only methods. A case example at Oceanside harbor shows strong sensitivity of alongshore sediment transport predictions to different offshore boundary conditions. Despite this uncertainty in alongshore transport magnitude, alongshore gradients in transport (e.g. the location of model accretion and erosion zones) are determined by the local bathymetry, and are similar for all predictions.

Binary Interval Search: a scalable algorithm for counting interval intersections

PubMed Central

Layer, Ryan M.; Skadron, Kevin; Robins, Gabriel; Hall, Ira M.; Quinlan, Aaron R.

2013-01-01

Motivation: The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery. Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals. Availability: https://github.com/arq5x/bits. Contact: arq5x@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23129298

Central Tropical Pacific Variability And ENSO Response To Changing Climate Boundary Conditions: Evidence From Individual Line Island Foraminifera

NASA Astrophysics Data System (ADS)

Rustic, G. T.; Polissar, P. J.; Ravelo, A. C.; White, S. M.

2017-12-01

The El Niño Southern Oscillation (ENSO) plays a dominant role in Earth's climate variability. Paleoceanographic evidence suggests that ENSO has changed in the past, and these changes have been linked to large-scale climatic shifts. While a close relationship between ENSO evolution and climate boundary conditions has been predicted, testing these predictions remains challenging. These climate boundary conditions, including insolation, the mean surface temperature gradient of the tropical Pacific, global ice volume, and tropical thermocline depth, often co-vary and may work together to suppress or enhance the ocean-atmosphere feedbacks that drive ENSO variability. Furthermore, suitable paleo-archives spanning multiple climate states are sparse. We have aimed to test ENSO response to changing climate boundary conditions by generating new reconstructions of mixed-layer variability from sedimentary archives spanning the last three glacial-interglacial cycles from the Central Tropical Pacific Line Islands, where El Niño is strongly expressed. We analyzed Mg/Ca ratios from individual foraminifera to reconstruct mixed-layer variability at discrete time intervals representing combinations of climatic boundary conditions from the middle Holocene to Marine Isotope Stage (MIS) 8. We observe changes in the mixed-layer temperature variability during MIS 5 and during the previous interglacial (MIS 7) showing significant reductions in ENSO amplitude. Differences in variability during glacial and interglacial intervals are also observed. Additionally, we reconstructed mixed-layer and thermocline conditions using multi-species Mg/Ca and stable isotope measurements to more fully characterize the state of the Central Tropical Pacific during these intervals. These reconstructions provide us with a unique view of Central Tropical Pacific variability and water-column structure at discrete intervals under varying boundary climate conditions with which to assess factors that shape ENSO

Confidence Intervals from Realizations of Simulated Nuclear Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Younes, W.; Ratkiewicz, A.; Ressler, J. J.

2017-09-28

Various statistical techniques are discussed that can be used to assign a level of confidence in the prediction of models that depend on input data with known uncertainties and correlations. The particular techniques reviewed in this paper are: 1) random realizations of the input data using Monte-Carlo methods, 2) the construction of confidence intervals to assess the reliability of model predictions, and 3) resampling techniques to impose statistical constraints on the input data based on additional information. These techniques are illustrated with a calculation of the keff value, based on the 235U(n, f) and 239Pu (n, f) cross sections.

Accurate prediction of vaccine stability under real storage conditions and during temperature excursions.

PubMed

Clénet, Didier

2018-04-01

Due to their thermosensitivity, most vaccines must be kept refrigerated from production to use. To successfully carry out global immunization programs, ensuring the stability of vaccines is crucial. In this context, two important issues are critical, namely: (i) predicting vaccine stability and (ii) preventing product damage due to excessive temperature excursions outside of the recommended storage conditions (cold chain break). We applied a combination of advanced kinetics and statistical analyses on vaccine forced degradation data to accurately describe the loss of antigenicity for a multivalent freeze-dried inactivated virus vaccine containing three variants. The screening of large amounts of kinetic models combined with a statistical model selection approach resulted in the identification of two-step kinetic models. Predictions based on kinetic analysis and experimental stability data were in agreement, with approximately five percentage points difference from real values for long-term stability storage conditions, after excursions of temperature and during experimental shipments of freeze-dried products. Results showed that modeling a few months of forced degradation can be used to predict various time and temperature profiles endured by vaccines, i.e. long-term stability, short time excursions outside the labeled storage conditions or shipments at ambient temperature, with high accuracy. Pharmaceutical applications of the presented kinetics-based approach are discussed. Copyright © 2018 The Author. Published by Elsevier B.V. All rights reserved.

Constraint-based Attribute and Interval Planning

NASA Technical Reports Server (NTRS)

Jonsson, Ari; Frank, Jeremy

2013-01-01

In this paper we describe Constraint-based Attribute and Interval Planning (CAIP), a paradigm for representing and reasoning about plans. The paradigm enables the description of planning domains with time, resources, concurrent activities, mutual exclusions among sets of activities, disjunctive preconditions and conditional effects. We provide a theoretical foundation for the paradigm, based on temporal intervals and attributes. We then show how the plans are naturally expressed by networks of constraints, and show that the process of planning maps directly to dynamic constraint reasoning. In addition, we de ne compatibilities, a compact mechanism for describing planning domains. We describe how this framework can incorporate the use of constraint reasoning technology to improve planning. Finally, we describe EUROPA, an implementation of the CAIP framework.

Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

PubMed

Kerlikowske, Karla; Scott, Christopher G; Mahmoudzadeh, Amir P; Ma, Lin; Winham, Stacey; Jensen, Matthew R; Wu, Fang Fang; Malkov, Serghei; Pankratz, V Shane; Cummings, Steven R; Shepherd, John A; Brandt, Kathleen R; Miglioretti, Diana L; Vachon, Celine M

2018-06-05

assessed on tomosynthesis, an emerging breast screening method. Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density. National Cancer Institute.

Alternative Confidence Interval Methods Used in the Diagnostic Accuracy Studies

PubMed Central

Gülhan, Orekıcı Temel

2016-01-01

Background/Aim. It is necessary to decide whether the newly improved methods are better than the standard or reference test or not. To decide whether the new diagnostics test is better than the gold standard test/imperfect standard test, the differences of estimated sensitivity/specificity are calculated with the help of information obtained from samples. However, to generalize this value to the population, it should be given with the confidence intervals. The aim of this study is to evaluate the confidence interval methods developed for the differences between the two dependent sensitivity/specificity values on a clinical application. Materials and Methods. In this study, confidence interval methods like Asymptotic Intervals, Conditional Intervals, Unconditional Interval, Score Intervals, and Nonparametric Methods Based on Relative Effects Intervals are used. Besides, as clinical application, data used in diagnostics study by Dickel et al. (2010) has been taken as a sample. Results. The results belonging to the alternative confidence interval methods for Nickel Sulfate, Potassium Dichromate, and Lanolin Alcohol are given as a table. Conclusion. While preferring the confidence interval methods, the researchers have to consider whether the case to be compared is single ratio or dependent binary ratio differences, the correlation coefficient between the rates in two dependent ratios and the sample sizes. PMID:27478491

Alternative Confidence Interval Methods Used in the Diagnostic Accuracy Studies.

PubMed

Erdoğan, Semra; Gülhan, Orekıcı Temel

2016-01-01

Background/Aim. It is necessary to decide whether the newly improved methods are better than the standard or reference test or not. To decide whether the new diagnostics test is better than the gold standard test/imperfect standard test, the differences of estimated sensitivity/specificity are calculated with the help of information obtained from samples. However, to generalize this value to the population, it should be given with the confidence intervals. The aim of this study is to evaluate the confidence interval methods developed for the differences between the two dependent sensitivity/specificity values on a clinical application. Materials and Methods. In this study, confidence interval methods like Asymptotic Intervals, Conditional Intervals, Unconditional Interval, Score Intervals, and Nonparametric Methods Based on Relative Effects Intervals are used. Besides, as clinical application, data used in diagnostics study by Dickel et al. (2010) has been taken as a sample. Results. The results belonging to the alternative confidence interval methods for Nickel Sulfate, Potassium Dichromate, and Lanolin Alcohol are given as a table. Conclusion. While preferring the confidence interval methods, the researchers have to consider whether the case to be compared is single ratio or dependent binary ratio differences, the correlation coefficient between the rates in two dependent ratios and the sample sizes.

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

PubMed

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

2017-05-01

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

Pigeons' Choices between Fixed-Interval and Random-Interval Schedules: Utility of Variability?

ERIC Educational Resources Information Center

Andrzejewski, Matthew E.; Cardinal, Claudia D.; Field, Douglas P.; Flannery, Barbara A.; Johnson, Michael; Bailey, Kathleen; Hineline, Philip N.

2005-01-01

Pigeons' choosing between fixed-interval and random-interval schedules of reinforcement was investigated in three experiments using a discrete-trial procedure. In all three experiments, the random-interval schedule was generated by sampling a probability distribution at an interval (and in multiples of the interval) equal to that of the…

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

PubMed

Ishikawa, Joji; Ishikawa, Shizukiyo; Kario, Kazuomi

2015-03-01

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

Prediction future asset price which is non-concordant with the historical distribution

NASA Astrophysics Data System (ADS)

Seong, Ng Yew; Hin, Pooi Ah

2015-12-01

This paper attempts to predict the major characteristics of the future asset price which is non-concordant with the distribution estimated from the price today and the prices on a large number of previous days. The three major characteristics of the i-th non-concordant asset price are the length of the interval between the occurrence time of the previous non-concordant asset price and that of the present non-concordant asset price, the indicator which denotes that the non-concordant price is extremely small or large by its values -1 and 1 respectively, and the degree of non-concordance given by the negative logarithm of the probability of the left tail or right tail of which one of the end points is given by the observed future price. The vector of three major characteristics of the next non-concordant price is modelled to be dependent on the vectors corresponding to the present and l - 1 previous non-concordant prices via a 3-dimensional conditional distribution which is derived from a 3(l + 1)-dimensional power-normal mixture distribution. The marginal distribution for each of the three major characteristics can then be derived from the conditional distribution. The mean of the j-th marginal distribution is an estimate of the value of the j-th characteristics of the next non-concordant price. Meanwhile, the 100(α/2) % and 100(1 - α/2) % points of the j-th marginal distribution can be used to form a prediction interval for the j-th characteristic of the next non-concordant price. The performance measures of the above estimates and prediction intervals indicate that the fitted conditional distribution is satisfactory. Thus the incorporation of the distribution of the characteristics of the next non-concordant price in the model for asset price has a good potential of yielding a more realistic model.

Coefficient Omega Bootstrap Confidence Intervals: Nonnormal Distributions

ERIC Educational Resources Information Center

Padilla, Miguel A.; Divers, Jasmin

2013-01-01

The performance of the normal theory bootstrap (NTB), the percentile bootstrap (PB), and the bias-corrected and accelerated (BCa) bootstrap confidence intervals (CIs) for coefficient omega was assessed through a Monte Carlo simulation under conditions not previously investigated. Of particular interests were nonnormal Likert-type and binary items.…

Prediction Models for Dynamic Demand Response

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aman, Saima; Frincu, Marc; Chelmis, Charalampos

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D 2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D 2R, which we address inmore » this paper. Our first contribution is the formal definition of D 2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D 2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D 2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D 2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D 2R. Also, prediction models require just few days’ worth of data indicating that small amounts of historical training data can be used to make reliable predictions

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

PubMed

Fujikawa, Hiroshi; Kimura, Bon; Fujii, Tateo

2009-09-01

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

Event- and interval-based measurement of stuttering: a review.

PubMed

Valente, Ana Rita S; Jesus, Luis M T; Hall, Andreia; Leahy, Margaret

2015-01-01

Event- and interval-based measurements are two different ways of computing frequency of stuttering. Interval-based methodology emerged as an alternative measure to overcome problems associated with reproducibility in the event-based methodology. No review has been made to study the effect of methodological factors in interval-based absolute reliability data or to compute the agreement between the two methodologies in terms of inter-judge, intra-judge and accuracy (i.e., correspondence between raters' scores and an established criterion). To provide a review related to reproducibility of event-based and time-interval measurement, and to verify the effect of methodological factors (training, experience, interval duration, sample presentation order and judgment conditions) on agreement of time-interval measurement; in addition, to determine if it is possible to quantify the agreement between the two methodologies The first two authors searched for articles on ERIC, MEDLINE, PubMed, B-on, CENTRAL and Dissertation Abstracts during January-February 2013 and retrieved 495 articles. Forty-eight articles were selected for review. Content tables were constructed with the main findings. Articles related to event-based measurements revealed values of inter- and intra-judge greater than 0.70 and agreement percentages beyond 80%. The articles related to time-interval measures revealed that, in general, judges with more experience with stuttering presented significantly higher levels of intra- and inter-judge agreement. Inter- and intra-judge values were beyond the references for high reproducibility values for both methodologies. Accuracy (regarding the closeness of raters' judgements with an established criterion), intra- and inter-judge agreement were higher for trained groups when compared with non-trained groups. Sample presentation order and audio/video conditions did not result in differences in inter- or intra-judge results. A duration of 5 s for an interval appears to be

XBeach-G: a tool for predicting gravel barrier response to extreme storm conditions

NASA Astrophysics Data System (ADS)

Masselink, Gerd; Poate, Tim; McCall, Robert; Roelvink, Dano; Russell, Paul; Davidson, Mark

2014-05-01

Gravel beaches protect low-lying back-barrier regions from flooding during storm events and their importance to society is widely acknowledged. Unfortunately, breaching and extensive storm damage has occurred at many gravel sites and this is likely to increase as a result of sea-level rise and enhanced storminess due to climate change. Limited scientific guidance is currently available to provide beach managers with operational management tools to predict the response of gravel beaches to storms. The New Understanding and Prediction of Storm Impacts on Gravel beaches (NUPSIG) project aims to improve our understanding of storm impacts on gravel coastal environments and to develop a predictive capability by modelling these impacts. The NUPSIG project uses a 5-pronged approach to address its aim: (1) analyse hydrodynamic data collected during a proto-type laboratory experiment on a gravel beach; (2) collect hydrodynamic field data on a gravel beach under a range of conditions, including storm waves with wave heights up to 3 m; (3) measure swash dynamics and beach response on 10 gravel beaches during extreme wave conditions with wave heights in excess of 3 m; (4) use the data collected under 1-3 to develop and validate a numerical model to model hydrodynamics and morphological response of gravel beaches under storm conditions; and (5) develop a tool for end-users, based on the model formulated under (4), for predicting storm response of gravel beaches and barriers. The aim of this presentation is to present the key results of the NUPSIG project and introduce the end-user tool for predicting storm response on gravel beaches. The model is based on the numerical model XBeach, and different forcing scenarios (wave and tides), barrier configurations (dimensions) and sediment characteristics are easily uploaded for model simulations using a Graphics User Interface (GUI). The model can be used to determine the vulnerability of gravel barriers to storm events, but can also be

Re-evaluation of link between interpregnancy interval and adverse birth outcomes: retrospective cohort study matching two intervals per mother

PubMed Central

Pereira, Gavin; Jacoby, Peter; de Klerk, Nicholas; Stanley, Fiona J

2014-01-01

Objective To re-evaluate the causal effect of interpregnancy interval on adverse birth outcomes, on the basis that previous studies relying on between mother comparisons may have inadequately adjusted for confounding by maternal risk factors. Design Retrospective cohort study using conditional logistic regression (matching two intervals per mother so each mother acts as her own control) to model the incidence of adverse birth outcomes as a function of interpregnancy interval; additional unconditional logistic regression with adjustment for confounders enabled comparison with the unmatched design of previous studies. Setting Perth, Western Australia, 1980-2010. Participants 40 441 mothers who each delivered three liveborn singleton neonates. Main outcome measures Preterm birth (<37 weeks), small for gestational age birth (<10th centile of birth weight by sex and gestational age), and low birth weight (<2500 g). Results Within mother analysis of interpregnancy intervals indicated a much weaker effect of short intervals on the odds of preterm birth and low birth weight compared with estimates generated using a traditional between mother analysis. The traditional unmatched design estimated an adjusted odds ratio for an interpregnancy interval of 0-5 months (relative to the reference category of 18-23 months) of 1.41 (95% confidence interval 1.31 to 1.51) for preterm birth, 1.26 (1.15 to 1.37) for low birth weight, and 0.98 (0.92 to 1.06) for small for gestational age birth. In comparison, the matched design showed a much weaker effect of short interpregnancy interval on preterm birth (odds ratio 1.07, 0.86 to 1.34) and low birth weight (1.03, 0.79 to 1.34), and the effect for small for gestational age birth remained small (1.08, 0.87 to 1.34). Both the unmatched and matched models estimated a high odds of small for gestational age birth and low birth weight for long interpregnancy intervals (longer than 59 months), but the estimated effect of long interpregnancy

Method and apparatus to predict the remaining service life of an operating system

DOEpatents

Greitzer, Frank L.; Kangas, Lars J.; Terrones, Kristine M.; Maynard, Melody A.; Pawlowski, Ronald A. , Ferryman; Thomas A.; Skorpik, James R.; Wilson, Bary W.

2008-11-25

A method and computer-based apparatus for monitoring the degradation of, predicting the remaining service life of, and/or planning maintenance for, an operating system are disclosed. Diagnostic information on degradation of the operating system is obtained through measurement of one or more performance characteristics by one or more sensors onboard and/or proximate the operating system. Though not required, it is preferred that the sensor data are validated to improve the accuracy and reliability of the service life predictions. The condition or degree of degradation of the operating system is presented to a user by way of one or more calculated, numeric degradation figures of merit that are trended against one or more independent variables using one or more mathematical techniques. Furthermore, more than one trendline and uncertainty interval may be generated for a given degradation figure of merit/independent variable data set. The trendline(s) and uncertainty interval(s) are subsequently compared to one or more degradation figure of merit thresholds to predict the remaining service life of the operating system. The present invention enables multiple mathematical approaches in determining which trendline(s) to use to provide the best estimate of the remaining service life.

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.

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.

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

Prediction of as-cast grain size of inoculated aluminum alloys melt solidified under non-isothermal conditions

NASA Astrophysics Data System (ADS)

Du, Qiang; Li, Yanjun

2015-06-01

In this paper, a multi-scale as-cast grain size prediction model is proposed to predict as-cast grain size of inoculated aluminum alloys melt solidified under non-isothermal condition, i.e., the existence of temperature gradient. Given melt composition, inoculation and heat extraction boundary conditions, the model is able to predict maximum nucleation undercooling, cooling curve, primary phase solidification path and final as-cast grain size of binary alloys. The proposed model has been applied to two Al-Mg alloys, and comparison with laboratory and industrial solidification experimental results have been carried out. The preliminary conclusion is that the proposed model is a promising suitable microscopic model used within the multi-scale casting simulation modelling framework.

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

Personalized metabolomics for predicting glucose tolerance changes in sedentary women after high-intensity interval training.

PubMed

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

2014-08-28

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.

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

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 mortality

ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective

NASA Astrophysics Data System (ADS)

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

2018-07-01

Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas (socalled "sensitive areas"). And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Niño prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni˜no prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year, increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.

Abstract: Inference and Interval Estimation for Indirect Effects With Latent Variable Models.

PubMed

Falk, Carl F; Biesanz, Jeremy C

2011-11-30

Models specifying indirect effects (or mediation) and structural equation modeling are both popular in the social sciences. Yet relatively little research has compared methods that test for indirect effects among latent variables and provided precise estimates of the effectiveness of different methods. This simulation study provides an extensive comparison of methods for constructing confidence intervals and for making inferences about indirect effects with latent variables. We compared the percentile (PC) bootstrap, bias-corrected (BC) bootstrap, bias-corrected accelerated (BC a ) bootstrap, likelihood-based confidence intervals (Neale & Miller, 1997), partial posterior predictive (Biesanz, Falk, and Savalei, 2010), and joint significance tests based on Wald tests or likelihood ratio tests. All models included three reflective latent variables representing the independent, dependent, and mediating variables. The design included the following fully crossed conditions: (a) sample size: 100, 200, and 500; (b) number of indicators per latent variable: 3 versus 5; (c) reliability per set of indicators: .7 versus .9; (d) and 16 different path combinations for the indirect effect (α = 0, .14, .39, or .59; and β = 0, .14, .39, or .59). Simulations were performed using a WestGrid cluster of 1680 3.06GHz Intel Xeon processors running R and OpenMx. Results based on 1,000 replications per cell and 2,000 resamples per bootstrap method indicated that the BC and BC a bootstrap methods have inflated Type I error rates. Likelihood-based confidence intervals and the PC bootstrap emerged as methods that adequately control Type I error and have good coverage rates.

Hypothesis testing and earthquake prediction.

PubMed

Jackson, D D

1996-04-30

Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions.

Hypothesis testing and earthquake prediction.

PubMed Central

Jackson, D D

1996-01-01

Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions. PMID:11607663

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...

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.

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.

Use of Linear Prediction Uncertainty Analysis to Guide Conditioning of Models Simulating Surface-Water/Groundwater Interactions

NASA Astrophysics Data System (ADS)

Hughes, J. D.; White, J.; Doherty, J.

2011-12-01

Linear prediction uncertainty analysis in a Bayesian framework was applied to guide the conditioning of an integrated surface water/groundwater model that will be used to predict the effects of groundwater withdrawals on surface-water and groundwater flows. Linear prediction uncertainty analysis is an effective approach for identifying (1) raw and processed data most effective for model conditioning prior to inversion, (2) specific observations and periods of time critically sensitive to specific predictions, and (3) additional observation data that would reduce model uncertainty relative to specific predictions. We present results for a two-dimensional groundwater model of a 2,186 km2 area of the Biscayne aquifer in south Florida implicitly coupled to a surface-water routing model of the actively managed canal system. The model domain includes 5 municipal well fields withdrawing more than 1 Mm3/day and 17 operable surface-water control structures that control freshwater releases from the Everglades and freshwater discharges to Biscayne Bay. More than 10 years of daily observation data from 35 groundwater wells and 24 surface water gages are available to condition model parameters. A dense parameterization was used to fully characterize the contribution of the inversion null space to predictive uncertainty and included bias-correction parameters. This approach allows better resolution of the boundary between the inversion null space and solution space. Bias-correction parameters (e.g., rainfall, potential evapotranspiration, and structure flow multipliers) absorb information that is present in structural noise that may otherwise contaminate the estimation of more physically-based model parameters. This allows greater precision in predictions that are entirely solution-space dependent, and reduces the propensity for bias in predictions that are not. Results show that application of this analysis is an effective means of identifying those surface-water and

Strategies to reduce the risk of drug-induced QT interval prolongation: a pharmaceutical company perspective.

PubMed

Pollard, C E; Valentin, J-P; Hammond, T G

2008-08-01

Drug-induced prolongation of the QT interval is having a significant impact on the ability of the pharmaceutical industry to develop new drugs. The development implications for a compound causing a significant effect in the 'Thorough QT/QTc Study' -- as defined in the clinical regulatory guidance (ICH E14) -- are substantial. In view of this, and the fact that QT interval prolongation is linked to direct inhibition of the hERG channel, in the early stages of drug discovery the focus is on testing for and screening out hERG activity. This has led to understanding of how to produce low potency hERG blockers whilst retaining desirable properties. Despite this, a number of factors mean that when an integrated risk assessment is generated towards the end of the discovery phase (by conducting at least an in vivo QT assessment) a QT interval prolongation risk is still often apparent; inhibition of hERG channel trafficking and partitioning into cardiac tissue are just two confounding factors. However, emerging information suggests that hERG safety margins have high predictive value and that when hERG and in vivo non-clinical data are combined, their predictive value to man, whilst not perfect, is >80%. Although understanding the anomalies is important and is being addressed, of greater importance is developing a better understanding of TdP, with the aim of being able to predict TdP rather than using an imperfect surrogate marker (QT interval prolongation). Without an understanding of how to predict TdP risk, high-benefit drugs for serious indications may never be marketed.

A method to predict equilibrium conditions of gas hydrate formation in porous media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clarke, M.A.; Pooladi-Darvish, M.; Bishnoi, P.R.

1999-06-01

In the petroleum industry, it is desirable to avoid the formation of gas hydrates. When gas hydrates form, they tend to agglomerate and block pipelines and process equipment. However, naturally occurring gas hydrates that form in the permafrost region or in deep oceans represent a vast untouched natural gas reserve. Although the exact amount of gas in the hydrate form is not known, it is believed to be comparable to the known amount of gas in the free state. Numerous methods for the recovery of natural gas from hydrate fields have been proposed. These techniques include thermal decomposition, depressurization, andmore » chemical injection. To fully exploit hydrate reserves, it will be necessary to know the decomposition/formation conditions of the gas hydrate in porous media. A predictive model has been developed to determine the incipient hydrate formation conditions in porous media. The only additional information that is needed to determine the incipient hydrate formation conditions is the pore radius, surface energy per unit area, and wetting angle. It was found that the model performed well in predicting the experimental data of Handa and Stupin.« less

Predicting non-stationary algal dynamics following changes in hydrometeorological conditions using data assimilation techniques

NASA Astrophysics Data System (ADS)

Kim, S.; Seo, D. J.

2017-12-01

When water temperature (TW) increases due to changes in hydrometeorological conditions, the overall ecological conditions change in the aquatic system. The changes can be harmful to human health and potentially fatal to fish habitat. Therefore, it is important to assess the impacts of thermal disturbances on in-stream processes of water quality variables and be able to predict effectiveness of possible actions that may be taken for water quality protection. For skillful prediction of in-stream water quality processes, it is necessary for the watershed water quality models to be able to reflect such changes. Most of the currently available models, however, assume static parameters for the biophysiochemical processes and hence are not able to capture nonstationaries seen in water quality observations. In this work, we assess the performance of the Hydrological Simulation Program-Fortran (HSPF) in predicting algal dynamics following TW increase. The study area is located in the Republic of Korea where waterway change due to weir construction and drought concurrently occurred around 2012. In this work we use data assimilation (DA) techniques to update model parameters as well as the initial condition of selected state variables for in-stream processes relevant to algal growth. For assessment of model performance and characterization of temporal variability, various goodness-of-fit measures and wavelet analysis are used.

Confidence Interval Coverage for Cohen's Effect Size Statistic

ERIC Educational Resources Information Center

Algina, James; Keselman, H. J.; Penfield, Randall D.

2006-01-01

Kelley compared three methods for setting a confidence interval (CI) around Cohen's standardized mean difference statistic: the noncentral-"t"-based, percentile (PERC) bootstrap, and biased-corrected and accelerated (BCA) bootstrap methods under three conditions of nonnormality, eight cases of sample size, and six cases of population…

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. Copyright © 2016 the American Physiological Society.

The Processing of Attended and Predicted Sounds in Time.

PubMed

Paris, Tim; Kim, Jeesun; Davis, Chris

2016-01-01

Neural responses to an attended event are typically enhanced relative to those from an unattended one (attention enhancement). Conversely, neural responses to a predicted event are typically reduced relative to those from an unpredicted one (prediction suppression). What remains to be established is what happens with attended and predicted events. To examine the interaction between attention and prediction, we combined two robust paradigms developed for studying attention and prediction effects on ERPs into an orthogonal design. Participants were presented with sounds in attended or unattended intervals with onsets that were either predicted by a moving visual cue or unpredicted (no cue was provided). We demonstrated an N1 enhancement effect for attended sounds and an N1 suppression effect for predicted sounds; furthermore, an interaction between these effects was found that emerged early in the N1 (50-95 msec), indicating that attention enhancement only occurred when the sound was unpredicted. This pattern of results can be explained by the precision of the predictive cue that reduces the need for attention selection in the attended and predicted condition.

Fire management strategies to maintain species population processes in a fragmented landscape of fire-interval extremes.

PubMed

Tulloch, Ayesha I T; Pichancourt, Jean-Baptiste; Gosper, Carl R; Sanders, Angela; Chadès, Iadine

2016-10-01

Changed fire regimes have led to declines of fire-regime-adapted species and loss of biodiversity globally. Fire affects population processes of growth, reproduction, and dispersal in different ways, but there is little guidance about the best fire regime(s) to maintain species population processes in fire-prone ecosystems. We use a process-based approach to determine the best range of fire intervals for keystone plant species in a highly modified Mediterranean ecosystem in southwestern Australia where current fire regimes vary. In highly fragmented areas, fires are few due to limited ignitions and active suppression of wildfire on private land, while in highly connected protected areas fires are frequent and extensive. Using matrix population models, we predict population growth of seven Banksia species under different environmental conditions and patch connectivity, and evaluate the sensitivity of species survival to different fire management strategies and burning intervals. We discover that contrasting, complementary patterns of species life-histories with time since fire result in no single best fire regime. All strategies result in the local patch extinction of at least one species. A small number of burning strategies secure complementary species sets depending on connectivity and post-fire growing conditions. A strategy of no fire always leads to fewer species persisting than prescribed fire or random wildfire, while too-frequent or too-rare burning regimes lead to the possible local extinction of all species. In low landscape connectivity, we find a smaller range of suitable fire intervals, and strategies of prescribed or random burning result in a lower number of species with positive growth rates after 100 years on average compared with burning high connectivity patches. Prescribed fire may reduce or increase extinction risk when applied in combination with wildfire depending on patch connectivity. Poor growing conditions result in a significantly

System implications of the ambulance arrival-to-patient contact interval on response interval compliance.

PubMed

Campbell, J P; Gratton, M C; Salomone, J A; Lindholm, D J; Watson, W A

1994-01-01

In some emergency medical services (EMS) system designs, response time intervals are mandated with monetary penalties for noncompliance. These times are set with the goal of providing rapid, definitive patient care. The time interval of vehicle at scene-to-patient access (VSPA) has been measured, but its effect on response time interval compliance has not been determined. To determine the effect of the VSPA interval on the mandated code 1 (< 9 min) and code 2 (< 13 min) response time interval compliance in an urban, public-utility model system. A prospective, observational study used independent third-party riders to collect the VSPA interval for emergency life-threatening (code 1) and emergency nonlife-threatening (code 2) calls. The VSPA interval was added to the 9-1-1 call-to-dispatch and vehicle dispatch-to-scene intervals to determine the total time interval from call received until paramedic access to the patient (9-1-1 call-to-patient access). Compliance with the mandated response time intervals was determined using the traditional time intervals (9-1-1 call-to-scene) plus the VSPA time intervals (9-1-1 call-to-patient access). Chi-square was used to determine statistical significance. Of the 216 observed calls, 198 were matched to the traditional time intervals. Sixty-three were code 1, and 135 were code 2. Of the code 1 calls, 90.5% were compliant using 9-1-1 call-to-scene intervals dropping to 63.5% using 9-1-1 call-to-patient access intervals (p < 0.0005). Of the code 2 calls, 94.1% were compliant using 9-1-1 call-to-scene intervals. Compliance decreased to 83.7% using 9-1-1 call-to-patient access intervals (p = 0.012). The addition of the VSPA interval to the traditional time intervals impacts system response time compliance. Using 9-1-1 call-to-scene compliance as a basis for measuring system performance underestimates the time for the delivery of definitive care. This must be considered when response time interval compliances are defined.

Bone turnover marker reference intervals in young females.

PubMed

Callegari, Emma T; Gorelik, Alexandra; Garland, Suzanne M; Chiang, Cherie Y; Wark, John D

2017-07-01

Background The use of bone turnover markers in clinical practice and research in younger people is limited by the lack of normative data and understanding of common causes of variation in bone turnover marker values in this demographic. To appropriately interpret bone turnover markers, robust reference intervals specific to age, development and sex are necessary. This study aimed to determine reference intervals of bone turnover markers in females aged 16-25 years participating in the Safe-D study. Methods Participants were recruited through social networking site Facebook and were asked to complete an extensive, online questionnaire and attend a site visit. Participants were tested for serum carboxy-terminal cross-linking telopeptide of type 1 collagen and total procollagen type 1 N-propeptide using the Roche Elecsys automated analyser. Reference intervals were determined using the 2.5th to 97.5th percentiles of normalized bone turnover marker values. Results Of 406 participants, 149 were excluded due to medical conditions or medication use (except hormonal contraception) which may affect bone metabolism. In the remaining 257 participants, the reference interval was 230-1000 ng/L for serum carboxy-terminal cross-linking telopeptide of type 1 collagen and 27-131  µg/L for procollagen type 1 N-propeptide. Both marker concentrations were inversely correlated with age and oral contraceptive pill use. Therefore, intervals specific to these variables were calculated. Conclusions We defined robust reference intervals for cross-linking telopeptide of type 1 collagen and procollagen type 1 N-propeptide in young females grouped by age and contraceptive pill use. We examined bone turnover markers' relationship with several lifestyle, clinical and demographic factors. Our normative intervals should aid interpretation of bone turnover markers in young females particularly in those aged 16 to 19 years where reference intervals are currently provisional.

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

PubMed

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

2014-06-16

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

Reduced short interval cortical inhibition correlates with atomoxetine response in children with attention-deficit hyperactivity disorder (ADHD).

PubMed

Chen, Tina H; Wu, Steve W; Welge, Jeffrey A; Dixon, Stephan G; Shahana, Nasrin; Huddleston, David A; Sarvis, Adam R; Sallee, Floyd R; Gilbert, Donald L

2014-12-01

Clinical trials in children with attention-deficit hyperactivity disorder (ADHD) show variability in behavioral responses to the selective norepinephrine reuptake inhibitor atomoxetine. The objective of this study was to determine whether transcranial magnetic stimulation-evoked short interval cortical inhibition might be a biomarker predicting, or correlating with, clinical atomoxetine response. At baseline and after 4 weeks of atomoxetine treatment in 7- to 12-year-old children with ADHD, transcranial magnetic stimulation short interval cortical inhibition was measured, blinded to clinical improvement. Primary analysis was by multivariate analysis of covariance. Baseline short interval cortical inhibition did not predict clinical responses. However, paradoxically, after 4 weeks of atomoxetine, mean short interval cortical inhibition was reduced 31.9% in responders and increased 6.1% in nonresponders (analysis of covariance t 41 = 2.88; P = .0063). Percentage reductions in short interval cortical inhibition correlated with reductions in the ADHD Rating Scale (r = 0.50; P = .0005). In children ages 7 to 12 years with ADHD treated with atomoxetine, improvements in clinical symptoms are correlated with reductions in motor cortex short interval cortical inhibition. © The Author(s) 2014.

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.,…

Orders on Intervals Over Partially Ordered Sets: Extending Allen's Algebra and Interval Graph Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zapata, Francisco; Kreinovich, Vladik; Joslyn, Cliff A.

2013-08-01

To make a decision, we need to compare the values of quantities. In many practical situations, we know the values with interval uncertainty. In such situations, we need to compare intervals. Allen’s algebra describes all possible relations between intervals on the real line, and ordering relations between such intervals are well studied. In this paper, we extend this description to intervals in an arbitrary partially ordered set (poset). In particular, we explicitly describe ordering relations between intervals that generalize relation between points. As auxiliary results, we provide a logical interpretation of the relation between intervals, and extend the results aboutmore » interval graphs to intervals over posets.« less

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

2015-01-01

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.

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

Treesearch

R. H. Weidman

1923-01-01

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

Interval Graph Limits

PubMed Central

Diaconis, Persi; Holmes, Susan; Janson, Svante

2015-01-01

We work out a graph limit theory for dense interval graphs. The theory developed departs from the usual description of a graph limit as a symmetric function W (x, y) on the unit square, with x and y uniform on the interval (0, 1). Instead, we fix a W and change the underlying distribution of the coordinates x and y. We find choices such that our limits are continuous. Connections to random interval graphs are given, including some examples. We also show a continuity result for the chromatic number and clique number of interval graphs. Some results on uniqueness of the limit description are given for general graph limits. PMID:26405368

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

PubMed

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

2014-12-01

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

Fixed-interval performance and self-control in infants.

PubMed Central

Darcheville, J C; Rivière, V; Wearden, J H

1993-01-01

Twenty-six infants, 3 to 23 months old, were trained on fixed-interval schedules ranging from 10 s to 80 s. The operant response was touching an illuminated location on a touch-sensitive screen, and 20 s of cartoon presentation was the reinforcer. The subjects were also trained in a six-phase self-control procedure in which the critical phases involved choice between 20 s of cartoon available after a 0.5-s delay (impulsive choice) and 40 s of cartoon delayed for 40 s (self-controlled choice). All the youngest children (3 to 5 months) showed long postreinforcement pauses on the fixed-interval schedule, with most intervals involving the emission of a single, reinforced, response, and all made self-controlled choices. Older subjects (9 to 23 months) either produced the same pattern as the younger ones on the fixed-interval schedule (classified as pause-sensitive subjects) or produced short pauses and higher steady response rates (classified as pause-insensitive subjects). All pause-sensitive subjects made self-controlled choices in the self-control condition, and all pause-insensitive subjects made impulsive ones. PMID:8409821

Effect of different rest intervals after whole-body vibration on vertical jump performance.

PubMed

Dabbs, Nicole C; Muñoz, Colleen X; Tran, Tai T; Brown, Lee E; Bottaro, Martim

2011-03-01

Whole-body vibration (WBV) may potentiate vertical jump (VJ) performance via augmented muscular strength and motor function. The purpose of this study was to evaluate the effect of different rest intervals after WBV on VJ performance. Thirty recreationally trained subjects (15 men and 15 women) volunteered to participate in 4 testing visits separated by 24 hours. Visit 1 acted as a familiarization visit where subjects were introduced to the VJ and WBV protocols. Visits 2-4 contained 2 randomized conditions per visit with a 10-minute rest period between conditions. The WBV was administered on a pivotal platform with a frequency of 30 Hz and an amplitude of 6.5 mm in 4 bouts of 30 seconds for a total of 2 minutes with 30 seconds of rest between bouts. During WBV, subjects performed a quarter squat every 5 seconds, simulating a countermovement jump (CMJ). Whole-body vibration was followed by 3 CMJs with 5 different rest intervals: immediate, 30 seconds, 1 minute, 2 minutes, or 4 minutes. For a control condition, subjects performed squats with no WBV. There were no significant (p > 0.05) differences in peak velocity or relative ground reaction force after WBV rest intervals. However, results of VJ height revealed that maximum values, regardless of rest interval (56.93 ± 13.98 cm), were significantly (p < 0.05) greater than the control condition (54.44 ± 13.74 cm). Therefore, subjects' VJ height potentiated at different times after WBV suggesting strong individual differences in optimal rest interval. Coaches may use WBV to enhance acute VJ performance but should first identify each individual's optimal rest time to maximize the potentiating effects.

Organic-rich shale lithofacies geophysical prediction: A case study in the fifth organic-matter-rich interval of Paleogene Hetaoyuan Formation, Biyang Depression

NASA Astrophysics Data System (ADS)

Fei, S.; Xinong, X.

2017-12-01

The fifth organic-matter-rich interval (ORI 5) in the He-third Member of the Paleogene Hetaoyuan Formation is believed to be the main exploration target for shale oil in Biyang Depression, eastern China. An important part of successful explorating and producing shale oil is to identify and predict organic-rich shale lithofacies with different reservoir capacities and rock geomechanical properties, which are related to organic matter content and mineral components. In this study, shale lithofacies are defined by core analysis data, well-logging and seismic data, and the spatial-temporal distribution of various lithologies are predicted qualitatively by seismic attribute technology and quantitatively by geostatistical inversion analysis, and the prediction results are confirmed by the logging data and geological background. ORI 5 is present in lacustrine expanding system tract and can be further divided into four parasequence sets based on the analysis of conventional logs, TOC content and wavelet transform. Calcareous shale, dolomitic shale, argillaceous shale, silty shale and muddy siltstone are defined within ORI 5, and can be separated and predicted in regional-scale by root mean square amplitude (RMS) analysis and wave impedance. The results indicate that in the early expansion system tract, dolomitic shale and calcareous shale widely developed in the study area, and argillaceous shale, silty shale, and muddy siltstone only developed in periphery of deep depression. With the lake level rising, argillaceous shale and calcareous shale are well developed, and argillaceous shale interbeded with silty shale or muddy siltstone developed in deep or semi-deep lake. In the late expansion system tract, argillaceous shale is widely deposited in the deepest depression, calcareous shale presented band distribution in the east of the depression. Actual test results indicate that these methods are feasible to predict the spatial distribution of shale lithofacies.

Reliability of Degree-Day Models to Predict the Development Time of Plutella xylostella (L.) under Field Conditions.

PubMed

Marchioro, C A; Krechemer, F S; de Moraes, C P; Foerster, L A

2015-12-01

The diamondback moth, Plutella xylostella (L.), is a cosmopolitan pest of brassicaceous crops occurring in regions with highly distinct climate conditions. Several studies have investigated the relationship between temperature and P. xylostella development rate, providing degree-day models for populations from different geographical regions. However, there are no data available to date to demonstrate the suitability of such models to make reliable projections on the development time for this species in field conditions. In the present study, 19 models available in the literature were tested regarding their ability to accurately predict the development time of two cohorts of P. xylostella under field conditions. Only 11 out of the 19 models tested accurately predicted the development time for the first cohort of P. xylostella, but only seven for the second cohort. Five models correctly predicted the development time for both cohorts evaluated. Our data demonstrate that the accuracy of the models available for P. xylostella varies widely and therefore should be used with caution for pest management purposes.

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

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

USDA-ARS?s Scientific Manuscript database

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

Investigating fluvial pattern and delta-planform geometry based on varying intervals of flood and interflood

NASA Astrophysics Data System (ADS)

Rambo, J. E.; Kim, W.; Miller, K.

2017-12-01

Physical modeling of a delta's evolution can represent how changing the intervals of flood and interflood can alter a delta's fluvial pattern and geometry. Here we present a set of six experimental runs in which sediment and water were discharged at constant rates over each experiment. During the "flood" period, both sediment and water were discharged at rates of 0.25 cm3/s and 15 ml/s respectively, and during the "interflood" period, only water was discharged at 7.5 ml/s. The flood periods were only run for 30 minutes to keep the total volume of sediment constant. Run 0 did not have an interflood period and therefore ran with constant sediment and water discharge for the duration of the experiment.The other five runs had either 5, 10, or 15-min intervals of flood with 5, 10, or 15-min intervals of interflood. The experimental results show that Run 0 had the smallest topset area. This is due to a lack of surface reworking that takes place during interflood periods. Run 1 had 15-minute intervals of flood and 15-minute intervals of interflood, and it had the largest topset area. Additionally, the experiments that had longer intervals of interflood than flood had more elongated delta geometries. Wetted fraction color maps were also created to plot channel locations during each run. The maps show that the runs with longer interflood durations had channels occurring predominantly down the middle with stronger incisions; these runs produced deltas with more elongated geometries. When the interflood duration was even longer, however, strong channels started to occur at multiple locations. This increased interflood period allowed for the entire area over the delta's surface to be reworked, thus reducing the downstream slope and allowing channels to be more mobile laterally. Physical modeling of a delta allows us to predict a delta's resulting geometry given a set of conditions. This insight is needed especially with delta's being the home to many populations of people and

Lifecourse social conditions and racial disparities in incidence of first stroke.

PubMed

Glymour, M Maria; Avendaño, Mauricio; Haas, Steven; Berkman, Lisa F

2008-12-01

Some previous studies found excess stroke rates among black subjects persisted after adjustment for socioeconomic status (SES), fueling speculation regarding racially patterned genetic predispositions to stroke. Previous research was hampered by incomplete SES assessments, without measures of childhood conditions or adult wealth. We assess the role of lifecourse SES in explaining stroke risk and stroke disparities. Health and Retirement Study participants age 50+ (n = 20,661) were followed on average 9.9 years for self- or proxy-reported first stroke (2175 events). Childhood social conditions (southern state of birth, parental SES, self-reported fair/poor childhood health, and attained height), adult SES (education, income, wealth, and occupational status) and traditional cardiovascular risk factors were used to predict first stroke onset using Cox proportional hazards models. Black subjects had a 48% greater risk of first stroke incidence than whites (95% confidence interval, 1.33-1.65). Childhood conditions predicted stroke risk in both blacks and whites, independently of adult SES. Adjustment for both childhood social conditions and adult SES measures attenuated racial differences to marginal significance (hazard ratio, 1.13; 95% CI, 1.00-1.28). Childhood social conditions predict stroke risk in black and White American adults. Additional adjustment for adult SES, in particular wealth, nearly eliminated the disparity in stroke risk between black and white subjects.

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

USGS Publications Warehouse

Deviney, Frank A.; Rice, Karen C.; Hornberger, George 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.

Serial binary interval ratios improve rhythm reproduction.

PubMed

Wu, Xiang; Westanmo, Anders; Zhou, Liang; Pan, Junhao

2013-01-01

Musical rhythm perception is a natural human ability that involves complex cognitive processes. Rhythm refers to the organization of events in time, and musical rhythms have an underlying hierarchical metrical structure. The metrical structure induces the feeling of a beat and the extent to which a rhythm induces the feeling of a beat is referred to as its metrical strength. Binary ratios are the most frequent interval ratio in musical rhythms. Rhythms with hierarchical binary ratios are better discriminated and reproduced than rhythms with hierarchical non-binary ratios. However, it remains unclear whether a superiority of serial binary over non-binary ratios in rhythm perception and reproduction exists. In addition, how different types of serial ratios influence the metrical strength of rhythms remains to be elucidated. The present study investigated serial binary vs. non-binary ratios in a reproduction task. Rhythms formed with exclusively binary (1:2:4:8), non-binary integer (1:3:5:6), and non-integer (1:2.3:5.3:6.4) ratios were examined within a constant meter. The results showed that the 1:2:4:8 rhythm type was more accurately reproduced than the 1:3:5:6 and 1:2.3:5.3:6.4 rhythm types, and the 1:2.3:5.3:6.4 rhythm type was more accurately reproduced than the 1:3:5:6 rhythm type. Further analyses showed that reproduction performance was better predicted by the distribution pattern of event occurrences within an inter-beat interval, than by the coincidence of events with beats, or the magnitude and complexity of interval ratios. Whereas rhythm theories and empirical data emphasize the role of the coincidence of events with beats in determining metrical strength and predicting rhythm performance, the present results suggest that rhythm processing may be better understood when the distribution pattern of event occurrences is taken into account. These results provide new insights into the mechanisms underlining musical rhythm perception.

Serial binary interval ratios improve rhythm reproduction

PubMed Central

Wu, Xiang; Westanmo, Anders; Zhou, Liang; Pan, Junhao

2013-01-01

Musical rhythm perception is a natural human ability that involves complex cognitive processes. Rhythm refers to the organization of events in time, and musical rhythms have an underlying hierarchical metrical structure. The metrical structure induces the feeling of a beat and the extent to which a rhythm induces the feeling of a beat is referred to as its metrical strength. Binary ratios are the most frequent interval ratio in musical rhythms. Rhythms with hierarchical binary ratios are better discriminated and reproduced than rhythms with hierarchical non-binary ratios. However, it remains unclear whether a superiority of serial binary over non-binary ratios in rhythm perception and reproduction exists. In addition, how different types of serial ratios influence the metrical strength of rhythms remains to be elucidated. The present study investigated serial binary vs. non-binary ratios in a reproduction task. Rhythms formed with exclusively binary (1:2:4:8), non-binary integer (1:3:5:6), and non-integer (1:2.3:5.3:6.4) ratios were examined within a constant meter. The results showed that the 1:2:4:8 rhythm type was more accurately reproduced than the 1:3:5:6 and 1:2.3:5.3:6.4 rhythm types, and the 1:2.3:5.3:6.4 rhythm type was more accurately reproduced than the 1:3:5:6 rhythm type. Further analyses showed that reproduction performance was better predicted by the distribution pattern of event occurrences within an inter-beat interval, than by the coincidence of events with beats, or the magnitude and complexity of interval ratios. Whereas rhythm theories and empirical data emphasize the role of the coincidence of events with beats in determining metrical strength and predicting rhythm performance, the present results suggest that rhythm processing may be better understood when the distribution pattern of event occurrences is taken into account. These results provide new insights into the mechanisms underlining musical rhythm perception. PMID:23964258

Psychosocial and nonclinical factors predicting hospital utilization in patients of a chronic disease management program: a prospective observational study.

PubMed

Tran, Mark W; Weiland, Tracey J; Phillips, Georgina A

2015-01-01

Psychosocial factors such as marital status (odds ratio, 3.52; 95% confidence interval, 1.43-8.69; P = .006) and nonclinical factors such as outpatient nonattendances (odds ratio, 2.52; 95% confidence interval, 1.22-5.23; P = .013) and referrals made (odds ratio, 1.20; 95% confidence interval, 1.06-1.35; P = .003) predict hospital utilization for patients in a chronic disease management program. Along with optimizing patients' clinical condition by prescribed medical guidelines and supporting patient self-management, addressing psychosocial and nonclinical issues are important in attempting to avoid hospital utilization for people with chronic illnesses.

Chosen interval methods for solving linear interval systems with special type of matrix

NASA Astrophysics Data System (ADS)

Szyszka, Barbara

2013-10-01

The paper is devoted to chosen direct interval methods for solving linear interval systems with special type of matrix. This kind of matrix: band matrix with a parameter, from finite difference problem is obtained. Such linear systems occur while solving one dimensional wave equation (Partial Differential Equations of hyperbolic type) by using the central difference interval method of the second order. Interval methods are constructed so as the errors of method are enclosed in obtained results, therefore presented linear interval systems contain elements that determining the errors of difference method. The chosen direct algorithms have been applied for solving linear systems because they have no errors of method. All calculations were performed in floating-point interval arithmetic.

Prediction and Monitoring of Monsoon Intraseasonal Oscillations over Indian Monsoon Region in an Ensemble Prediction System using CFSv2

NASA Astrophysics Data System (ADS)

Borah, Nabanita; Sukumarpillai, Abhilash; Sahai, Atul Kumar; Chattopadhyay, Rajib; Joseph, Susmitha; De, Soumyendu; Nath Goswami, Bhupendra; Kumar, Arun

2014-05-01

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using NCEP Climate Forecast System model version2 at T126 horizontal resolution. The EPS is formulated by producing 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio becomes unity by about18 days and the predictability error saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are observed even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of amplitude of large scale MISO as well as the initial conditions related to the different phases of MISO. Categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

Prediction and Monitoring of Monsoon Intraseasonal Oscillations over Indian Monsoon Region in an Ensemble Prediction System using CFSv2

NASA Astrophysics Data System (ADS)

Borah, N.; Abhilash, S.; Sahai, A. K.; Chattopadhyay, R.; Joseph, S.; Sharmila, S.; de, S.; Goswami, B.; Kumar, A.

2013-12-01

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISOs) of Indian summer monsoon (ISM) using NCEP Climate Forecast System model version2 at T126 horizontal resolution. The EPS is formulated by producing 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio becomes unity by about18 days and the predictability error saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are observed even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of amplitude of large scale MISO as well as the initial conditions related to the different phases of MISO. Categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

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

PubMed Central

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

2017-01-01

Objectives Interval colorectal cancer (CRC) after colonoscopy may affect effectiveness and cost-effectiveness of screening programmes. We aimed to investigate whether and how faecal haemoglobin concentration (FHbC) of faecal immunochemical testing (FIT) affected the risk prediction of interval cancer (IC) caused by inadequate colonoscopy quality in a FIT-based population screening programme. Design From 2004 to 2009, 29 969 subjects underwent complete colonoscopy after positive FIT in the Taiwanese Nationwide CRC Screening Program. The IC rate was traced until the end of 2012. The incidence of IC was calculated in relation to patient characteristics, endoscopy-related factors (such adenoma detection rate (ADR)) and FHbC. Poisson regression analysis was performed to assess the potential risk factors for colonoscopy IC. Results One hundred and sixty-two ICs developed after an index colonoscopy and the estimated incidence was 1.14 per 1000 person-years of observation for the entire cohort. Increased risk of IC was most remarkable in the uptake of colonoscopy in settings with ADR lower than 15% (adjusted relative risk (aRR)=3.09, 95% CI 1.55 to 6.18) and then higher FHbC (μg Hb/g faeces) (100–149: aRR=2.55, 95% CI 1.52 to 4.29, ≥150: aRR=2.74, 95% CI 1.84 to 4.09) with adjustment for older age and colorectal neoplasm detected at baseline colonoscopy. Similar findings were observed for subjects with negative index colonoscopy. Conclusions Colonoscopy ICs arising from FIT-based population screening programmes were mainly influenced by inadequate colonoscopy quality and independently predicted by FHbC that is associated with a priori chance of advanced neoplasm. This finding is helpful for future modification of screening logistics based on FHbC. PMID:26515543

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.

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.

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

DTIC Science & Technology

2012-10-01

Grade and 812 D-Grade Sofnolime.3 Definitions According to Devore,4 A CI (confidence interval) refers to a parameter, or population ... characteristic , whose value is fixed but unknown to us. In contrast, a future value of Y is not a parameter but instead a random variable; for this

Control of Angular Intervals for Angle-Multiplexed Holographic Memory

NASA Astrophysics Data System (ADS)

Kinoshita, Nobuhiro; Muroi, Tetsuhiko; Ishii, Norihiko; Kamijo, Koji; Shimidzu, Naoki

2009-03-01

In angle-multiplexed holographic memory, the full width at half maximum of the Bragg selectivity curves is dependent on the angle formed between the medium and incident laser beams. This indicates the possibility of high density and high multiplexing number by varying the angular intervals between adjacent holograms. We propose an angular interval scheduling for closely stacking holograms into medium even when the angle range is limited. We obtained bit error rates of the order of 10-4 under the following conditions: medium thickness of 1 mm, laser beam wavelength of 532 nm, and angular multiplexing number of 300.

Improved central confidence intervals for the ratio of Poisson means

NASA Astrophysics Data System (ADS)

Cousins, R. D.

The problem of confidence intervals for the ratio of two unknown Poisson means was "solved" decades ago, but a closer examination reveals that the standard solution is far from optimal from the frequentist point of view. We construct a more powerful set of central confidence intervals, each of which is a (typically proper) subinterval of the corresponding standard interval. They also provide upper and lower confidence limits which are more restrictive than the standard limits. The construction follows Neyman's original prescription, though discreteness of the Poisson distribution and the presence of a nuisance parameter (one of the unknown means) lead to slightly conservative intervals. Philosophically, the issue of the appropriateness of the construction method is similar to the issue of conditioning on the margins in 2×2 contingency tables. From a frequentist point of view, the new set maintains (over) coverage of the unknown true value of the ratio of means at each stated confidence level, even though the new intervals are shorter than the old intervals by any measure (except for two cases where they are identical). As an example, when the number 2 is drawn from each Poisson population, the 90% CL central confidence interval on the ratio of means is (0.169, 5.196), rather than (0.108, 9.245). In the cited literature, such confidence intervals have applications in numerous branches of pure and applied science, including agriculture, wildlife studies, manufacturing, medicine, reliability theory, and elementary particle physics.

Image discrimination models predict detection in fixed but not random noise

NASA Technical Reports Server (NTRS)

Ahumada, A. J. Jr; Beard, B. L.; Watson, A. B. (Principal Investigator)

1997-01-01

By means of a two-interval forced-choice procedure, contrast detection thresholds for an aircraft positioned on a simulated airport runway scene were measured with fixed and random white-noise masks. The term fixed noise refers to a constant, or unchanging, noise pattern for each stimulus presentation. The random noise was either the same or different in the two intervals. Contrary to simple image discrimination model predictions, the same random noise condition produced greater masking than the fixed noise. This suggests that observers seem unable to hold a new noisy image for comparison. Also, performance appeared limited by internal process variability rather than by external noise variability, since similar masking was obtained for both random noise types.

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

PubMed

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

2016-01-01

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

Interval Predictor Models for Data with Measurement Uncertainty

NASA Technical Reports Server (NTRS)

Lacerda, Marcio J.; Crespo, Luis G.

2017-01-01

An interval predictor model (IPM) is a computational model that predicts the range of an output variable given input-output data. This paper proposes strategies for constructing IPMs based on semidefinite programming and sum of squares (SOS). The models are optimal in the sense that they yield an interval valued function of minimal spread containing all the observations. Two different scenarios are considered. The first one is applicable to situations where the data is measured precisely whereas the second one is applicable to data subject to known biases and measurement error. In the latter case, the IPMs are designed to fully contain regions in the input-output space where the data is expected to fall. Moreover, we propose a strategy for reducing the computational cost associated with generating IPMs as well as means to simulate them. Numerical examples illustrate the usage and performance of the proposed formulations.

Inverse analysis and regularisation in conditional source-term estimation modelling

NASA Astrophysics Data System (ADS)

Labahn, Jeffrey W.; Devaud, Cecile B.; Sipkens, Timothy A.; Daun, Kyle J.

2014-05-01

Conditional Source-term Estimation (CSE) obtains the conditional species mass fractions by inverting a Fredholm integral equation of the first kind. In the present work, a Bayesian framework is used to compare two different regularisation methods: zeroth-order temporal Tikhonov regulatisation and first-order spatial Tikhonov regularisation. The objectives of the current study are: (i) to elucidate the ill-posedness of the inverse problem; (ii) to understand the origin of the perturbations in the data and quantify their magnitude; (iii) to quantify the uncertainty in the solution using different priors; and (iv) to determine the regularisation method best suited to this problem. A singular value decomposition shows that the current inverse problem is ill-posed. Perturbations to the data may be caused by the use of a discrete mixture fraction grid for calculating the mixture fraction PDF. The magnitude of the perturbations is estimated using a box filter and the uncertainty in the solution is determined based on the width of the credible intervals. The width of the credible intervals is significantly reduced with the inclusion of a smoothing prior and the recovered solution is in better agreement with the exact solution. The credible intervals for temporal and spatial smoothing are shown to be similar. Credible intervals for temporal smoothing depend on the solution from the previous time step and a smooth solution is not guaranteed. For spatial smoothing, the credible intervals are not dependent upon a previous solution and better predict characteristics for higher mixture fraction values. These characteristics make spatial smoothing a promising alternative method for recovering a solution from the CSE inversion process.

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.

Emotional arousal predicts intertemporal choice

PubMed Central

Lempert, Karolina M.; Johnson, Eli; Phelps, Elizabeth A.

2016-01-01

People generally prefer immediate rewards to rewards received after a delay, often even when the delayed reward is larger. This phenomenon is known as temporal discounting. It has been suggested that preferences for immediate rewards may be due to their being more concrete than delayed rewards. This concreteness may evoke an enhanced emotional response. Indeed, manipulating the representation of a future reward to make it more concrete has been shown to heighten the reward’s subjective emotional intensity, making people more likely to choose it. Here we use an objective measure of arousal – pupil dilation – to investigate if emotional arousal mediates the influence of delayed reward concreteness on choice. We recorded pupil dilation responses while participants made choices between immediate and delayed rewards. We manipulated concreteness through time interval framing: delayed rewards were presented either with the date on which they would be received (e.g., “$30, May 3”; DATE condition, more concrete) or in terms of delay to receipt (e.g., “$30, 7 days; DAYS condition, less concrete). Contrary to prior work, participants were not overall more patient in the DATE condition. However, there was individual variability in response to time framing, and this variability was predicted by differences in pupil dilation between conditions. Emotional arousal increased as the subjective value of delayed rewards increased, and predicted choice of the delayed reward on each trial. This study advances our understanding of the role of emotion in temporal discounting. PMID:26882337

Fault detection for discrete-time LPV systems using interval observers

NASA Astrophysics Data System (ADS)

Zhang, Zhi-Hui; Yang, Guang-Hong

2017-10-01

This paper is concerned with the fault detection (FD) problem for discrete-time linear parameter-varying systems subject to bounded disturbances. A parameter-dependent FD interval observer is designed based on parameter-dependent Lyapunov and slack matrices. The design method is presented by translating the parameter-dependent linear matrix inequalities (LMIs) into finite ones. In contrast to the existing results based on parameter-independent and diagonal Lyapunov matrices, the derived disturbance attenuation, fault sensitivity and nonnegative conditions lead to less conservative LMI characterisations. Furthermore, without the need to design the residual evaluation functions and thresholds, the residual intervals generated by the interval observers are used directly for FD decision. Finally, simulation results are presented for showing the effectiveness and superiority of the proposed method.

Humidity-corrected Arrhenius equation: The reference condition approach.

PubMed

Naveršnik, Klemen; Jurečič, Rok

2016-03-16

Accelerated and stress stability data is often used to predict shelf life of pharmaceuticals. Temperature, combined with humidity accelerates chemical decomposition and the Arrhenius equation is used to extrapolate accelerated stability results to long-term stability. Statistical estimation of the humidity-corrected Arrhenius equation is not straightforward due to its non-linearity. A two stage nonlinear fitting approach is used in practice, followed by a prediction stage. We developed a single-stage statistical procedure, called the reference condition approach, which has better statistical properties (less collinearity, direct estimation of uncertainty, narrower prediction interval) and is significantly easier to use, compared to the existing approaches. Our statistical model was populated with data from a 35-day stress stability study on a laboratory batch of vitamin tablets and required mere 30 laboratory assay determinations. The stability prediction agreed well with the actual 24-month long term stability of the product. The approach has high potential to assist product formulation, specification setting and stability statements. Copyright © 2016 Elsevier B.V. All rights reserved.

Conditioned pain modulation predicts duloxetine efficacy in painful diabetic neuropathy.

PubMed

Yarnitsky, David; Granot, Michal; Nahman-Averbuch, Hadas; Khamaisi, Mogher; Granovsky, Yelena

2012-06-01

This study aims to individualize the selection of drugs for neuropathic pain by examining the potential coupling of a given drug's mechanism of action with the patient's pain modulation pattern. The latter is assessed by the conditioned pain modulation (CPM) and temporal summation (TS) protocols. We hypothesized that patients with a malfunctioning pain modulation pattern, such as less efficient CPM, would benefit more from drugs augmenting descending inhibitory pain control than would patients with a normal modulation pattern of efficient CPM. Thirty patients with painful diabetic neuropathy received 1 week of placebo, 1 week of 30 mg/d duloxetine, and 4 weeks of 60 mg/d duloxetine. Pain modulation was assessed psychophysically, both before and at the end of treatment. Patient assessment of drug efficacy, assessed weekly, was the study's primary outcome. Baseline CPM was found to be correlated with duloxetine efficacy (r=0.628, P<.001, efficient CPM is marked negative), such that less efficient CPM predicted efficacious use of duloxetine. Regression analysis (R(2)=0.673; P=.012) showed that drug efficacy was predicted only by CPM (P=.001) and not by pretreatment pain levels, neuropathy severity, depression level, or patient assessment of improvement by placebo. Furthermore, beyond its predictive value, the treatment-induced improvement in CPM was correlated with drug efficacy (r=-0.411, P=.033). However, this improvement occurred only in patients with less efficient CPM (16.8±16.0 to -1.1±15.5, P<.050). No predictive role was found for TS. In conclusion, the coupling of CPM and duloxetine efficacy highlights the importance of pain pathophysiology in the clinical decision-making process. This evaluative approach promotes personalized pain therapy. Copyright © 2012 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

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

USGS Publications Warehouse

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

2017-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Scaling and memory in volatility return intervals in financial markets

NASA Astrophysics Data System (ADS)

Yamasaki, Kazuko; Muchnik, Lev; Havlin, Shlomo; Bunde, Armin; Stanley, H. Eugene

2005-06-01

For both stock and currency markets, we study the return intervals τ between the daily volatilities of the price changes that are above a certain threshold q. We find that the distribution function Pq(τ) scales with the mean return interval [Formula] as [Formula]. The scaling function f(x) is similar in form for all seven stocks and for all seven currency databases analyzed, and f(x) is consistent with a power-law form, f(x) ˜ x-γ with γ ≈ 2. We also quantify how the conditional distribution Pq(τ|τ0) depends on the previous return interval τ0 and find that small (or large) return intervals are more likely to be followed by small (or large) return intervals. This “clustering” of the volatility return intervals is a previously unrecognized phenomenon that we relate to the long-term correlations known to be present in the volatility. Author contributions: S.H. and H.E.S. designed research; K.Y., L.M., S.H., and H.E.S. performed research; A.B. contributed new reagents/analytic tools; A.B. analyzed data; and S.H. wrote the paper.Abbreviations: pdf, probability density function; S&P 500, Standard and Poor's 500 Index; USD, U.S. dollar; JPY, Japanese yen; SEK, Swedish krona.

Finding Every Root of a Broad Class of Real, Continuous Functions in a Given Interval

NASA Technical Reports Server (NTRS)

Tausworthe, Robert C.; Wolgast, Paul A.

2011-01-01

One of the most pervasive needs within the Deep Space Network (DSN) Metric Prediction Generator (MPG) view period event generation is that of finding solutions to given occurrence conditions. While the general form of an equation expresses equivalence between its left-hand and right-hand expressions, the traditional treatment of the subject subtracts the two sides, leaving an expression of the form Integral of(x) = 0. Values of the independent variable x satisfying this condition are roots, or solutions. Generally speaking, there may be no solutions, a unique solution, multiple solutions, or a continuum of solutions to a given equation. In particular, all view period events are modeled as zero crossings of various metrics; for example, the time at which the elevation of a spacecraft reaches its maximum value, as viewed from a Deep Space Station (DSS), is found by locating that point at which the derivative of the elevation function becomes zero. Moreover, each event type may have several occurrences within a given time interval of interest. For example, a spacecraft in a low Moon orbit will experience several possible occultations per day, each of which must be located in time. The MPG is charged with finding all specified event occurrences that take place within a given time interval (or pass ), without any special clues from operators as to when they may occur, for the entire spectrum of missions undertaken by the DSN. For each event type, the event metric function is a known form that can be computed for any instant within the interval. A method has been created for a mathematical root finder to be capable of finding all roots of an arbitrary continuous function, within a given interval, to be subject to very lenient, parameterized assumptions. One assumption is that adjacent roots are separated at least by a given amount, xGuard. Any point whose function value is less than ef in magnitude is considered to be a root, and the function values at distances x

Stopover decision during migration: physiological conditions predict nocturnal restlessness in wild passerines.

PubMed

Fusani, Leonida; Cardinale, Massimiliano; Carere, Claudio; Goymann, Wolfgang

2009-06-23

During migration, a number of bird species rely on stopover sites for resting and feeding before and after crossing ecological barriers such as deserts or seas. The duration of a stopover depends on the combined effects of environmental factors, endogenous programmes and physiological conditions. Previous studies indicated that lean birds prolong their refuelling stopover compared with fat birds; however, the quantitative relationship between physiological conditions and stopover behaviour has not been studied yet. Here, we tested in a large sample of free-living birds of three European passerines (whinchats, Saxicola rubetra, garden warblers, Sylvia borin and whitethroats, Sylvia communis) whether the amount of migratory restlessness (Zugunruhe) shown at a stopover site depends on physiological conditions. An integrated measure of condition based on body mass, amount of subcutaneous fat and thickness of pectoral muscles strongly predicted the intensity of Zugunruhe shown in recording cages in the night following capture. These results provide novel and robust quantitative evidence in support of the hypothesis that the amount of energy reserves plays a major role in determining the stopover duration in migratory birds.

Stopover decision during migration: physiological conditions predict nocturnal restlessness in wild passerines

PubMed Central

Fusani, Leonida; Cardinale, Massimiliano; Carere, Claudio; Goymann, Wolfgang

2009-01-01

During migration, a number of bird species rely on stopover sites for resting and feeding before and after crossing ecological barriers such as deserts or seas. The duration of a stopover depends on the combined effects of environmental factors, endogenous programmes and physiological conditions. Previous studies indicated that lean birds prolong their refuelling stopover compared with fat birds; however, the quantitative relationship between physiological conditions and stopover behaviour has not been studied yet. Here, we tested in a large sample of free-living birds of three European passerines (whinchats, Saxicola rubetra, garden warblers, Sylvia borin and whitethroats, Sylvia communis) whether the amount of migratory restlessness (Zugunruhe) shown at a stopover site depends on physiological conditions. An integrated measure of condition based on body mass, amount of subcutaneous fat and thickness of pectoral muscles strongly predicted the intensity of Zugunruhe shown in recording cages in the night following capture. These results provide novel and robust quantitative evidence in support of the hypothesis that the amount of energy reserves plays a major role in determining the stopover duration in migratory birds. PMID:19324648

Verification of an ENSO-Based Long-Range Prediction of Anomalous Weather Conditions During the Vancouver 2010 Olympics and Paralympics

NASA Astrophysics Data System (ADS)

Mo, Ruping; Joe, Paul I.; Doyle, Chris; Whitfield, Paul H.

2014-01-01

A brief review of the anomalous weather conditions during the Vancouver 2010 Winter Olympic and Paralympic Games and the efforts to predict these anomalies based on some preceding El Niño-Southern Oscillation (ENSO) signals are presented. It is shown that the Olympic Games were held under extraordinarily warm conditions in February 2010, with monthly mean temperature anomalies of +2.2 °C in Vancouver and +2.8 °C in Whistler, ranking respectively as the highest and the second highest in the past 30 years (1981-2010). The warm conditions continued, but became less anomalous, in March 2010 for the Paralympic Games. While the precipitation amounts in the area remained near normal through this winter, the lack of snow due to warm conditions created numerous media headlines and practical problems for the alpine competitions. A statistical model was developed on the premise that February and March temperatures in the Vancouver area could be predicted using an ENSO signal with considerable lead time. This model successfully predicted the warmer-than-normal, lower-snowfall conditions for the Vancouver 2010 Winter Olympics and Paralympics.

Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2

NASA Astrophysics Data System (ADS)

Abhilash, S.; Sahai, A. K.; Borah, N.; Chattopadhyay, R.; Joseph, S.; Sharmila, S.; De, S.; Goswami, B. N.; Kumar, Arun

2014-05-01

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using National Centers for Environmental Prediction Climate Forecast System model version 2 at T126 horizontal resolution. The EPS is formulated by generating 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio of the forecasted rainfall becomes unity by about 18 days. The potential predictability error of the forecasted rainfall saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are found even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of large-scale MISO amplitude as well as the initial conditions related to the different phases of MISO. An analysis of categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

The Predictive Capability of Conditioned Simulation of Discrete Fracture Networks using Structural and Hydraulic Data from the ONKALO Underground Research Facility, Finland

NASA Astrophysics Data System (ADS)

Williams, T. R. N.; Baxter, S.; Hartley, L.; Appleyard, P.; Koskinen, L.; Vanhanarkaus, O.; Selroos, J. O.; Munier, R.

2017-12-01

Discrete fracture network (DFN) models provide a natural analysis framework for rock conditions where flow is predominately through a series of connected discrete features. Mechanistic models to predict the structural patterns of networks are generally intractable due to inherent uncertainties (e.g. deformation history) and as such fracture characterisation typically involves empirical descriptions of fracture statistics for location, intensity, orientation, size, aperture etc. from analyses of field data. These DFN models are used to make probabilistic predictions of likely flow or solute transport conditions for a range of applications in underground resource and construction projects. However, there are many instances when the volumes in which predictions are most valuable are close to data sources. For example, in the disposal of hazardous materials such as radioactive waste, accurate predictions of flow-rates and network connectivity around disposal areas are required for long-term safety evaluation. The problem at hand is thus: how can probabilistic predictions be conditioned on local-scale measurements? This presentation demonstrates conditioning of a DFN model based on the current structural and hydraulic characterisation of the Demonstration Area at the ONKALO underground research facility. The conditioned realisations honour (to a required level of similarity) the locations, orientations and trace lengths of fractures mapped on the surfaces of the nearby ONKALO tunnels and pilot drillholes. Other data used as constraints include measurements from hydraulic injection tests performed in pilot drillholes and inflows to the subsequently reamed experimental deposition holes. Numerical simulations using this suite of conditioned DFN models provides a series of prediction-outcome exercises detailing the reliability of the DFN model to make local-scale predictions of measured geometric and hydraulic properties of the fracture system; and provides an understanding

Using Confidence Intervals and Recurrence Intervals to Determine Precipitation Delivery Mechanisms Responsible for Mass Wasting Events.

NASA Astrophysics Data System (ADS)

Ulizio, T. P.; Bilbrey, C.; Stoyanoff, N.; Dixon, J. L.

2015-12-01

upper bound of the 99% confidence interval at all SNOTEL sites while precipitation accumulated in the form of rain is within the expected average, indicating an anomalously snow year and average amounts of rainfall during the same water year. This information can be used to better predict circumstances leading to slope failures in northern latitude alpine landscapes.

Changes in Pilot Behavior with Predictive System Status Information

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.

1998-01-01

Research has shown a strong pilot preference for predictive information of aircraft system status in the flight deck. However, changes in pilot behavior associated with using this predictive information have not been ascertained. The study described here quantified these changes using three types of predictive information (none, whether a parameter was changing abnormally, and the time for a parameter to reach an alert range) and three initial time intervals until a parameter alert range was reached (ITIs) (1 minute, 5 minutes, and 15 minutes). With predictive information, subjects accomplished most of their tasks before an alert occurred. Subjects organized the time they did their tasks by locus-of-control with no predictive information and for the 1-minute ITI, and by aviatenavigate-communicate for the time for a parameter to reach an alert range and the 15-minute conditions. Overall, predictive information and the longer ITIs moved subjects to performing tasks before the alert actually occurred and had them more mission oriented as indicated by their tasks grouping of aviate-navigate-communicate.

Predictive sensor method and apparatus

NASA Technical Reports Server (NTRS)

Nail, William L. (Inventor); Koger, Thomas L. (Inventor); Cambridge, Vivien (Inventor)

1990-01-01

A predictive algorithm is used to determine, in near real time, the steady state response of a slow responding sensor such as hydrogen gas sensor of the type which produces an output current proportional to the partial pressure of the hydrogen present. A microprocessor connected to the sensor samples the sensor output at small regular time intervals and predicts the steady state response of the sensor in response to a perturbation in the parameter being sensed, based on the beginning and end samples of the sensor output for the current sample time interval.

Predicting Secchi disk depth from average beam attenuation in a deep, ultra-clear lake

USGS Publications Warehouse

Larson, G.L.; Hoffman, R.L.; Hargreaves, B.R.; Collier, R.W.

2007-01-01

We addressed potential sources of error in estimating the water clarity of mountain lakes by investigating the use of beam transmissometer measurements to estimate Secchi disk depth. The optical properties Secchi disk depth (SD) and beam transmissometer attenuation (BA) were measured in Crater Lake (Crater Lake National Park, Oregon, USA) at a designated sampling station near the maximum depth of the lake. A standard 20 cm black and white disk was used to measure SD. The transmissometer light source had a nearly monochromatic wavelength of 660 nm and a path length of 25 cm. We created a SD prediction model by regression of the inverse SD of 13 measurements recorded on days when environmental conditions were acceptable for disk deployment with BA averaged over the same depth range as the measured SD. The relationship between inverse SD and averaged BA was significant and the average 95% confidence interval for predicted SD relative to the measured SD was ??1.6 m (range = -4.6 to 5.5 m) or ??5.0%. Eleven additional sample dates tested the accuracy of the predictive model. The average 95% confidence interval for these sample dates was ??0.7 m (range = -3.5 to 3.8 m) or ??2.2%. The 1996-2000 time-series means for measured and predicted SD varied by 0.1 m, and the medians varied by 0.5 m. The time-series mean annual measured and predicted SD's also varied little, with intra-annual differences between measured and predicted mean annual SD ranging from -2.1 to 0.1 m. The results demonstrated that this prediction model reliably estimated Secchi disk depths and can be used to significantly expand optical observations in an environment where the conditions for standardized SD deployments are limited. ?? 2007 Springer Science+Business Media B.V.

Robust Confidence Interval for a Ratio of Standard Deviations

ERIC Educational Resources Information Center

Bonett, Douglas G.

2006-01-01

Comparing variability of test scores across alternate forms, test conditions, or subpopulations is a fundamental problem in psychometrics. A confidence interval for a ratio of standard deviations is proposed that performs as well as the classic method with normal distributions and performs dramatically better with nonnormal distributions. A simple…

Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

PubMed

Darrington, Richard T; Jiao, Jim

2004-04-01

Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

Comparison Between Vortices Created and Evolving During Fixed and Dynamic Solar Wind Conditions

NASA Technical Reports Server (NTRS)

Collado-Vega, Yaireska M.; Kessel, R. L.; Sibeck, David Gary; Kalb, V. L.; Boller, R. A.; Rastaetter, L.

2013-01-01

We employ Magnetohydrodynamic (MHD) simulations to examine the creation and evolution of plasma vortices within the Earth's magnetosphere for steady solar wind plasma conditions. Very few vortices form during intervals of such solar wind conditions. Those that do remain in fixed positions for long periods (often hours) and exhibit rotation axes that point primarily in the x or y direction, parallel (or antiparallel) to the local magnetospheric magnetic field direction. Occasionally, the orientation of the axes rotates from the x direction to another direction. We compare our results with simulations previously done for unsteady solar wind conditions. By contrast, these vortices that form during intervals of varying solar wind conditions exhibit durations ranging from seconds (in the case of those with axes in the x or y direction) to minutes (in the case of those with axes in the z direction) and convect antisunward. The local-time dependent sense of rotation seen in these previously reported vortices suggests an interpretation in terms of the Kelvin-Helmholtz instability. For steady conditions, the biggest vortices developed on the dayside (about 6R(E) in diameter), had their rotation axes aligned with the y direction and had the longest periods of duration. We attribute these vortices to the flows set up by reconnection on the high latitude magnetopause during intervals of northward Interplanetary Magnetic Field (IMF) orientation. This is the first time that vortices due to high-latitude reconnection have been visualized. The model also successfully predicts the principal characteristics of previously reported plasma vortices within the magnetosphere, namely their dimension, flow velocities, and durations.

Commentary on Holmes et al. (2007): resolving the debate on when extinction risk is predictable.

PubMed

Ellner, Stephen P; Holmes, Elizabeth E

2008-08-01

We reconcile the findings of Holmes et al. (Ecology Letters, 10, 2007, 1182) that 95% confidence intervals for quasi-extinction risk were narrow for many vertebrates of conservation concern, with previous theory predicting wide confidence intervals. We extend previous theory, concerning the precision of quasi-extinction estimates as a function of population dynamic parameters, prediction intervals and quasi-extinction thresholds, and provide an approximation that specifies the prediction interval and threshold combinations where quasi-extinction estimates are precise (vs. imprecise). This allows PVA practitioners to define the prediction interval and threshold regions of safety (low risk with high confidence), danger (high risk with high confidence), and uncertainty.

Identifying black swans in NextGen: predicting human performance in off-nominal conditions.

PubMed

Wickens, Christopher D; Hooey, Becky L; Gore, Brian F; Sebok, Angelia; Koenicke, Corey S

2009-10-01

The objective is to validate a computational model of visual attention against empirical data--derived from a meta-analysis--of pilots' failure to notice safety-critical unexpected events. Many aircraft accidents have resulted, in part, because of failure to notice nonsalient unexpected events outside of foveal vision, illustrating the phenomenon of change blindness. A model of visual noticing, N-SEEV (noticing-salience, expectancy, effort, and value), was developed to predict these failures. First, 25 studies that reported objective data on miss rate for unexpected events in high-fidelity cockpit simulations were identified, and their miss rate data pooled across five variables (phase of flight, event expectancy, event location, presence of a head-up display, and presence of a highway-in-the-sky display). Second, the parameters of the N-SEEV model were tailored to mimic these dichotomies. The N-SEEV model output predicted variance in the obtained miss rate (r = .73). The individual miss rates of all six dichotomous conditions were predicted within 14%, and four of these were predicted within 7%. The N-SEEV model, developed on the basis of an independent data set, was able to successfully predict variance in this safety-critical measure of pilot response to abnormal circumstances, as collected from the literature. As new technology and procedures are envisioned for the future airspace, it is important to predict if these may compromise safety in terms of pilots' failing to notice unexpected events. Computational models such as N-SEEV support cost-effective means of making such predictions.

Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes.

PubMed

Mosley, Jonathan D; Shoemaker, M Benjamin; Wells, Quinn S; Darbar, Dawood; Shaffer, Christian M; Edwards, Todd L; Bastarache, Lisa; McCarty, Catherine A; Thompson, Will; Chute, Christopher G; Jarvik, Gail P; Crosslin, David R; Larson, Eric B; Kullo, Iftikhar J; Pacheco, Jennifer A; Peissig, Peggy L; Brilliant, Murray H; Linneman, James G; Witte, John S; Denny, Josh C; Roden, Dan M

2017-04-01

One potential use for the PR interval is as a biomarker of disease risk. We hypothesized that quantifying the shared genetic architectures of the PR interval and a set of clinical phenotypes would identify genetic mechanisms contributing to PR variability and identify diseases associated with a genetic predictor of PR variability. We used ECG measurements from the ARIC study (Atherosclerosis Risk in Communities; n=6731 subjects) and 63 genetically modulated diseases from the eMERGE network (Electronic Medical Records and Genomics; n=12 978). We measured pairwise genetic correlations (rG) between PR phenotypes (PR interval, PR segment, P-wave duration) and each of the 63 phenotypes. The PR segment was genetically correlated with atrial fibrillation (rG=-0.88; P =0.0009). An analysis of metabolic phenotypes in ARIC also showed that the P wave was genetically correlated with waist circumference (rG=0.47; P =0.02). A genetically predicted PR interval phenotype based on 645 714 single-nucleotide polymorphisms was associated with atrial fibrillation (odds ratio=0.89 per SD change; 95% confidence interval, 0.83-0.95; P =0.0006). The differing pattern of associations among the PR phenotypes is consistent with analyses that show that the genetic correlation between the P wave and PR segment was not significantly different from 0 (rG=-0.03 [0.16]). The genetic architecture of the PR interval comprises modulators of atrial fibrillation risk and obesity. © 2017 American Heart Association, Inc.

Predicting solvation free energies and thermodynamics in polar solvents and mixtures using a solvation-layer interface condition

PubMed Central

Goossens, Spencer; Mehdizadeh Rahimi, Ali

2017-01-01

We demonstrate that with two small modifications, the popular dielectric continuum model is capable of predicting, with high accuracy, ion solvation thermodynamics (Gibbs free energies, entropies, and heat capacities) in numerous polar solvents. We are also able to predict ion solvation free energies in water–co-solvent mixtures over available concentration series. The first modification to the classical dielectric Poisson model is a perturbation of the macroscopic dielectric-flux interface condition at the solute–solvent interface: we add a nonlinear function of the local electric field, giving what we have called a solvation-layer interface condition (SLIC). The second modification is including the microscopic interface potential (static potential) in our model. We show that the resulting model exhibits high accuracy without the need for fitting solute atom radii in a state-dependent fashion. Compared to experimental results in nine water–co-solvent mixtures, SLIC predicts transfer free energies to within 2.5 kJ/mol. The co-solvents include both protic and aprotic species, as well as biologically relevant denaturants such as urea and dimethylformamide. Furthermore, our results indicate that the interface potential is essential to reproduce entropies and heat capacities. These and previous tests of the SLIC model indicate that it is a promising dielectric continuum model for accurate predictions in a wide range of conditions.

Predicting solvation free energies and thermodynamics in polar solvents and mixtures using a solvation-layer interface condition

NASA Astrophysics Data System (ADS)

Molavi Tabrizi, Amirhossein; Goossens, Spencer; Mehdizadeh Rahimi, Ali; Knepley, Matthew; Bardhan, Jaydeep P.

2017-03-01

We demonstrate that with two small modifications, the popular dielectric continuum model is capable of predicting, with high accuracy, ion solvation thermodynamics (Gibbs free energies, entropies, and heat capacities) in numerous polar solvents. We are also able to predict ion solvation free energies in water-co-solvent mixtures over available concentration series. The first modification to the classical dielectric Poisson model is a perturbation of the macroscopic dielectric-flux interface condition at the solute-solvent interface: we add a nonlinear function of the local electric field, giving what we have called a solvation-layer interface condition (SLIC). The second modification is including the microscopic interface potential (static potential) in our model. We show that the resulting model exhibits high accuracy without the need for fitting solute atom radii in a state-dependent fashion. Compared to experimental results in nine water-co-solvent mixtures, SLIC predicts transfer free energies to within 2.5 kJ/mol. The co-solvents include both protic and aprotic species, as well as biologically relevant denaturants such as urea and dimethylformamide. Furthermore, our results indicate that the interface potential is essential to reproduce entropies and heat capacities. These and previous tests of the SLIC model indicate that it is a promising dielectric continuum model for accurate predictions in a wide range of conditions.

Interval timing in genetically modified mice: a simple paradigm

PubMed Central

Balci, F.; Papachristos, E. B.; Gallistel, C. R.; Brunner, D.; Gibson, J.; Shumyatsky, G. P.

2009-01-01

We describe a behavioral screen for the quantitative study of interval timing and interval memory in mice. Mice learn to switch from a short-latency feeding station to a long-latency station when the short latency has passed without a feeding. The psychometric function is the cumulative distribution of switch latencies. Its median measures timing accuracy and its interquartile interval measures timing precision. Next, using this behavioral paradigm, we have examined mice with a gene knockout of the receptor for gastrin-releasing peptide that show enhanced (i.e. prolonged) freezing in fear conditioning. We have tested the hypothesis that the mutants freeze longer because they are more uncertain than wild types about when to expect the electric shock. The knockouts however show normal accuracy and precision in timing, so we have rejected this alternative hypothesis. Last, we conduct the pharmacological validation of our behavioral screen using D-amphetamine and methamphetamine. We suggest including the analysis of interval timing and temporal memory in tests of genetically modified mice for learning and memory and argue that our paradigm allows this to be done simply and efficiently. PMID:17696995

Interval timing in genetically modified mice: a simple paradigm.

PubMed

Balci, F; Papachristos, E B; Gallistel, C R; Brunner, D; Gibson, J; Shumyatsky, G P

2008-04-01

We describe a behavioral screen for the quantitative study of interval timing and interval memory in mice. Mice learn to switch from a short-latency feeding station to a long-latency station when the short latency has passed without a feeding. The psychometric function is the cumulative distribution of switch latencies. Its median measures timing accuracy and its interquartile interval measures timing precision. Next, using this behavioral paradigm, we have examined mice with a gene knockout of the receptor for gastrin-releasing peptide that show enhanced (i.e. prolonged) freezing in fear conditioning. We have tested the hypothesis that the mutants freeze longer because they are more uncertain than wild types about when to expect the electric shock. The knockouts however show normal accuracy and precision in timing, so we have rejected this alternative hypothesis. Last, we conduct the pharmacological validation of our behavioral screen using d-amphetamine and methamphetamine. We suggest including the analysis of interval timing and temporal memory in tests of genetically modified mice for learning and memory and argue that our paradigm allows this to be done simply and efficiently.

Validation of PR interval length as a criterion for development of atrial fibrillation in non-Hispanic whites, African Americans and Hispanics.

PubMed

Shulman, Eric; Aagaard, Philip; Kargoli, Faraj; Hoch, Ethan; Zheng, Laura; Di Biase, Luigi; Fisher, John; Gross, Jay; Kim, Soo; Ferrick, Kevin; Krumerman, Andrew

2015-01-01

PR interval prolongation on electrocardiogram (ECG) increases the risk of atrial fibrillation (AF). Non-Hispanic Whites are at higher risk of AF compared to African Americans and Hispanics. However, it remains unknown if prolongation of the PR interval for the development of AF varies by race/ethnicity. Therefore, we determined whether race affects the PR interval length's ability to predict AF and if the commonly used criterion of 200 ms in AF prediction models can continue to be used for non-White cohorts. This is a retrospective epidemiological study of consecutive inpatient and outpatients. An ECG database was initially interrogated. Patients were included if their initial ECG demonstrated sinus rhythm and had two or more electrocardiograms and declared a race and/or ethnicity as non-Hispanic White, African American or Hispanic. Development of AF was stratified by race/ethnicity along varying PR intervals. Cox models controlled for age, gender, race/ethnicity, systolic blood pressure, BMI, QRS, QTc, heart rate, murmur, treatment for hypertension, heart failure and use of AV nodal blocking agents to assess PR interval's predictive ability for development of AF. 50,870 patients met inclusion criteria of which 5,199 developed AF over 3.72 mean years of follow-up. When the PR interval was separated by quantile, prolongation of the PR interval to predict AF first became significant in Hispanic and African Americans at the 92.5th quantile of 196-201 ms (HR: 1.42, 95% CI: 1.09-1.86, p=0.01; HR: 1.32, 95% CI: 1.07-1.64, p=0.01, respectively) then in non-Hispanic Whites at the 95th quantile at 203-212 ms (HR: 1.24, 95% CI: 1.24-1.53, p=0.04). For those with a PR interval above 200 ms, African Americans had a lower risk than non-Hispanic Whites to develop AF (HR: 0.80, 95% CI: 0.64-0.95, p=0.012), however, no significant difference was demonstrated in Hispanics. This is the first study to validate a PR interval value of 200 ms as a criterion in African Americans and

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.

Predictive genetic testing for neurodegenerative conditions: how should conflicting interests within families be managed?

PubMed

Stark, Zornitza; Wallace, Jane; Gillam, Lynn; Burgess, Matthew; Delatycki, Martin B

2016-10-01

Predictive genetic testing for a neurodegenerative condition in one individual in a family may have implications for other family members, in that it can reveal their genetic status. Herein a complex clinical case is explored where the testing wish of one family member was in direct conflict to that of another. The son of a person at 50% risk of an autosomal dominant neurodegenerative condition requested testing to reveal his genetic status. The main reason for the request was if he had the familial mutation, he and his partner planned to utilise preimplantation genetic diagnosis to prevent his offspring having the condition. His at-risk parent was clear that if they found out they had the mutation, they would commit suicide. We assess the potential benefits and harms from acceding to or denying such a request and present an approach to balancing competing rights of individuals within families at risk of late-onset genetic conditions, where family members have irreconcilable differences with respect to predictive testing. We argue that while it may not be possible to completely avoid harm in these situations, it is important to consider the magnitude of risks, and make every effort to limit the potential for adverse outcomes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Experimental and numerical investigation of low-drag intervals in turbulent boundary layer

NASA Astrophysics Data System (ADS)

Park, Jae Sung; Ryu, Sangjin; Lee, Jin

2017-11-01

It has been widely investigated that there is a substantial intermittency between high and low drag states in wall-bounded shear flows. Recent experimental and computational studies in a turbulent channel flow have identified low-drag time intervals based on wall shear stress measurements. These intervals are a weak turbulence state characterized by low-speed streaks and weak streamwise vortices. In this study, the spatiotemporal dynamics of low-drag intervals in a turbulent boundary layer is investigated using experiments and simulations. The low-drag intervals are monitored based on the wall shear stress measurement. We show that near the wall conditionally-sampled mean velocity profiles during low-drag intervals closely approach that of a low-drag nonlinear traveling wave solution as well as that of the so-called maximum drag reduction asymptote. This observation is consistent with the channel flow studies. Interestingly, the large spatial stretching of the streak is very evident in the wall-normal direction during low-drag intervals. Lastly, a possible connection between the mean velocity profile during the low-drag intervals and the Blasius profile will be discussed. This work was supported by startup funds from the University of Nebraska-Lincoln.

Investigating the correlation between paediatric stride interval persistence and gross energy expenditure.

PubMed

Fairley, Jillian A; Sejdić, Ervin; Chau, Tom

2010-02-26

Stride interval persistence, a term used to describe the correlation structure of stride interval time series, is thought to provide insight into neuromotor control, though its exact clinical meaning has not yet been realized. Since human locomotion is shaped by energy efficient movements, it has been hypothesized that stride interval dynamics and energy expenditure may be inherently tied, both having demonstrated similar sensitivities to age, disease, and pace-constrained walking. This study tested for correlations between stride interval persistence and measures of energy expenditure including mass-specific gross oxygen consumption per minute (VO₂), mass-specific gross oxygen cost per meter (VO₂) and heart rate (HR). Metabolic and stride interval data were collected from 30 asymptomatic children who completed one 10-minute walking trial under each of the following conditions: (i) overground walking, (ii) hands-free treadmill walking, and (iii) handrail-supported treadmill walking. Stride interval persistence was not significantly correlated with (p > 0.32), VO₂ (p > 0.18) or HR (p > 0.56). No simple linear dependence exists between stride interval persistence and measures of gross energy expenditure in asymptomatic children when walking overground and on a treadmill.

Scaling and memory in volatility return intervals in financial markets

PubMed Central

Yamasaki, Kazuko; Muchnik, Lev; Havlin, Shlomo; Bunde, Armin; Stanley, H. Eugene

2005-01-01

For both stock and currency markets, we study the return intervals τ between the daily volatilities of the price changes that are above a certain threshold q. We find that the distribution function Pq(τ) scales with the mean return interval \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} \\begin{equation*}{\\bar {{\\tau}}}\\end{equation*}\\end{document} as \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} \\begin{equation*}P_{q}({\\tau})={\\bar {{\\tau}}}^{-1}f({\\tau}/{\\bar {{\\tau}}})\\end{equation*}\\end{document}. The scaling function f(x) is similar in form for all seven stocks and for all seven currency databases analyzed, and f(x) is consistent with a power-law form, f(x) ∼ x-γ with γ ≈ 2. We also quantify how the conditional distribution Pq(τ|τ0) depends on the previous return interval τ0 and find that small (or large) return intervals are more likely to be followed by small (or large) return intervals. This “clustering” of the volatility return intervals is a previously unrecognized phenomenon that we relate to the long-term correlations known to be present in the volatility. PMID:15980152

Predicting Average Vehicle Speed in Two Lane Highways Considering Weather Condition and Traffic Characteristics

NASA Astrophysics Data System (ADS)

Mirbaha, Babak; Saffarzadeh, Mahmoud; AmirHossein Beheshty, Seyed; Aniran, MirMoosa; Yazdani, Mirbahador; Shirini, Bahram

2017-10-01

Analysis of vehicle speed with different weather condition and traffic characteristics is very effective in traffic planning. Since the weather condition and traffic characteristics vary every day, the prediction of average speed can be useful in traffic management plans. In this study, traffic and weather data for a two-lane highway located in Northwest of Iran were selected for analysis. After merging traffic and weather data, the linear regression model was calibrated for speed prediction using STATA12.1 Statistical and Data Analysis software. Variables like vehicle flow, percentage of heavy vehicles, vehicle flow in opposing lane, percentage of heavy vehicles in opposing lane, rainfall (mm), snowfall and maximum daily wind speed more than 13m/s were found to be significant variables in the model. Results showed that variables of vehicle flow and heavy vehicle percent acquired the positive coefficient that shows, by increasing these variables the average vehicle speed in every weather condition will also increase. Vehicle flow in opposing lane, percentage of heavy vehicle in opposing lane, rainfall amount (mm), snowfall and maximum daily wind speed more than 13m/s acquired the negative coefficient that shows by increasing these variables, the average vehicle speed will decrease.

Psychological and behavioral responses to interval and continuous exercise.

PubMed

Stork, Matthew J; Gibala, Martin J; Martin Ginis, Kathleen A

2018-05-16

To compare psychological responses to, and preferences for, moderate-intensity continuous training (MICT), high-intensity interval training (HIIT), and sprint interval training (SIT) among inactive adults; and to investigate the relationships between affect, enjoyment, exercise preferences, and subsequent exercise behavior over a 4-wk follow-up period. Thirty inactive men and women (21.23±3.81 y), inexperienced with HIIT or SIT, completed three trials of cycle ergometer exercise in random order on separate days: MICT (45min continuous; ~70-75% of heart rate maximum (HRmax)); HIIT (10x1 min bouts at ~85-90%HRmax with 1-min recovery periods); and SIT (3x20-s "all-out" sprints with 2-min recovery periods). Perceived exertion (RPE), affect, and arousal were measured throughout the trials and enjoyment was measured post-exercise. Participants rank-ordered the protocols (#1-3) according to preference and logged their exercise over a 4-week follow-up. Despite elevated HR, RPE, and arousal during work periods (ps<0.05), and negative affect during HIIT and SIT, enjoyment and preferences for MICT, HIIT, and SIT were similar (ps>0.05). In-task affect was predictive of post-exercise enjoyment for each type of exercise (rs=0.32 to 0.47; ps<0.05). In-task affect and post-exercise enjoyment predicted preferences for HIIT and SIT (rss=-0.34 to -0.61; ps<0.05), but not for MICT (ps>0.05), respectively. Over the follow-up, participants completed more MICT (M=6.11±4.12) than SIT sessions (M=1.39±1.85; p<0.01, d=1.34). Although participants tended to complete more sessions of MICT than HIIT (M=3.54±4.23; p=0.16, d=0.56), and more sessions of HIIT than SIT (p=0.07, d=0.60), differences were not significant. In-task affect predicted the number of sessions of MICT (r=0.40; p<0.05), but not HIIT or SIT (ps>0.05). This study provides new evidence that a single session of HIIT and SIT can be as enjoyable and preferable as MICT among inactive individuals and that there may be differences

Intertrial-interval effects on sensitivity (A') and response bias (B") in a temporal discrimination by rats.

PubMed Central

Raslear, T G; Shurtleff, D; Simmons, L

1992-01-01

Killeen and Fetterman's (1988) behavioral theory of animal timing predicts that decreases in the rate of reinforcement should produce decreases in the sensitivity (A') of temporal discriminations and a decrease in miss and correct rejection rates (decrease in bias toward "long" responses). Eight rats were trained on a 10- versus 0.1-s temporal discrimination with an intertrial interval of 5 s and were subsequently tested on probe days on the same discrimination with intertrial intervals of 1, 2.5, 5, 10, or 20 s. The rate of reinforcement declined for all animals as intertrial interval increased. Although sensitivity (A') decreased with increasing intertrial interval, all rats showed an increase in bias to make long responses. PMID:1447544

Audiovisual Interval Size Estimation Is Associated with Early Musical Training.

PubMed

Abel, Mary Kathryn; Li, H Charles; Russo, Frank A; Schlaug, Gottfried; Loui, Psyche

2016-01-01

Although pitch is a fundamental attribute of auditory perception, substantial individual differences exist in our ability to perceive differences in pitch. Little is known about how these individual differences in the auditory modality might affect crossmodal processes such as audiovisual perception. In this study, we asked whether individual differences in pitch perception might affect audiovisual perception, as it relates to age of onset and number of years of musical training. Fifty-seven subjects made subjective ratings of interval size when given point-light displays of audio, visual, and audiovisual stimuli of sung intervals. Audiovisual stimuli were divided into congruent and incongruent (audiovisual-mismatched) stimuli. Participants' ratings correlated strongly with interval size in audio-only, visual-only, and audiovisual-congruent conditions. In the audiovisual-incongruent condition, ratings correlated more with audio than with visual stimuli, particularly for subjects who had better pitch perception abilities and higher nonverbal IQ scores. To further investigate the effects of age of onset and length of musical training, subjects were divided into musically trained and untrained groups. Results showed that among subjects with musical training, the degree to which participants' ratings correlated with auditory interval size during incongruent audiovisual perception was correlated with both nonverbal IQ and age of onset of musical training. After partialing out nonverbal IQ, pitch discrimination thresholds were no longer associated with incongruent audio scores, whereas age of onset of musical training remained associated with incongruent audio scores. These findings invite future research on the developmental effects of musical training, particularly those relating to the process of audiovisual perception.

Audiovisual Interval Size Estimation Is Associated with Early Musical Training

PubMed Central

Abel, Mary Kathryn; Li, H. Charles; Russo, Frank A.; Schlaug, Gottfried; Loui, Psyche

2016-01-01

Although pitch is a fundamental attribute of auditory perception, substantial individual differences exist in our ability to perceive differences in pitch. Little is known about how these individual differences in the auditory modality might affect crossmodal processes such as audiovisual perception. In this study, we asked whether individual differences in pitch perception might affect audiovisual perception, as it relates to age of onset and number of years of musical training. Fifty-seven subjects made subjective ratings of interval size when given point-light displays of audio, visual, and audiovisual stimuli of sung intervals. Audiovisual stimuli were divided into congruent and incongruent (audiovisual-mismatched) stimuli. Participants’ ratings correlated strongly with interval size in audio-only, visual-only, and audiovisual-congruent conditions. In the audiovisual-incongruent condition, ratings correlated more with audio than with visual stimuli, particularly for subjects who had better pitch perception abilities and higher nonverbal IQ scores. To further investigate the effects of age of onset and length of musical training, subjects were divided into musically trained and untrained groups. Results showed that among subjects with musical training, the degree to which participants’ ratings correlated with auditory interval size during incongruent audiovisual perception was correlated with both nonverbal IQ and age of onset of musical training. After partialing out nonverbal IQ, pitch discrimination thresholds were no longer associated with incongruent audio scores, whereas age of onset of musical training remained associated with incongruent audio scores. These findings invite future research on the developmental effects of musical training, particularly those relating to the process of audiovisual perception. PMID:27760134

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

DOE PAGES

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

2016-09-29

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

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

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

Musical training generalises across modalities and reveals efficient and adaptive mechanisms for reproducing temporal intervals.

PubMed

Aagten-Murphy, David; Cappagli, Giulia; Burr, David

2014-03-01

Expert musicians are able to time their actions accurately and consistently during a musical performance. We investigated how musical expertise influences the ability to reproduce auditory intervals and how this generalises across different techniques and sensory modalities. We first compared various reproduction strategies and interval length, to examine the effects in general and to optimise experimental conditions for testing the effect of music, and found that the effects were robust and consistent across different paradigms. Focussing on a 'ready-set-go' paradigm subjects reproduced time intervals drawn from distributions varying in total length (176, 352 or 704 ms) or in the number of discrete intervals within the total length (3, 5, 11 or 21 discrete intervals). Overall, Musicians performed more veridical than Non-Musicians, and all subjects reproduced auditory-defined intervals more accurately than visually-defined intervals. However, Non-Musicians, particularly with visual stimuli, consistently exhibited a substantial and systematic regression towards the mean interval. When subjects judged intervals from distributions of longer total length they tended to regress more towards the mean, while the ability to discriminate between discrete intervals within the distribution had little influence on subject error. These results are consistent with a Bayesian model that minimizes reproduction errors by incorporating a central tendency prior weighted by the subject's own temporal precision relative to the current distribution of intervals. Finally a strong correlation was observed between all durations of formal musical training and total reproduction errors in both modalities (accounting for 30% of the variance). Taken together these results demonstrate that formal musical training improves temporal reproduction, and that this improvement transfers from audition to vision. They further demonstrate the flexibility of sensorimotor mechanisms in adapting to

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cattaneo, Richard; Hanna, Rabbie K.; Jacobsen, Gordon

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 onmore » 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

Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality.

PubMed

Li, Pengxiang; Kim, Michelle M; Doshi, Jalpa A

2010-08-20

The Centers for Medicare and Medicaid Services (CMS) has implemented the CMS-Hierarchical Condition Category (CMS-HCC) model to risk adjust Medicare capitation payments. This study intends to assess the performance of the CMS-HCC risk adjustment method and to compare it to the Charlson and Elixhauser comorbidity measures in predicting in-hospital and six-month mortality in Medicare beneficiaries. The study used the 2005-2006 Chronic Condition Data Warehouse (CCW) 5% Medicare files. The primary study sample included all community-dwelling fee-for-service Medicare beneficiaries with a hospital admission between January 1st, 2006 and June 30th, 2006. Additionally, four disease-specific samples consisting of subgroups of patients with principal diagnoses of congestive heart failure (CHF), stroke, diabetes mellitus (DM), and acute myocardial infarction (AMI) were also selected. Four analytic files were generated for each sample by extracting inpatient and/or outpatient claims for each patient. Logistic regressions were used to compare the methods. Model performance was assessed using the c-statistic, the Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and their 95% confidence intervals estimated using bootstrapping. The CMS-HCC had statistically significant higher c-statistic and lower AIC and BIC values than the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality across all samples in analytic files that included claims from the index hospitalization. Exclusion of claims for the index hospitalization generally led to drops in model performance across all methods with the highest drops for the CMS-HCC method. However, the CMS-HCC still performed as well or better than the other two methods. The CMS-HCC method demonstrated better performance relative to the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality. The CMS-HCC model is preferred over the Charlson and Elixhauser methods

Generalized additive models and Lucilia sericata growth: assessing confidence intervals and error rates in forensic entomology.

PubMed

Tarone, Aaron M; Foran, David R

2008-07-01

Forensic entomologists use blow fly development to estimate a postmortem interval. Although accurate, fly age estimates can be imprecise for older developmental stages and no standard means of assigning confidence intervals exists. Presented here is a method for modeling growth of the forensically important blow fly Lucilia sericata, using generalized additive models (GAMs). Eighteen GAMs were created to predict the extent of juvenile fly development, encompassing developmental stage, length, weight, strain, and temperature data, collected from 2559 individuals. All measures were informative, explaining up to 92.6% of the deviance in the data, though strain and temperature exerted negligible influences. Predictions made with an independent data set allowed for a subsequent examination of error. Estimates using length and developmental stage were within 5% of true development percent during the feeding portion of the larval life cycle, while predictions for postfeeding third instars were less precise, but within expected error.

Sensory cortical response to uncertainty and low salience during recognition of affective cues in musical intervals.

PubMed

Bravo, Fernando; Cross, Ian; Stamatakis, Emmanuel Andreas; Rohrmeier, Martin

2017-01-01

Previous neuroimaging studies have shown an increased sensory cortical response (i.e., heightened weight on sensory evidence) under higher levels of predictive uncertainty. The signal enhancement theory proposes that attention improves the quality of the stimulus representation, and therefore reduces uncertainty by increasing the gain of the sensory signal. The present study employed functional magnetic resonance imaging (fMRI) to investigate the neural correlates for ambiguous valence inferences signaled by auditory information within an emotion recognition paradigm. Participants categorized sound stimuli of three distinct levels of consonance/dissonance controlled by interval content. Separate behavioural and neuroscientific experiments were conducted. Behavioural results revealed that, compared with the consonance condition (perfect fourths, fifths and octaves) and the strong dissonance condition (minor/major seconds and tritones), the intermediate dissonance condition (minor thirds) was the most ambiguous, least salient and more cognitively demanding category (slowest reaction times). The neuroscientific findings were consistent with a heightened weight on sensory evidence whilst participants were evaluating intermediate dissonances, which was reflected in an increased neural response of the right Heschl's gyrus. The results support previous studies that have observed enhanced precision of sensory evidence whilst participants attempted to represent and respond to higher degrees of uncertainty, and converge with evidence showing preferential processing of complex spectral information in the right primary auditory cortex. These findings are discussed with respect to music-theoretical concepts and recent Bayesian models of perception, which have proposed that attention may heighten the weight of information coming from sensory channels to stimulate learning about unknown predictive relationships.

Sensory cortical response to uncertainty and low salience during recognition of affective cues in musical intervals

PubMed Central

Cross, Ian; Stamatakis, Emmanuel Andreas; Rohrmeier, Martin

2017-01-01

Previous neuroimaging studies have shown an increased sensory cortical response (i.e., heightened weight on sensory evidence) under higher levels of predictive uncertainty. The signal enhancement theory proposes that attention improves the quality of the stimulus representation, and therefore reduces uncertainty by increasing the gain of the sensory signal. The present study employed functional magnetic resonance imaging (fMRI) to investigate the neural correlates for ambiguous valence inferences signaled by auditory information within an emotion recognition paradigm. Participants categorized sound stimuli of three distinct levels of consonance/dissonance controlled by interval content. Separate behavioural and neuroscientific experiments were conducted. Behavioural results revealed that, compared with the consonance condition (perfect fourths, fifths and octaves) and the strong dissonance condition (minor/major seconds and tritones), the intermediate dissonance condition (minor thirds) was the most ambiguous, least salient and more cognitively demanding category (slowest reaction times). The neuroscientific findings were consistent with a heightened weight on sensory evidence whilst participants were evaluating intermediate dissonances, which was reflected in an increased neural response of the right Heschl’s gyrus. The results support previous studies that have observed enhanced precision of sensory evidence whilst participants attempted to represent and respond to higher degrees of uncertainty, and converge with evidence showing preferential processing of complex spectral information in the right primary auditory cortex. These findings are discussed with respect to music-theoretical concepts and recent Bayesian models of perception, which have proposed that attention may heighten the weight of information coming from sensory channels to stimulate learning about unknown predictive relationships. PMID:28422990

Time, rate, and conditioning.

PubMed

Gallistel, C R; Gibbon, J

2000-04-01

The authors draw together and develop previous timing models for a broad range of conditioning phenomena to reveal their common conceptual foundations: First, conditioning depends on the learning of the temporal intervals between events and the reciprocals of these intervals, the rates of event occurrence. Second, remembered intervals and rates translate into observed behavior through decision processes whose structure is adapted to noise in the decision variables. The noise and the uncertainties consequent on it have both subjective and objective origins. A third feature of these models is their timescale invariance, which the authors argue is a very important property evident in the available experimental data. This conceptual framework is similar to the psychophysical conceptual framework in which contemporary models of sensory processing are rooted. The authors contrast it with the associative conceptual framework.

The Applicability of Confidence Intervals of Quantiles for the Generalized Logistic Distribution

NASA Astrophysics Data System (ADS)

Shin, H.; Heo, J.; Kim, T.; Jung, Y.

2007-12-01

The generalized logistic (GL) distribution has been widely used for frequency analysis. However, there is a little study related to the confidence intervals that indicate the prediction accuracy of distribution for the GL distribution. In this paper, the estimation of the confidence intervals of quantiles for the GL distribution is presented based on the method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) and the asymptotic variances of each quantile estimator are derived as functions of the sample sizes, return periods, and parameters. Monte Carlo simulation experiments are also performed to verify the applicability of the derived confidence intervals of quantile. As the results, the relative bias (RBIAS) and relative root mean square error (RRMSE) of the confidence intervals generally increase as return period increases and reverse as sample size increases. And PWM for estimating the confidence intervals performs better than the other methods in terms of RRMSE when the data is almost symmetric while ML shows the smallest RBIAS and RRMSE when the data is more skewed and sample size is moderately large. The GL model was applied to fit the distribution of annual maximum rainfall data. The results show that there are little differences in the estimated quantiles between ML and PWM while distinct differences in MOM.

VARIABLE TIME-INTERVAL GENERATOR

DOEpatents

Gross, J.E.

1959-10-31

This patent relates to a pulse generator and more particularly to a time interval generator wherein the time interval between pulses is precisely determined. The variable time generator comprises two oscillators with one having a variable frequency output and the other a fixed frequency output. A frequency divider is connected to the variable oscillator for dividing its frequency by a selected factor and a counter is used for counting the periods of the fixed oscillator occurring during a cycle of the divided frequency of the variable oscillator. This defines the period of the variable oscillator in terms of that of the fixed oscillator. A circuit is provided for selecting as a time interval a predetermined number of periods of the variable oscillator. The output of the generator consists of a first pulse produced by a trigger circuit at the start of the time interval and a second pulse marking the end of the time interval produced by the same trigger circuit.

Dopamine cells respond to predicted events during classical conditioning: evidence for eligibility traces in the reward-learning network.

PubMed

Pan, Wei-Xing; Schmidt, Robert; Wickens, Jeffery R; Hyland, Brian I

2005-06-29

Behavioral conditioning of cue-reward pairing results in a shift of midbrain dopamine (DA) cell activity from responding to the reward to responding to the predictive cue. However, the precise time course and mechanism underlying this shift remain unclear. Here, we report a combined single-unit recording and temporal difference (TD) modeling approach to this question. The data from recordings in conscious rats showed that DA cells retain responses to predicted reward after responses to conditioned cues have developed, at least early in training. This contrasts with previous TD models that predict a gradual stepwise shift in latency with responses to rewards lost before responses develop to the conditioned cue. By exploring the TD parameter space, we demonstrate that the persistent reward responses of DA cells during conditioning are only accurately replicated by a TD model with long-lasting eligibility traces (nonzero values for the parameter lambda) and low learning rate (alpha). These physiological constraints for TD parameters suggest that eligibility traces and low per-trial rates of plastic modification may be essential features of neural circuits for reward learning in the brain. Such properties enable rapid but stable initiation of learning when the number of stimulus-reward pairings is limited, conferring significant adaptive advantages in real-world environments.

Reaction time in pilots during intervals of high sustained g.

PubMed

Truszczynski, Olaf; Lewkowicz, Rafal; Wojtkowiak, Mieczyslaw; Biernacki, Marcin P

2014-11-01

An important problem for pilots is visual disturbances occurring under +Gz acceleration. Assessment of the degree of intensification of these disturbances is generally accepted as the acceleration tolerance level (ATL) criterion determined in human centrifuges. The aim of this research was to evaluate the visual-motor responses of pilots during rapidly increasing acceleration contained in cyclic intervals of +6 Gz to the maximum ATL. The study involved 40 male pilots ages 32-41 yr. The task was a quick and faultless response to the light stimuli presented on a light bar during exposure to acceleration until reaching the ATL. Simple response time (SRT) measurements were performed using a visual-motor analysis system throughout the exposures which allowed assessment of a pilot's ATL. There were 29 pilots who tolerated the initial phase of interval acceleration and achieved +6 Gz, completing the test at ATL. Relative to the control measurements, the obtained results indicate a significant effect of the applied acceleration on response time. SRT during +6 Gz exposure was not significantly longer compared with the reaction time between each of the intervals. SRT and erroneous reactions indicated no statistically significant differences between the "lower" and "higher" ATL groups. SRT measurements over the +6-Gz exposure intervals did not vary between "lower" and "higher" ATL groups and, therefore, are not useful in predicting pilot performance. The gradual exposure to the maximum value of +6 Gz with exposure to the first three intervals on the +6-Gz plateau effectively differentiated pilots.

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

PubMed

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

2017-03-01

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

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

ERIC Educational Resources Information Center

Kyndt, Eva; Vermeire, Eva; Cabus, Shana

2016-01-01

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

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.

Minimax confidence intervals in geomagnetism

NASA Technical Reports Server (NTRS)

Stark, Philip B.

1992-01-01

The present paper uses theory of Donoho (1989) to find lower bounds on the lengths of optimally short fixed-length confidence intervals (minimax confidence intervals) for Gauss coefficients of the field of degree 1-12 using the heat flow constraint. The bounds on optimal minimax intervals are about 40 percent shorter than Backus' intervals: no procedure for producing fixed-length confidence intervals, linear or nonlinear, can give intervals shorter than about 60 percent the length of Backus' in this problem. While both methods rigorously account for the fact that core field models are infinite-dimensional, the application of the techniques to the geomagnetic problem involves approximations and counterfactual assumptions about the data errors, and so these results are likely to be extremely optimistic estimates of the actual uncertainty in Gauss coefficients.

Method of high precision interval measurement in pulse laser ranging system

NASA Astrophysics Data System (ADS)

Wang, Zhen; Lv, Xin-yuan; Mao, Jin-jin; Liu, Wei; Yang, Dong

2013-09-01

Laser ranging is suitable for laser system, for it has the advantage of high measuring precision, fast measuring speed,no cooperative targets and strong resistance to electromagnetic interference,the measuremen of laser ranging is the key paremeters affecting the performance of the whole system.The precision of the pulsed laser ranging system was decided by the precision of the time interval measurement, the principle structure of laser ranging system was introduced, and a method of high precision time interval measurement in pulse laser ranging system was established in this paper.Based on the analysis of the factors which affected the precision of range measure,the pulse rising edges discriminator was adopted to produce timing mark for the start-stop time discrimination,and the TDC-GP2 high precision interval measurement system based on TMS320F2812 DSP was designed to improve the measurement precision.Experimental results indicate that the time interval measurement method in this paper can obtain higher range accuracy. Compared with the traditional time interval measurement system,the method simplifies the system design and reduce the influence of bad weather conditions,furthermore,it satisfies the requirements of low costs and miniaturization.

A predictive model of geosynchronous magnetopause crossings

NASA Astrophysics Data System (ADS)

Dmitriev, A.; Suvorova, A.; Chao, J.-K.

2011-05-01

We have developed a model predicting whether or not the magnetopause crosses geosynchronous orbit at a given location for given solar wind pressure Psw, Bz component of the interplanetary magnetic field (IMF), and geomagnetic conditions characterized by 1 min SYM-H index. The model is based on more than 300 geosynchronous magnetopause crossings (GMCs) and about 6000 min when geosynchronous satellites of GOES and Los Alamos National Laboratory (LANL) series are located in the magnetosheath (so-called MSh intervals) in 1994-2001. Minimizing of the Psw required for GMCs and MSh intervals at various locations, Bz, and SYM-H allows describing both an effect of magnetopause dawn-dusk asymmetry and saturation of Bz influence for very large southward IMF. The asymmetry is strong for large negative Bz and almost disappears when Bz is positive. We found that the larger the amplitude of negative SYM-H, the lower the solar wind pressure required for GMCs. We attribute this effect to a depletion of the dayside magnetic field by a storm time intensification of the cross-tail current. It is also found that the magnitude of threshold for Bz saturation increases with SYM-H index such that for small negative and positive SYM-H the effect of saturation diminishes. This supports an idea that enhanced thermal pressure of the magnetospheric plasma and ring current particles during magnetic storms results in the saturation of magnetic effect of the IMF Bz at the dayside magnetopause. A noticeable advantage of the model's prediction capabilities in comparison with other magnetopause models makes the model useful for space weather predictions.

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

Gaussian process regression for tool wear prediction

NASA Astrophysics Data System (ADS)

Kong, Dongdong; Chen, Yongjie; Li, Ning

2018-05-01

To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.

Lesions of the amygdala central nucleus abolish lipoprivic-enhanced responding during oil-predicting conditioned stimuli.

PubMed

Benoit, S C; Morell, J R; Davidson, T L

1999-12-01

T. L. Davidson, A. M. Altizer, S. C. Benoit, E. K. Walls, and T. L. Powley (1997) reported that rats show facilitated responding to conditioned stimuli (CSs) that predict oil, after administration of the lipoprivic agent, Na-2-mercaptoacetate (MA). This facilitation was blocked by vagal deafferentation. The present article extends that investigation to another structure, the amygdala central nucleus (CN). The CN receives inputs from dorsal vagal nuclei, and neurotoxic lesions of this nucleus are reported to abolish feeding in response to lipoprivic challenges. In Experiment 1, rats with ibotenic acid (IBO) lesions of the CN failed to show enhanced appetitive responding during oil-predicting CSs after administration of MA. Experiment 2 used a conditioned taste-aversion procedure to establish that rats with IBO lesions of the CN were able to discriminate the tastes of sucrose and peanut oil and had intact CS-US representations. It is concluded that the amygdala CN is a necessary structure for the detection of lipoprivic challenges.

Obtaining appropriate interval estimates for age when multiple indicators are used: evaluation of an ad-hoc procedure.

PubMed

Fieuws, Steffen; Willems, Guy; Larsen-Tangmose, Sara; Lynnerup, Niels; Boldsen, Jesper; Thevissen, Patrick

2016-03-01

When an estimate of age is needed, typically multiple indicators are present as found in skeletal or dental information. There exists a vast literature on approaches to estimate age from such multivariate data. Application of Bayes' rule has been proposed to overcome drawbacks of classical regression models but becomes less trivial as soon as the number of indicators increases. Each of the age indicators can lead to a different point estimate ("the most plausible value for age") and a prediction interval ("the range of possible values"). The major challenge in the combination of multiple indicators is not the calculation of a combined point estimate for age but the construction of an appropriate prediction interval. Ignoring the correlation between the age indicators results in intervals being too small. Boldsen et al. (2002) presented an ad-hoc procedure to construct an approximate confidence interval without the need to model the multivariate correlation structure between the indicators. The aim of the present paper is to bring under attention this pragmatic approach and to evaluate its performance in a practical setting. This is all the more needed since recent publications ignore the need for interval estimation. To illustrate and evaluate the method, Köhler et al. (1995) third molar scores are used to estimate the age in a dataset of 3200 male subjects in the juvenile age range.

On the differentiation matrix for Daubechies-based wavelets on an interval

NASA Technical Reports Server (NTRS)

Jameson, Leland

1993-01-01

The differentiation matrix for a Daubechies-based wavlet basis defined on an interval will be constructed. It will be shown that the differentiation matrix based on the currently available boundary constructions does not maintain the superconvergence encountered under periodic boundary conditions.

Can longer forest harvest intervals increase summer streamflow for salmon recovery?

EPA Science Inventory

The Mashel Streamflow Modeling Project in the Mashel River Basin, Washington, is using a watershed-scale ecohydrological model to assess whether longer forest harvest intervals can remediate summer low flow conditions that have contributed to sharply reduced runs of spawning Chin...

Infinite time interval backward stochastic differential equations with continuous coefficients.

PubMed

Zong, Zhaojun; Hu, Feng

2016-01-01

In this paper, we study the existence theorem for [Formula: see text] [Formula: see text] solutions to a class of 1-dimensional infinite time interval backward stochastic differential equations (BSDEs) under the conditions that the coefficients are continuous and have linear growths. We also obtain the existence of a minimal solution. Furthermore, we study the existence and uniqueness theorem for [Formula: see text] [Formula: see text] solutions of infinite time interval BSDEs with non-uniformly Lipschitz coefficients. It should be pointed out that the assumptions of this result is weaker than that of Theorem 3.1 in Zong (Turkish J Math 37:704-718, 2013).

Interval stability for complex systems

NASA Astrophysics Data System (ADS)

Klinshov, Vladimir V.; Kirillov, Sergey; Kurths, Jürgen; Nekorkin, Vladimir I.

2018-04-01

Stability of dynamical systems against strong perturbations is an important problem of nonlinear dynamics relevant to many applications in various areas. Here, we develop a novel concept of interval stability, referring to the behavior of the perturbed system during a finite time interval. Based on this concept, we suggest new measures of stability, namely interval basin stability (IBS) and interval stability threshold (IST). IBS characterizes the likelihood that the perturbed system returns to the stable regime (attractor) in a given time. IST provides the minimal magnitude of the perturbation capable to disrupt the stable regime for a given interval of time. The suggested measures provide important information about the system susceptibility to external perturbations which may be useful for practical applications. Moreover, from a theoretical viewpoint the interval stability measures are shown to bridge the gap between linear and asymptotic stability. We also suggest numerical algorithms for quantification of the interval stability characteristics and demonstrate their potential for several dynamical systems of various nature, such as power grids and neural networks.

Pavement remaining service interval implementation guidelines.

DOT National Transportation Integrated Search

2013-11-01

"Many important decisions are necessary in order to effectively provide and manage a pavement network. At the heart of this : process is the prediction of needed future construction events. One approach to providing a single numeric on the condition ...

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

Predicting Operator Execution Times Using CogTool

NASA Technical Reports Server (NTRS)

Santiago-Espada, Yamira; Latorella, Kara A.

2013-01-01

Researchers and developers of NextGen systems can use predictive human performance modeling tools as an initial approach to obtain skilled user performance times analytically, before system testing with users. This paper describes the CogTool models for a two pilot crew executing two different types of a datalink clearance acceptance tasks, and on two different simulation platforms. The CogTool time estimates for accepting and executing Required Time of Arrival and Interval Management clearances were compared to empirical data observed in video tapes and registered in simulation files. Results indicate no statistically significant difference between empirical data and the CogTool predictions. A population comparison test found no significant differences between the CogTool estimates and the empirical execution times for any of the four test conditions. We discuss modeling caveats and considerations for applying CogTool to crew performance modeling in advanced cockpit environments.

Prediction of the optimum hybridization conditions of dot-blot-SNP analysis using estimated melting temperature of oligonucleotide probes.

PubMed

Shiokai, Sachiko; Kitashiba, Hiroyasu; Nishio, Takeshi

2010-08-01

Although the dot-blot-SNP technique is a simple cost-saving technique suitable for genotyping of many plant individuals, optimization of hybridization and washing conditions for each SNP marker requires much time and labor. For prediction of the optimum hybridization conditions for each probe, we compared T (m) values estimated from nucleotide sequences using the DINAMelt web server, measured T (m) values, and hybridization conditions yielding allele-specific signals. The estimated T (m) values were comparable to the measured T (m) values with small differences of less than 3 degrees C for most of the probes. There were differences of approximately 14 degrees C between the specific signal detection conditions and estimated T (m) values. Change of one level of SSC concentrations of 0.1, 0.2, 0.5, and 1.0x SSC corresponded to a difference of approximately 5 degrees C in optimum signal detection temperature. Increasing the sensitivity of signal detection by shortening the exposure time to X-ray film changed the optimum hybridization condition for specific signal detection. Addition of competitive oligonucleotides to the hybridization mixture increased the suitable hybridization conditions by 1.8. Based on these results, optimum hybridization conditions for newly produced dot-blot-SNP markers will become predictable.

On the estimation of risk associated with an attenuation prediction

NASA Technical Reports Server (NTRS)

Crane, R. K.

1992-01-01

Viewgraphs from a presentation on the estimation of risk associated with an attenuation prediction is presented. Topics covered include: link failure - attenuation exceeding a specified threshold for a specified time interval or intervals; risk - the probability of one or more failures during the lifetime of the link or during a specified accounting interval; the problem - modeling the probability of attenuation by rainfall to provide a prediction of the attenuation threshold for a specified risk; and an accounting for the inadequacy of a model or models.

Human instrumental performance in ratio and interval contingencies: A challenge for associative theory.

PubMed

Pérez, Omar D; Aitken, Michael R F; Zhukovsky, Peter; Soto, Fabián A; Urcelay, Gonzalo P; Dickinson, Anthony

2016-12-15

Associative learning theories regard the probability of reinforcement as the critical factor determining responding. However, the role of this factor in instrumental conditioning is not completely clear. In fact, free-operant experiments show that participants respond at a higher rate on variable ratio than on variable interval schedules even though the reinforcement probability is matched between the schedules. This difference has been attributed to the differential reinforcement of long inter-response times (IRTs) by interval schedules, which acts to slow responding. In the present study, we used a novel experimental design to investigate human responding under random ratio (RR) and regulated probability interval (RPI) schedules, a type of interval schedule that sets a reinforcement probability independently of the IRT duration. Participants responded on each type of schedule before a final choice test in which they distributed responding between two schedules similar to those experienced during training. Although response rates did not differ during training, the participants responded at a lower rate on the RPI schedule than on the matched RR schedule during the choice test. This preference cannot be attributed to a higher probability of reinforcement for long IRTs and questions the idea that similar associative processes underlie classical and instrumental conditioning.

Geophysical Anomalies and Earthquake Prediction

NASA Astrophysics Data System (ADS)

Jackson, D. D.

2008-12-01

Finding anomalies is easy. Predicting earthquakes convincingly from such anomalies is far from easy. Why? Why have so many beautiful geophysical abnormalities not led to successful prediction strategies? What is earthquake prediction? By my definition it is convincing information that an earthquake of specified size is temporarily much more likely than usual in a specific region for a specified time interval. We know a lot about normal earthquake behavior, including locations where earthquake rates are higher than elsewhere, with estimable rates and size distributions. We know that earthquakes have power law size distributions over large areas, that they cluster in time and space, and that aftershocks follow with power-law dependence on time. These relationships justify prudent protective measures and scientific investigation. Earthquake prediction would justify exceptional temporary measures well beyond those normal prudent actions. Convincing earthquake prediction would result from methods that have demonstrated many successes with few false alarms. Predicting earthquakes convincingly is difficult for several profound reasons. First, earthquakes start in tiny volumes at inaccessible depth. The power law size dependence means that tiny unobservable ones are frequent almost everywhere and occasionally grow to larger size. Thus prediction of important earthquakes is not about nucleation, but about identifying the conditions for growth. Second, earthquakes are complex. They derive their energy from stress, which is perniciously hard to estimate or model because it is nearly singular at the margins of cracks and faults. Physical properties vary from place to place, so the preparatory processes certainly vary as well. Thus establishing the needed track record for validation is very difficult, especially for large events with immense interval times in any one location. Third, the anomalies are generally complex as well. Electromagnetic anomalies in particular require

TIME-INTERVAL MEASURING DEVICE

DOEpatents

Gross, J.E.

1958-04-15

An electronic device for measuring the time interval between two control pulses is presented. The device incorporates part of a previous approach for time measurement, in that pulses from a constant-frequency oscillator are counted during the interval between the control pulses. To reduce the possible error in counting caused by the operation of the counter gating circuit at various points in the pulse cycle, the described device provides means for successively delaying the pulses for a fraction of the pulse period so that a final delay of one period is obtained and means for counting the pulses before and after each stage of delay during the time interval whereby a plurality of totals is obtained which may be averaged and multplied by the pulse period to obtain an accurate time- Interval measurement.

Moderate irrigation intervals facilitate establishment of two desert shrubs in the Taklimakan Desert Highway Shelterbelt in China.

PubMed

Li, Congjuan; Shi, Xiang; Mohamad, Osama Abdalla; Gao, Jie; Xu, Xinwen; Xie, Yijun

2017-01-01

Water influences various physiological and ecological processes of plants in different ecosystems, especially in desert ecosystems. The purpose of this study is to investigate the response of physiological and morphological acclimation of two shrubs Haloxylon ammodendron and Calligonum mongolicunl to variations in irrigation intervals. The irrigation frequency was set as 1-, 2-, 4-, 8- and 12-week intervals respectively from March to October during 2012-2014 to investigate the response of physiological and morphological acclimation of two desert shrubs Haloxylon ammodendron and Calligonum mongolicunl to variations in the irrigation system. The irrigation interval significantly affected the individual-scale carbon acquisition and biomass allocation pattern of both species. Under good water conditions (1- and 2-week intervals), carbon assimilation was significantly higher than other treatments; while, under water shortage conditions (8- and 12-week intervals), there was much defoliation; and under moderate irrigation intervals (4 weeks), the assimilative organs grew gently with almost no defoliation occurring. Both studied species maintained similar ecophysiologically adaptive strategies, while C. mongolicunl was more sensitive to drought stress because of its shallow root system and preferential belowground allocation of resources. A moderate irrigation interval of 4 weeks was a suitable pattern for both plants since it not only saved water but also met the water demands of the plants.

Improving orbit prediction accuracy through supervised machine learning

NASA Astrophysics Data System (ADS)

Peng, Hao; Bai, Xiaoli

2018-05-01

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

High-intensity interval training: Modulating interval duration in overweight/obese men.

PubMed

Smith-Ryan, Abbie E; Melvin, Malia N; Wingfield, Hailee L

2015-05-01

High-intensity interval training (HIIT) is a time-efficient strategy shown to induce various cardiovascular and metabolic adaptations. Little is known about the optimal tolerable combination of intensity and volume necessary for adaptations, especially in clinical populations. In a randomized controlled pilot design, we evaluated the effects of two types of interval training protocols, varying in intensity and interval duration, on clinical outcomes in overweight/obese men. Twenty-five men [body mass index (BMI) > 25 kg · m(2)] completed baseline body composition measures: fat mass (FM), lean mass (LM) and percent body fat (%BF) and fasting blood glucose, lipids and insulin (IN). A graded exercise cycling test was completed for peak oxygen consumption (VO2peak) and power output (PO). Participants were randomly assigned to high-intensity short interval (1MIN-HIIT), high-intensity interval (2MIN-HIIT) or control groups. 1MIN-HIIT and 2MIN-HIIT completed 3 weeks of cycling interval training, 3 days/week, consisting of either 10 × 1 min bouts at 90% PO with 1 min rests (1MIN-HIIT) or 5 × 2 min bouts with 1 min rests at undulating intensities (80%-100%) (2MIN-HIIT). There were no significant training effects on FM (Δ1.06 ± 1.25 kg) or %BF (Δ1.13% ± 1.88%), compared to CON. Increases in LM were not significant but increased by 1.7 kg and 2.1 kg for 1MIN and 2MIN-HIIT groups, respectively. Increases in VO2peak were also not significant for 1MIN (3.4 ml·kg(-1) · min(-1)) or 2MIN groups (2.7 ml · kg(-1) · min(-1)). IN sensitivity (HOMA-IR) improved for both training groups (Δ-2.78 ± 3.48 units; p < 0.05) compared to CON. HIIT may be an effective short-term strategy to improve cardiorespiratory fitness and IN sensitivity in overweight males.

High-intensity interval training: Modulating interval duration in overweight/obese men

PubMed Central

Smith-Ryan, Abbie E.; Melvin, Malia N.; Wingfield, Hailee L.

2015-01-01

Introduction High-intensity interval training (HIIT) is a time-efficient strategy shown to induce various cardiovascular and metabolic adaptations. Little is known about the optimal tolerable combination of intensity and volume necessary for adaptations, especially in clinical populations. Objectives In a randomized controlled pilot design, we evaluated the effects of two types of interval training protocols, varying in intensity and interval duration, on clinical outcomes in overweight/obese men. Methods Twenty-five men [body mass index (BMI) > 25 kg·m2] completed baseline body composition measures: fat mass (FM), lean mass (LM) and percent body fat (%BF) and fasting blood glucose, lipids and insulin (IN). A graded exercise cycling test was completed for peak oxygen consumption (VO2peak) and power output (PO). Participants were randomly assigned to high-intensity short interval (1MIN-HIIT), high-intensity interval (2MIN-HIIT) or control groups. 1MIN-HIIT and 2MIN-HIIT completed 3 weeks of cycling interval training, 3 days/week, consisting of either 10 × 1 min bouts at 90% PO with 1 min rests (1MIN-HIIT) or 5 × 2 min bouts with 1 min rests at undulating intensities (80%–100%) (2MIN-HIIT). Results There were no significant training effects on FM (Δ1.06 ± 1.25 kg) or %BF (Δ1.13% ± 1.88%), compared to CON. Increases in LM were not significant but increased by 1.7 kg and 2.1 kg for 1MIN and 2MIN-HIIT groups, respectively. Increases in VO2peak were also not significant for 1MIN (3.4 ml·kg−1·min−1) or 2MIN groups (2.7 ml·kg−1·min−1). IN sensitivity (HOMA-IR) improved for both training groups (Δ −2.78 ± 3.48 units; p < 0.05) compared to CON. Conclusion HIIT may be an effective short-term strategy to improve cardiorespiratory fitness and IN sensitivity in overweight males. PMID:25913937

Fatigue Exacerbation by Interval or Continuous Exercise in Chronic Fatigue Syndrome.

PubMed

Sandler, Carolina X; Lloyd, Andrew R; Barry, Benjamin K

2016-10-01

The objective of this study is to determine whether the typical exacerbation of symptoms in patients with chronic fatigue syndrome (CFS) after a bout of exercise differs between high-intensity interval training (HIIT) or continuous (CONT) aerobic exercise of the same duration and mechanical work. Participants with specialist-diagnosed CFS performed two 20-min bouts of cycling in a randomized crossover study. The bouts were either moderate-intensity continuous (70% age-predicted HR maximum) or high-intensity interval exercise, separated by at least 2 wk. Self-report questionnaires capturing fatigue, the related symptoms, and actigraphy were collected across 2 d before and 4 d after the exercise. Comparisons between exercise bouts were made using paired sample t-tests. Fourteen moderately affected participants who were unable to work, but not bed bound, completed the study (nine female, 32 ± 10 yr, 67 ± 11 kg). Mechanical work was matched successfully between the exercise bouts (HIIT, 83,037, vs CONT, 83,348 J, P = 0.84). Mean HR (HIIT, 76% ± 5%, vs CONT, 73% ± 6% age-predicted HR maximum, P < 0.05) and RPE (6-20) in the legs (HIIT, 15.4 ± 1.4, vs CONT, 13.2 ± 1.2, P < 0.001) were higher for the interval compared with continuous exercise. Mean fatigue scores (0-10) were similar before each exercise challenge (HIIT, 4.5 ± 1.8, vs CONT, 4.1 ± 1.7, P = 0.43). Participants reported an increase in fatigue scores after both challenges (mean difference: HIIT, 1.0 ± 1.3, P < 0.01; CONT, 1.5 ± 0.7, P < 0.001), but these exacerbations in fatigue were not statistically or clinically different (P = 0.20). High-intensity interval exercise did not exacerbate fatigue any more than continuous exercise of comparable workload. This finding supports evaluation of HIIT in graded exercise therapy interventions for patients with CFS.

[Prediction of the total Japanese cedar pollen counts based on male flower-setting conditions of standard trees].

PubMed

Yuta, Atsushi; Ukai, Kotaro; Sakakura, Yasuo; Tani, Hideshi; Matsuda, Fukiko; Yang, Tian-qun; Majima, Yuichi

2002-07-01

We made a prediction of the Japanese cedar (Cryptomeria japonica) pollen counts at Tsu city based on male flower-setting conditions of standard trees. The 69 standard trees from 23 kinds of clones, planted at Mie Prefecture Science and Technology Promotion Center (Hakusan, Mie) in 1964, were selected. Male flower-setting conditions for 276 faces (69 trees x 4 points of the compass) were scored from 0 to 3. The average of scores and total pollen counts from 1988 to 2000 was analyzed. As the results, the average scores from standard trees and total pollen counts except two mass pollen-scattered years in 1995 and 2000 had a positive correlation (r = 0.914) by linear function. On the mass pollen-scattered years, pollen counts were influenced from the previous year. Therefore, the score of the present year minus that of the previous year were used for analysis. The average scores from male flower-setting conditions and pollen counts had a strong positive correlation (r = 0.994) when positive scores by taking account of the previous year were analyzed. We conclude that prediction of pollen counts are possible based on the male flower-setting conditions of standard trees.

Psychoacoustic Factors in Musical Intonation: Beats, Interval Tuning, and Inharmonicity.

NASA Astrophysics Data System (ADS)

Keislar, Douglas Fleming

Three psychoacoustic experiments were conducted using musically experienced subjects. In the first two experiments, the interval tested was the perfect fifth F4-C5; in the final one it was the major third F4-A4. The beat rate was controlled by two different methods: (1) simply retuning the interval, and (2) frequency-shifting one partial of each pair of beating partials without changing the overall interval tuning. The second method introduces inharmonicity. In addition, two levels of beat amplitude were introduced by using either a complete spectrum of 16 equal-amplitude partials per note, or by deleting one partial from each pair of beating partials. The results of all three experiments indicate that, for these stimuli, beating does not contribute significantly to the percept of "out-of-tuneness," because it made no difference statistically whether the beat amplitude was maximal or minimal. By contrast, mistuning the interval was highly significant. For the fifths, frequency-shifting the appropriate partials had about as much effect on the perceived intonation as mistuning the interval. For thirds, this effect was weaker, presumably since there were fewer inharmonic partials and they were higher in the harmonic series. Subjects were less consistent in their judgments of thirds than of fifths, perhaps because the equal-tempered and just thirds differ noticeably, unlike fifths. Since it is unlikely that beats would be more audible in real musical situations than under these laboratory conditions, these results suggest that the perception of intonation in music is dependent on the actual interval tuning rather than the concomitant beat rate. If beating partials are unimportant vis-a-vis interval tuning, this strengthens the argument for a cultural basis for musical intonation and scales, as opposed to the acoustical basis set forth by Helmholtz and others.

Predictive Accuracy of Sweep Frequency Impedance Technology in Identifying Conductive Conditions in Newborns.

PubMed

Aithal, Venkatesh; Kei, Joseph; Driscoll, Carlie; Murakoshi, Michio; Wada, Hiroshi

2018-02-01

Diagnosing conductive conditions in newborns is challenging for both audiologists and otolaryngologists. Although high-frequency tympanometry (HFT), acoustic stapedial reflex tests, and wideband absorbance measures are useful diagnostic tools, there is performance measure variability in their detection of middle ear conditions. Additional diagnostic sensitivity and specificity measures gained through new technology such as sweep frequency impedance (SFI) measures may assist in the diagnosis of middle ear dysfunction in newborns. The purpose of this study was to determine the test performance of SFI to predict the status of the outer and middle ear in newborns against commonly used reference standards. Automated auditory brainstem response (AABR), HFT (1000 Hz), transient evoked otoacoustic emission (TEOAE), distortion product otoacoustic emission (DPOAE), and SFI tests were administered to the study sample. A total of 188 neonates (98 males and 90 females) with a mean gestational age of 39.4 weeks were included in the sample. Mean age at the time of testing was 44.4 hr. Diagnostic accuracy of SFI was assessed in terms of its ability to identify conductive conditions in neonates when compared with nine different reference standards (including four single tests [AABR, HFT, TEOAE, and DPOAE] and five test batteries [HFT + DPOAE, HFT + TEOAE, DPOAE + TEOAE, DPOAE + AABR, and TEOAE + AABR]), using receiver operating characteristic (ROC) analysis and traditional test performance measures such as sensitivity and specificity. The test performance of SFI against the test battery reference standard of HFT + DPOAE and single reference standard of HFT was high with an area under the ROC curve (AROC) of 0.87 and 0.82, respectively. Although the HFT + DPOAE test battery reference standard performed better than the HFT reference standard in predicting middle ear conductive conditions in neonates, the difference in AROC was not significant. Further analysis revealed that the

Sampling Theory and Confidence Intervals for Effect Sizes: Using ESCI To Illustrate "Bouncing"; Confidence Intervals.

ERIC Educational Resources Information Center

Du, Yunfei

This paper discusses the impact of sampling error on the construction of confidence intervals around effect sizes. Sampling error affects the location and precision of confidence intervals. Meta-analytic resampling demonstrates that confidence intervals can haphazardly bounce around the true population parameter. Special software with graphical…

Research on regional numerical weather prediction

NASA Technical Reports Server (NTRS)

Kreitzberg, C. W.

1976-01-01

Extension of the predictive power of dynamic weather forecasting to scales below the conventional synoptic or cyclonic scales in the near future is assessed. Lower costs per computation, more powerful computers, and a 100 km mesh over the North American area (with coarser mesh extending beyond it) are noted at present. Doubling the resolution even locally (to 50 km mesh) would entail a 16-fold increase in costs (including vertical resolution and halving the time interval), and constraints on domain size and length of forecast. Boundary conditions would be provided by the surrounding 100 km mesh, and time-varying lateral boundary conditions can be considered to handle moving phenomena. More physical processes to treat, more efficient numerical techniques, and faster computers (improved software and hardware) backing up satellite and radar data could produce further improvements in forecasting in the 1980s. Boundary layer modeling, initialization techniques, and quantitative precipitation forecasting are singled out among key tasks.

Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace.

PubMed

Gama, Repson; Van Dyk, J Susan; Burton, Mike H; Pletschke, Brett I

2017-06-01

The enzymatic degradation of lignocellulosic biomass such as apple pomace is a complex process influenced by a number of hydrolysis conditions. Predicting optimal conditions, including enzyme and substrate concentration, temperature and pH can improve conversion efficiency. In this study, the production of sugar monomers from apple pomace using commercial enzyme preparations, Celluclast 1.5L, Viscozyme L and Novozyme 188 was investigated. A limited number of experiments were carried out and then analysed using an artificial neural network (ANN) to model the enzymatic hydrolysis process. The ANN was used to simulate the enzymatic hydrolysis process for a range of input variables and the optimal conditions were successfully selected as was indicated by the R 2 value of 0.99 and a small MSE value. The inputs for the ANN were substrate loading, enzyme loading, temperature, initial pH and a combination of these parameters, while release profiles of glucose and reducing sugars were the outputs. Enzyme loadings of 0.5 and 0.2 mg/g substrate and a substrate loading of 30% were optimal for glucose and reducing sugar release from apple pomace, respectively, resulting in concentrations of 6.5 g/L glucose and 28.9 g/L reducing sugars. Apple pomace hydrolysis can be successfully carried out based on the predicted optimal conditions from the ANN.

Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

NASA Technical Reports Server (NTRS)

Lo, Ching F.

1999-01-01

The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.

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

A contracting-interval program for the Danilewski method. Ph.D. Thesis - Va. Univ.

NASA Technical Reports Server (NTRS)

Harris, J. D.

1971-01-01

The concept of contracting-interval programs is applied to finding the eigenvalues of a matrix. The development is a three-step process in which (1) a program is developed for the reduction of a matrix to Hessenberg form, (2) a program is developed for the reduction of a Hessenberg matrix to colleague form, and (3) the characteristic polynomial with interval coefficients is readily obtained from the interval of colleague matrices. This interval polynomial is then factored into quadratic factors so that the eigenvalues may be obtained. To develop a contracting-interval program for factoring this polynomial with interval coefficients it is necessary to have an iteration method which converges even in the presence of controlled rounding errors. A theorem is stated giving sufficient conditions for the convergence of Newton's method when both the function and its Jacobian cannot be evaluated exactly but errors can be made proportional to the square of the norm of the difference between the previous two iterates. This theorem is applied to prove the convergence of the generalization of the Newton-Bairstow method that is used to obtain quadratic factors of the characteristic polynomial.

Moderate irrigation intervals facilitate establishment of two desert shrubs in the Taklimakan Desert Highway Shelterbelt in China

PubMed Central

Li, Congjuan; Shi, Xiang; Mohamad, Osama Abdalla; Gao, Jie; Xu, Xinwen; Xie, Yijun

2017-01-01

Background Water influences various physiological and ecological processes of plants in different ecosystems, especially in desert ecosystems. The purpose of this study is to investigate the response of physiological and morphological acclimation of two shrubs Haloxylon ammodendron and Calligonum mongolicunl to variations in irrigation intervals. Methodology/Principal findings The irrigation frequency was set as 1-, 2-, 4-, 8- and 12-week intervals respectively from March to October during 2012–2014 to investigate the response of physiological and morphological acclimation of two desert shrubs Haloxylon ammodendron and Calligonum mongolicunl to variations in the irrigation system. The irrigation interval significantly affected the individual-scale carbon acquisition and biomass allocation pattern of both species. Under good water conditions (1- and 2-week intervals), carbon assimilation was significantly higher than other treatments; while, under water shortage conditions (8- and 12-week intervals), there was much defoliation; and under moderate irrigation intervals (4 weeks), the assimilative organs grew gently with almost no defoliation occurring. Conclusion/Significance Both studied species maintained similar ecophysiologically adaptive strategies, while C. mongolicunl was more sensitive to drought stress because of its shallow root system and preferential belowground allocation of resources. A moderate irrigation interval of 4 weeks was a suitable pattern for both plants since it not only saved water but also met the water demands of the plants. PMID:28719623

The 32nd CDC: Robust stabilizer synthesis for interval plants using Nevanlina-pick theory

NASA Technical Reports Server (NTRS)

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

1989-01-01

The synthesis of robustly stabilizing compensators for interval plants, i.e., plants whose parameters vary within prescribed ranges is discussed. Well-known H(sup infinity) methods are used to establish robust stabilizability conditions for a family of plants and also to synthesize controllers that would stabilize the whole family. Though conservative, these methods give a very simple way to come up with a family of robust stabilizers for an interval plant.

Fatigue Behavior and Modeling of Additively Manufactured Ti-6Al-4V Including Interlayer Time Interval Effects

NASA Astrophysics Data System (ADS)

Torries, Brian; Shamsaei, Nima

2017-12-01

The effects of different cooling rates, as achieved by varying the interlayer time interval, on the fatigue behavior of additively manufactured Ti-6Al-4V specimens were investigated and modeled via a microstructure-sensitive fatigue model. Comparisons are made between two sets of specimens fabricated via Laser Engineered Net Shaping (LENS™), with variance in interlayer time interval accomplished by depositing either one or two specimens per print operation. Fully reversed, strain-controlled fatigue tests were conducted, with fractography following specimen failure. A microstructure-sensitive fatigue model was calibrated to model the fatigue behavior of both sets of specimens and was found to be capable of correctly predicting the longer fatigue lives of the single-built specimens and the reduced scatter of the double-built specimens; all data points fell within the predicted upper and lower bounds of fatigue life. The time interval effects and the ability to be modeled are important to consider when producing test specimens that are smaller than the production part (i.e., property-performance relationships).

Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Non-invasive Estimation of Cardiac Time Intervals.

PubMed

Khosrow-Khavar, Farzad; Tavakolian, Kouhyar; Blaber, Andrew; Menon, Carlo

2016-10-12

The purpose of this research was to design a delineation algorithm that could detect specific fiducial points of the seismocardiogram (SCG) signal with or without using the electrocardiogram (ECG) R-wave as the reference point. The detected fiducial points were used to estimate cardiac time intervals. Due to complexity and sensitivity of the SCG signal, the algorithm was designed to robustly discard the low-quality cardiac cycles, which are the ones that contain unrecognizable fiducial points. The algorithm was trained on a dataset containing 48,318 manually annotated cardiac cycles. It was then applied to three test datasets: 65 young healthy individuals (dataset 1), 15 individuals above 44 years old (dataset 2), and 25 patients with previous heart conditions (dataset 3). The algorithm accomplished high prediction accuracy with the rootmean- square-error of less than 5 ms for all the test datasets. The algorithm overall mean detection rate per individual recordings (DRI) were 74, 68, and 42 percent for the three test datasets when concurrent ECG and SCG were used. For the standalone SCG case, the mean DRI was 32, 14 and 21 percent. When the proposed algorithm applied to concurrent ECG and SCG signals, the desired fiducial points of the SCG signal were successfully estimated with a high detection rate. For the standalone case, however, the algorithm achieved high prediction accuracy and detection rate for only the young individual dataset. The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.

Hematology and plasma chemistry reference intervals for cultured tilapia (Oreochromis hybrid).

PubMed

Hrubec, Terry C.; Cardinale, Jenifer L.; Smith, Stephen A.

2000-01-01

Tilapia are a commonly aquacultured fish yet little is known about their normal physiology and response to disease. In this study we determined the results of complete hematologic (n=40) and plasma biochemical profiles (n=63) in production tilapia (Oreochromis hybrids). The fish were raised in recirculating systems with a high stocking density (120 g/L), and were in the middle of a 15-month production cycle. Blood was analyzed using standard techniques, and reference intervals were determined using nonparametric methods. Non-production tilapia (n=15) from low-density tanks (4 g/L) also were sampled; the clinical chemistry results were compared to reference intervals from the fish raised in high-density tanks. Differences were noted in plasma protein, calcium and phosphorus concentrations, such that reference intervals for high-density production tilapia were not applicable to fish raised under different environmental and management conditions.

A Comparison of Various Stress Rupture Life Models for Orbiter Composite Pressure Vessels and Confidence Intervals

NASA Technical Reports Server (NTRS)

Grimes-Ledesma, Lorie; Murthy, Pappu L. N.; Phoenix, S. Leigh; Glaser, Ronald

2007-01-01

In conjunction with a recent NASA Engineering and Safety Center (NESC) investigation of flight worthiness of Kevlar Overwrapped Composite Pressure Vessels (COPVs) on board the Orbiter, two stress rupture life prediction models were proposed independently by Phoenix and by Glaser. In this paper, the use of these models to determine the system reliability of 24 COPVs currently in service on board the Orbiter is discussed. The models are briefly described, compared to each other, and model parameters and parameter uncertainties are also reviewed to understand confidence in reliability estimation as well as the sensitivities of these parameters in influencing overall predicted reliability levels. Differences and similarities in the various models will be compared via stress rupture reliability curves (stress ratio vs. lifetime plots). Also outlined will be the differences in the underlying model premises, and predictive outcomes. Sources of error and sensitivities in the models will be examined and discussed based on sensitivity analysis and confidence interval determination. Confidence interval results and their implications will be discussed for the models by Phoenix and Glaser.

One-way ANOVA based on interval information

NASA Astrophysics Data System (ADS)

Hesamian, Gholamreza

2016-08-01

This paper deals with extending the one-way analysis of variance (ANOVA) to the case where the observed data are represented by closed intervals rather than real numbers. In this approach, first a notion of interval random variable is introduced. Especially, a normal distribution with interval parameters is introduced to investigate hypotheses about the equality of interval means or test the homogeneity of interval variances assumption. Moreover, the least significant difference (LSD method) for investigating multiple comparison of interval means is developed when the null hypothesis about the equality of means is rejected. Then, at a given interval significance level, an index is applied to compare the interval test statistic and the related interval critical value as a criterion to accept or reject the null interval hypothesis of interest. Finally, the method of decision-making leads to some degrees to accept or reject the interval hypotheses. An applied example will be used to show the performance of this method.

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

PubMed

Kim, Minseung; Zorraquino, Violeta; Tagkopoulos, Ilias

2015-03-01

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

Pavlovian conditioning and cumulative reinforcement rate.

PubMed

Harris, Justin A; Patterson, Angela E; Gharaei, Saba

2015-04-01

In 5 experiments using delay conditioning of magazine approach with rats, reinforcement rate was varied either by manipulating the mean interval between onset of the conditioned stimulus (CS) and unconditioned stimulus (US) or by manipulating the proportion of CS presentations that ended with the US (trial-based reinforcement rate). Both manipulations influenced the acquisition of responding. In each experiment, a specific comparison was made between 2 CSs that differed in their mean CS-US interval and in their trial-based reinforcement rate, such that the cumulative reinforcement rate-the cumulative duration of the CS between reinforcements-was the same for the 2 CSs. For example, a CS reinforced on 100% of trials with a mean CS-US interval of 60 s was compared with a CS reinforced on 33% of trials and a mean duration of 20 s. Across the 5 experiments, conditioning was virtually identical for the 2 CSs with matched cumulative reinforcement rate. This was true as long as the timing of the US was unpredictable and, thus, response rates were uniform across the length of the CS. We conclude that the effects of CS-US interval and of trial-based reinforcement rate are reducible entirely to their common effect on cumulative reinforcement rate. We discuss the implications of this for rate-based, trial-based, and real-time associative models of conditioning. (c) 2015 APA, all rights reserved).

Lean mass predicts conditioned pain modulation in adolescents across weight status.

PubMed

Stolzman, S; Hoeger Bement, M

2016-07-01

There is a wide continuum of conditioned pain modulation (CPM) in adults with older adults experiencing an attenuated CPM response compared with younger adults. Less is known for adolescents and the role of anthropometrics. Fifty-six adolescents (15.1 ± 1.8 years; 32 normal weight and 24 overweight/obese; 27 boys) completed in a CPM session that included anthropometric testing. Pressure pain thresholds were measured at the nailbed and deltoid muscle (test stimuli) with the foot submerged in a cool or ice water bath (conditioning stimulus). Weight status, body composition (Dual-energy X-ray absorptiometry scan), physical activity levels and clinical pain were also evaluated. The CPM response in adolescents was similar across sites (nailbed vs. deltoid), weight status (normal vs. overweight/obese) and sex. CPM measured at the deltoid muscle was positively associated with left arm lean mass but not fat mass; lean mass of the arm uniquely predicted 10% of the CPM magnitude. CPM measured at the nailbed was positively correlated with physical activity levels. These results suggest that lean mass and physical activity levels may contribute to endogenous pain inhibition in adolescents across weight status. © 2016 European Pain Federation - EFIC®

Decadal climate predictions improved by ocean ensemble dispersion filtering

NASA Astrophysics Data System (ADS)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.