Science.gov

Sample records for activation time prediction

  1. Real-time Neural Network predictions of geomagnetic activity indices

    NASA Astrophysics Data System (ADS)

    Bala, R.; Reiff, P. H.

    2009-12-01

    The Boyle potential or the Boyle Index (BI), Φ (kV)=10-4 (V/(km/s))2 + 11.7 (B/nT) sin3(θ/2), is an empirically-derived formula that can characterize the Earth's polar cap potential, which is readily derivable in real time using the solar wind data from ACE (Advanced Composition Explorer). The BI has a simplistic form that utilizes a non-magnetic "viscous" and a magnetic "merging" component to characterize the magnetospheric behavior in response to the solar wind. We have investigated its correlation with two of conventional geomagnetic activity indices in Kp and the AE index. We have shown that the logarithms of both 3-hr and 1-hr averages of the BI correlate well with the subsequent Kp: Kp = 8.93 log10(BI) - 12.55 along with 1-hr BI correlating with the subsequent log10(AE): log10(AE) = 1.78 log10(BI) - 3.6. We have developed a new set of algorithms based on Artificial Neural Networks (ANNs) suitable for short term space weather forecasts with an enhanced lead-time and better accuracy in predicting Kp and AE over some leading models; the algorithms omit the time history of its targets to utilize only the solar wind data. Inputs to our ANN models benefit from the BI and its proven record as a forecasting parameter since its initiation in October, 2003. We have also performed time-sensitivity tests using cross-correlation analysis to demonstrate that our models are as efficient as those that incorporates the time history of the target indices in their inputs. Our algorithms can predict the upcoming full 3-hr Kp, purely from the solar wind data and achieve a linear correlation coefficient of 0.840, which means that it predicts the upcoming Kp value on average to within 1.3 step, which is approximately the resolution of the real-time Kp estimate. Our success in predicting Kp during a recent unexpected event (22 July ’09) is shown in the figure. Also, when predicting an equivalent "one hour Kp'', the correlation coefficient is 0.86, meaning on average a prediction

  2. Predictive active disturbance rejection control for processes with time delay.

    PubMed

    Zheng, Qinling; Gao, Zhiqiang

    2014-07-01

    Active disturbance rejection control (ADRC) has been shown to be an effective tool in dealing with real world problems of dynamic uncertainties, disturbances, nonlinearities, etc. This paper addresses its existing limitations with plants that have a large transport delay. In particular, to overcome the delay, the extended state observer (ESO) in ADRC is modified to form a predictive ADRC, leading to significant improvements in the transient response and stability characteristics, as shown in extensive simulation studies and hardware-in-the-loop tests, as well as in the frequency response analysis. In this research, it is assumed that the amount of delay is approximately known, as is the approximated model of the plant. Even with such uncharacteristic assumptions for ADRC, the proposed method still exhibits significant improvements in both performance and robustness over the existing methods such as the dead-time compensator based on disturbance observer and the Filtered Smith Predictor, in the context of some well-known problems of chemical reactor and boiler control problems.

  3. Cortical delta activity reflects reward prediction error and related behavioral adjustments, but at different times.

    PubMed

    Cavanagh, James F

    2015-04-15

    Recent work has suggested that reward prediction errors elicit a positive voltage deflection in the scalp-recorded electroencephalogram (EEG); an event sometimes termed a reward positivity. However, a strong test of this proposed relationship remains to be defined. Other important questions remain unaddressed: such as the role of the reward positivity in predicting future behavioral adjustments that maximize reward. To answer these questions, a three-armed bandit task was used to investigate the role of positive prediction errors during trial-by-trial exploration and task-set based exploitation. The feedback-locked reward positivity was characterized by delta band activities, and these related EEG features scaled with the degree of a computationally derived positive prediction error. However, these phenomena were also dissociated: the computational model predicted exploitative action selection and related response time speeding whereas the feedback-locked EEG features did not. Compellingly, delta band dynamics time-locked to the subsequent bandit (the P3) successfully predicted these behaviors. These bandit-locked findings included an enhanced parietal to motor cortex delta phase lag that correlated with the degree of response time speeding, suggesting a mechanistic role for delta band activities in motivating action selection. This dissociation in feedback vs. bandit locked EEG signals is interpreted as a differentiation in hierarchically distinct types of prediction error, yielding novel predictions about these dissociable delta band phenomena during reinforcement learning and decision making.

  4. Single-trial prediction of reaction time variability from MEG brain activity

    PubMed Central

    Ohata, Ryu; Ogawa, Kenji; Imamizu, Hiroshi

    2016-01-01

    Neural activity prior to movement onset contains essential information for predictive assistance for humans using brain-machine-interfaces (BMIs). Even though previous studies successfully predicted different goals for upcoming movements, it is unclear whether non-invasive recording signals contain the information to predict trial-by-trial behavioral variability under the same movement. In this paper, we examined the predictability of subsequent short or long reaction times (RTs) from magnetoencephalography (MEG) signals in a delayed-reach task. The difference in RTs was classified significantly above chance from 550 ms before the go-signal onset using the cortical currents in the premotor cortex. Significantly above-chance classification was performed in the lateral prefrontal and the right inferior parietal cortices at the late stage of the delay period. Thus, inter-trial variability in RTs is predictable information. Our study provides a proof-of-concept of the future development of non-invasive BMIs to prevent delayed movements. PMID:27250872

  5. Prediction of geomagnetic activity on time scales of one to ten years

    NASA Technical Reports Server (NTRS)

    Feynman, J.; Gu, X. Y.

    1986-01-01

    The long-term prediction of geomagnetic indices that characterize the state of the magnetosphere is discussed. While a prediction of the yearly average sunspot number is simultaneously a prediction of the yearly number of sudden-commencement storms, it is not a prediction of the number of disturbed or quiet half days. Knowledge of the sunspot cycle phase leads to a good estimate of the correlation expected between activity during one 27-day solar rotation period and the next.

  6. Scale Development for Measuring and Predicting Adolescents’ Leisure Time Physical Activity Behavior

    PubMed Central

    Ries, Francis; Romero Granados, Santiago; Arribas Galarraga, Silvia

    2009-01-01

    The aim of this study was to develop a scale for assessing and predicting adolescents’ physical activity behavior in Spain and Luxembourg using the Theory of Planned Behavior as a framework. The sample was comprised of 613 Spanish (boys = 309, girls = 304; M age =15.28, SD =1.127) and 752 Luxembourgish adolescents (boys = 343, girls = 409; M age = 14.92, SD = 1.198), selected from students of two secondary schools in both countries, with a similar socio-economic status. The initial 43-items were all scored on a 4-point response format using the structured alternative format and translated into Spanish, French and German. In order to ensure the accuracy of the translation, standardized parallel back-translation techniques were employed. Following two pilot tests and subsequent revisions, a second order exploratory factor analysis with oblimin direct rotation was used for factor extraction. Internal consistency and test-retest reliabilities were also tested. The 4-week test-retest correlations confirmed the items’ time stability. The same five factors were obtained, explaining 63.76% and 63.64% of the total variance in both samples. Internal consistency for the five factors ranged from α = 0.759 to α = 0. 949 in the Spanish sample and from α = 0.735 to α = 0.952 in the Luxembourgish sample. For both samples, inter-factor correlations were all reported significant and positive, except for Factor 5 where they were significant but negative. The high internal consistency of the subscales, the reported item test-retest reliabilities and the identical factor structure confirm the adequacy of the elaborated questionnaire for assessing the TPB-based constructs when used with a population of adolescents in Spain and Luxembourg. The results give some indication that they may have value in measuring the hypothesized TPB constructs for PA behavior in a cross-cultural context. Key points When using the structured alternative format, weak internal consistency was obtained

  7. Real-time prediction of neuronal population spiking activity using FPGA.

    PubMed

    Li, Will X Y; Cheung, Ray C C; Chan, Rosa H M; Song, Dong; Berger, Theodore W

    2013-08-01

    A field-programmable gate array (FPGA)-based hardware architecture is proposed and utilized for prediction of neuronal population firing activity. The hardware system adopts the multi-input multi-output (MIMO) generalized Laguerre-Volterra model (GLVM) structure to describe the nonlinear dynamic neural process of mammalian brain and can switch between the two important functions: estimation of GLVM coefficients and prediction of neuronal population spiking activity (model outputs). The model coefficients are first estimated using the in-sample training data; then the output is predicted using the out-of-sample testing data and the field estimated coefficients. Test results show that compared with previous software implementation of the generalized Laguerre-Volterra algorithm running on an Intel Core i7-2620M CPU, the FPGA-based hardware system can achieve up to 2.66×10(3) speedup in doing model parameters estimation and 698.84 speedup in doing model output prediction. The proposed hardware platform will facilitate research on the highly nonlinear neural process of the mammal brain, and the cognitive neural prosthesis design.

  8. Occipital MEG Activity in the Early Time Range (<300 ms) Predicts Graded Changes in Perceptual Consciousness.

    PubMed

    Andersen, Lau M; Pedersen, Michael N; Sandberg, Kristian; Overgaard, Morten

    2016-06-01

    Two electrophysiological components have been extensively investigated as candidate neural correlates of perceptual consciousness: An early, occipitally realized component occurring 130-320 ms after stimulus onset and a late, frontally realized component occurring 320-510 ms after stimulus onset. Recent studies have suggested that the late component may not be uniquely related to perceptual consciousness, but also to sensory expectations, task associations, and selective attention. We conducted a magnetoencephalographic study; using multivariate analysis, we compared classification accuracies when decoding perceptual consciousness from the 2 components using sources from occipital and frontal lobes. We found that occipital sources during the early time range were significantly more accurate in decoding perceptual consciousness than frontal sources during both the early and late time ranges. These results are the first of its kind where the predictive values of the 2 components are quantitatively compared, and they provide further evidence for the primary importance of occipital sources in realizing perceptual consciousness. The results have important consequences for current theories of perceptual consciousness, especially theories emphasizing the role of frontal sources.

  9. Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: implications of age.

    PubMed

    Strath, Scott J; Kate, Rohit J; Keenan, Kevin G; Welch, Whitney A; Swartz, Ann M

    2015-11-01

    To develop and test time series single site and multi-site placement models, we used wrist, hip and ankle processed accelerometer data to estimate energy cost and type of physical activity in adults. Ninety-nine subjects in three age groups (18-39, 40-64, 65 +  years) performed 11 activities while wearing three triaxial accelereometers: one each on the non-dominant wrist, hip, and ankle. During each activity net oxygen cost (METs) was assessed. The time series of accelerometer signals were represented in terms of uniformly discretized values called bins. Support Vector Machine was used for activity classification with bins and every pair of bins used as features. Bagged decision tree regression was used for net metabolic cost prediction. To evaluate model performance we employed the jackknife leave-one-out cross validation method. Single accelerometer and multi-accelerometer site model estimates across and within age group revealed similar accuracy, with a bias range of -0.03 to 0.01 METs, bias percent of -0.8 to 0.3%, and a rMSE range of 0.81-1.04 METs. Multi-site accelerometer location models improved activity type classification over single site location models from a low of 69.3% to a maximum of 92.8% accuracy. For each accelerometer site location model, or combined site location model, percent accuracy classification decreased as a function of age group, or when young age groups models were generalized to older age groups. Specific age group models on average performed better than when all age groups were combined. A time series computation show promising results for predicting energy cost and activity type. Differences in prediction across age group, a lack of generalizability across age groups, and that age group specific models perform better than when all ages are combined needs to be considered as analytic calibration procedures to detect energy cost and type are further developed.

  10. Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: implications of age

    PubMed Central

    Strath, Scott J; Kate, Rohit J; Keenan, Kevin G; Welch, Whitney A; Swartz, Ann M

    2016-01-01

    To develop and test time series single site and multi-site placement models, we used wrist, hip and ankle processed accelerometer data to estimate energy cost and type of physical activity in adults. Ninety-nine subjects in three age groups (18–39, 40–64, 65 + years) performed 11 activities while wearing three triaxial accelereometers: one each on the non-dominant wrist, hip, and ankle. During each activity net oxygen cost (METs) was assessed. The time series of accelerometer signals were represented in terms of uniformly discretized values called bins. Support Vector Machine was used for activity classification with bins and every pair of bins used as features. Bagged decision tree regression was used for net metabolic cost prediction. To evaluate model performance we employed the jackknife leave-one-out cross validation method. Single accelerometer and multi-accelerometer site model estimates across and within age group revealed similar accuracy, with a bias range of −0.03 to 0.01 METs, bias percent of −0.8 to 0.3%, and a rMSE range of 0.81–1.04 METs. Multi-site accelerometer location models improved activity type classification over single site location models from a low of 69.3% to a maximum of 92.8% accuracy. For each accelerometer site location model, or combined site location model, percent accuracy classification decreased as a function of age group, or when young age groups models were generalized to older age groups. Specific age group models on average performed better than when all age groups were combined. A time series computation show promising results for predicting energy cost and activity type. Differences in prediction across age group, a lack of generalizability across age groups, and that age group specific models perform better than when all ages are combined needs to be considered as analytic calibration procedures to detect energy cost and type are further developed. PMID:26449155

  11. Moderate-to-vigorous physical activity, but not sedentary time, predicts changes in cardiometabolic risk factors in 10-y-old children: the Active Smarter Kids Study.

    PubMed

    Skrede, Turid; Stavnsbo, Mette; Aadland, Eivind; Aadland, Katrine N; Anderssen, Sigmund A; Resaland, Geir K; Ekelund, Ulf

    2017-04-05

    Background: Cross-sectional data have suggested an inverse relation between physical activity and cardiometabolic risk factors that is independent of sedentary time. However, little is known about which subcomponent of physical activity may predict cardiometabolic risk factors in youths.Objective: We examined the independent prospective associations between objectively measured sedentary time and subcomponents of physical activity with individual and clustered cardiometabolic risk factors in healthy children aged 10 y.Design: We included 700 children (49.1% males; 50.9% females) in which sedentary time and physical activity were measured with the use of accelerometry. Systolic blood pressure, waist circumference (WC), and fasting blood sample (total cholesterol, high-density lipoprotein cholesterol, triglycerides, glucose, fasting insulin) were measured with the use of standard clinical methods and analyzed individually and as a clustered cardiometabolic risk score standardized by age and sex (z score). Exposure and outcome variables were measured at baseline and at follow-up 7 mo later.Results: Sedentary time was not associated with any of the individual cardiometabolic risk factors or clustered cardiometabolic risk in prospective analyses. Moderate physical activity at baseline predicted higher concentrations of triglycerides (P = 0.021) and homeostatic model assessment for insulin resistance (P = 0.027) at follow-up independent of sex, socioeconomic status, Tanner stage, monitor wear time, or WC. Moderate-to-vigorous physical activity (P = 0.043) and vigorous physical activity (P = 0.028) predicted clustered cardiometabolic risk at follow-up, but these associations were attenuated after adjusting for WC.Conclusions: Physical activity, but not sedentary time, is prospectively associated with cardiometabolic risk in healthy children. Public health strategies aimed at improving children's cardiometabolic profile should strive for increasing physical activity of at

  12. Perceptual distortions in pitch and time reveal active prediction and support for an auditory pitch-motion hypothesis.

    PubMed

    Henry, Molly J; McAuley, J Devin

    2013-01-01

    A number of accounts of human auditory perception assume that listeners use prior stimulus context to generate predictions about future stimulation. Here, we tested an auditory pitch-motion hypothesis that was developed from this perspective. Listeners judged either the time change (i.e., duration) or pitch change of a comparison frequency glide relative to a standard (referent) glide. Under a constant-velocity assumption, listeners were hypothesized to use the pitch velocity (Δf/Δt) of the standard glide to generate predictions about the pitch velocity of the comparison glide, leading to perceptual distortions along the to-be-judged dimension when the velocities of the two glides differed. These predictions were borne out in the pattern of relative points of subjective equality by a significant three-way interaction between the velocities of the two glides and task. In general, listeners' judgments along the task-relevant dimension (pitch or time) were affected by expectations generated by the constant-velocity standard, but in an opposite manner for the two stimulus dimensions. When the comparison glide velocity was faster than the standard, listeners overestimated time change, but underestimated pitch change, whereas when the comparison glide velocity was slower than the standard, listeners underestimated time change, but overestimated pitch change. Perceptual distortions were least evident when the velocities of the standard and comparison glides were matched. Fits of an imputed velocity model further revealed increasingly larger distortions at faster velocities. The present findings provide support for the auditory pitch-motion hypothesis and add to a larger body of work revealing a role for active prediction in human auditory perception.

  13. Predicting Nonlinear Time Series

    DTIC Science & Technology

    1993-12-01

    response becomes R,(k) = f (Y FV,(k)) (2.4) where Wy specifies the weight associated with the output of node i to the input of nodej in the next layer and...interconnections for each of these previous nodes. 18 prr~~~o• wfe :t iam i -- ---- --- --- --- Figure 5: Delay block for ATNN [9] Thus, nodej receives the...computed values, aj(tn), and dj(tn) denotes the desired output of nodej at time in. In this thesis, the weights and time delays update after each input

  14. Predictive coding of multisensory timing

    PubMed Central

    Shi, Zhuanghua; Burr, David

    2016-01-01

    The sense of time is foundational for perception and action, yet it frequently departs significantly from physical time. In the paper we review recent progress on temporal contextual effects, multisensory temporal integration, temporal recalibration, and related computational models. We suggest that subjective time arises from minimizing prediction errors and adaptive recalibration, which can be unified in the framework of predictive coding, a framework rooted in Helmholtz’s ‘perception as inference’. PMID:27695705

  15. Predicting river travel time from hydraulic characteristics

    USGS Publications Warehouse

    Jobson, H.E.

    2001-01-01

    Predicting the effect of a pollutant spill on downstream water quality is primarily dependent on the water velocity, longitudinal mixing, and chemical/physical reactions. Of these, velocity is the most important and difficult to predict. This paper provides guidance on extrapolating travel-time information from one within bank discharge to another. In many cases, a time series of discharge (such as provided by a U.S. Geological Survey stream gauge) will provide an excellent basis for this extrapolation. Otherwise, the accuracy of a travel time extrapolation based on a resistance equation can be greatly improved by assuming the total flow area is composed of two parts, an active and an inactive area. For 60 reaches of 12 rivers with slopes greater than about 0.0002, travel times could be predicted to within about 10% by computing the active flow area using the Manning equation with n = 0.035 and assuming a constant inactive area for each reach. The predicted travel times were not very sensitive to the assumed values of bed slope or channel width.

  16. Leveraging the MJO for Predicting Envelopes of Tropical Wave and Synoptic Activity at Multi-Week Lead Times

    DTIC Science & Technology

    2013-09-30

    other components of the climate and weather systems. In this project, we will focus on its interactions with important higher-frequency tropical modes...synoptic waves associated with the MJO, thus contributing to forming a seamless bridge between weather and climate prediction. OBJECTIVES The main...NOAA/GFDL HiRAM ( climate ) and NAVGEM (weather forecast) GCMs to more fully characterize these models’ fidelity in simulating and forecasting MJO

  17. Neuro-fuzzy modeling to predict physicochemical and microbiological parameters of partially dried cherry tomato during storage: effects on water activity, temperature and storage time.

    PubMed

    Tao, Yang; Li, Yong; Zhou, Ruiyun; Chu, Dinh-Toi; Su, Lijuan; Han, Yongbin; Zhou, Jianzhong

    2016-10-01

    In the study, osmotically dehydrated cherry tomatoes were partially dried to water activity between 0.746 and 0.868, vacuum-packed and stored at 4-30 °C for 60 days. Adaptive neuro-fuzzy inference system (ANFIS) was utilized to predict the physicochemical and microbiological parameters of these partially dried cherry tomatoes during storage. Satisfactory accuracies were obtained when ANFIS was used to predict the lycopene and total phenolic contents, color and microbial contamination. The coefficients of determination for all the ANFIS models were higher than 0.86 and showed better performance for prediction compared with models developed by response surface methodology. Through ANFIS modeling, the effects of storage conditions on the properties of partially dried cherry tomatoes were visualized. Generally, contents of lycopene and total phenolics decreased with the increase in water activity, temperature and storage time, while aerobic plate count and number of yeasts and molds increased at high water activities and temperatures. Overall, ANFIS approach can be used as an effective tool to study the quality decrease and microbial pollution of partially dried cherry tomatoes during storage, as well as identify the suitable preservation conditions.

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

    PubMed

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

    2011-01-01

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

  19. Do time perspective and sensation-seeking predict quitting activity among smokers? Findings from the International Tobacco Control (ITC) Four Country Survey.

    PubMed

    Hall, Peter A; Fong, Geoffrey T; Yong, Hua-Hie; Sansone, Genevieve; Borland, Ron; Siahpush, Mohammad

    2012-12-01

    Personality factors such as time perspective and sensation-seeking have been shown to predict smoking uptake. However, little is known about the influences of these variables on quitting behavior, and no prior studies have examined the association cross-nationally in a large probability sample. In the current study it was hypothesized that future time perspective would enhance - while sensation-seeking would inhibit - quitting activity among smokers. It was anticipated that the effects would be similar across English speaking countries. Using a prospective cohort design, this cross-national study of adult smokers (N=8845) examined the associations among time perspective, sensation-seeking and quitting activity using the first three waves of data gathered from the International Tobacco Control Four Country Survey (ITC-4), a random digit dialed telephone survey of adult smokers from the United Kingdom, United States, Canada and Australia. Findings revealed that future time perspective (but not sensation-seeking) was a significant predictor of quitting attempts over the 8-month follow-up after adjusting for socio-demographic variables, factors known to inhibit quitting (e.g., perceived addiction, enjoyment of smoking, and perceived value of smoking), and factors known to enhance quitting (e.g., quit intention strength, perceived benefit of quitting, concerns about health effects of smoking). The latter, particularly intention, were significant mediators of the effect of time perspective on quitting activity. The effects of time perspective on quitting activity were similar across all four English speaking countries sampled. If these associations are causal in nature, it may be the case that interventions and health communications that enhance future-orientation may foster more quit attempts among current smokers.

  20. Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling

    PubMed Central

    Hill, Yolanda R.; Child, Nick; Hanson, Ben; Wallman, Mikael; Coronel, Ruben; Plank, Gernot; Rinaldi, Christopher A.; Gill, Jaswinder; Smith, Nicolas P.; Taggart, Peter; Bishop, Martin J.

    2016-01-01

    Exit sites associated with scar-related reentrant arrhythmias represent important targets for catheter ablation therapy. However, their accurate location in a safe and robust manner remains a significant clinical challenge. We recently proposed a novel quantitative metric (termed the Reentry Vulnerability Index, RVI) to determine the difference between activation and repolarisation intervals measured from pairs of spatial locations during premature stimulation to accurately locate the critical site of reentry formation. In the clinic, the method showed potential to identify regions of low RVI corresponding to areas vulnerable to reentry, subsequently identified as ventricular tachycardia (VT) circuit exit sites. Here, we perform an in silico investigation of the RVI metric in order to aid the acquisition and interpretation of RVI maps and optimise its future usage within the clinic. Within idealised 2D sheet models we show that the RVI produces lower values under correspondingly more arrhythmogenic conditions, with even low resolution (8 mm electrode separation) recordings still able to locate vulnerable regions. When applied to models of infarct scars, the surface RVI maps successfully identified exit sites of the reentrant circuit, even in scenarios where the scar was wholly intramural. Within highly complex infarct scar anatomies with multiple reentrant pathways, the identified exit sites were dependent upon the specific pacing location used to compute the endocardial RVI maps. However, simulated ablation of these sites successfully prevented the reentry re-initiation. We conclude that endocardial surface RVI maps are able to successfully locate regions vulnerable to reentry corresponding to critical exit sites during sustained scar-related VT. The method is robust against highly complex and intramural scar anatomies and low resolution clinical data acquisition. Optimal location of all relevant sites requires RVI maps to be computed from multiple pacing

  1. Activated partial thromboplastin time.

    PubMed

    Ignjatovic, Vera

    2013-01-01

    Activated partial thromboplastin time (APTT) is a commonly used coagulation assay that is easy to perform, is affordable, and is therefore performed in most coagulation laboratories, both clinical and research, worldwide. The APTT is based on the principle that in citrated plasma, the addition of a platelet substitute, factor XII activator, and CaCl2 allows for formation of a stable clot. The time required for the formation of a stable clot is recorded in seconds and represents the actual APTT result.

  2. Interactions of timing and prediction error learning.

    PubMed

    Kirkpatrick, Kimberly

    2014-01-01

    Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields.

  3. Dynamo theory prediction of solar activity

    NASA Technical Reports Server (NTRS)

    Schatten, Kenneth H.

    1988-01-01

    The dynamo theory technique to predict decadal time scale solar activity variations is introduced. The technique was developed following puzzling correlations involved with geomagnetic precursors of solar activity. Based upon this, a dynamo theory method was developed to predict solar activity. The method was used successfully in solar cycle 21 by Schatten, Scherrer, Svalgaard, and Wilcox, after testing with 8 prior solar cycles. Schatten and Sofia used the technique to predict an exceptionally large cycle, peaking early (in 1990) with a sunspot value near 170, likely the second largest on record. Sunspot numbers are increasing, suggesting that: (1) a large cycle is developing, and (2) that the cycle may even surpass the largest cycle (19). A Sporer Butterfly method shows that the cycle can now be expected to peak in the latter half of 1989, consistent with an amplitude comparable to the value predicted near the last solar minimum.

  4. Uncertainties in container failure time predictions

    SciTech Connect

    Williford, R.E.

    1990-01-01

    Stochastic variations in the local chemical environment of a geologic waste repository can cause corresponding variations in container corrosion rates and failure times, and thus in radionuclide release rates. This paper addresses how well the future variations in repository chemistries must be known in order to predict container failure times that are bounded by a finite time period within the repository lifetime. Preliminary results indicate that a 5000 year scatter in predicted container failure times requires that repository chemistries be known to within {plus minus}10% over the repository lifetime. These are small uncertainties compared to current estimates. 9 refs., 3 figs.

  5. Predicting application run times using historical information.

    SciTech Connect

    Foster, I.; Smith, W.; Taylor, V.

    1999-06-25

    The authors present a technique for deriving predictions for the run times of parallel applications from the run times of similar applications that have executed in the past. The novel aspect of the work is the use of search techniques to determine those application characteristics that yield the best definition of similarity for the purpose of making predictions. They use four workloads recorded from parallel computers at Argonne National Laboratory, the Cornell Theory Center, and the San Diego Supercomputer Center to evaluate the effectiveness of the approach.They show that on these workloads the techniques achieve predictions that are between 14 and 60% better than those achieved by other researchers; the approach achieves mean prediction errors that are between 41 and 65% of mean application run times.

  6. Improving predictions by cross pollination in time

    NASA Astrophysics Data System (ADS)

    Schevenhoven, Francine; Selten, Frank

    2016-04-01

    Given a set of imperfect weather models, one could ask how these models can be combined in order to improve weather predictions produced with these models. In this study we explore a technique called cross-pollination in time (CPT, Smith 2001). In the CPT approach the models exchange states during the prediction. The number of possible predictions grows quickly with time and a strategy to retain only a small number of predictions, called pruning, needs to be developed. In the learning phase a pruning strategy is proposed based on retaining those solutions that remain closest to the truth. From the learning phase probabilities are derived that determine weights to be applied to the imperfect models in the forecast phase. The CPT technique is explored using low-order dynamical systems and applied to a global atmospheric model. First results indicate that the CPT approach improves the forecast quality over the individual models.

  7. HELCATS Prediction of Planetary CME arrival times

    NASA Astrophysics Data System (ADS)

    Boakes, Peter; Moestl, Christian; Davies, Jackie; Harrison, Richard; Byrne, Jason; Barnes, David; Isavnin, Alexey; Kilpua, Emilia; Rollett, Tanja

    2015-04-01

    We present the first results of CME arrival time prediction at different planetary locations and their comparison to the in situ data within the HELCATS project. The EU FP7 HELCATS (Heliospheric Cataloguing, Analysis & Techniques Service) is a European effort to consolidate the exploitation of the maturing field of heliospheric imaging. HELCATS aims to catalogue solar wind transients, observed by the NASA STEREO Heliospheric Imager (HI) instruments, and validate different methods for the determination of their kinematic properties. This validation includes comparison with arrivals at Earth, and elsewhere in the heliosphere, as well as onsets at the Sun (http://www.helcats-fp7.eu/). A preliminary catalogue of manually identified CMEs, with over 1000 separate events, has been created from observations made by the STEREO/HI instruments covering the years 2007-2013. Initial speeds and directions of each CME have been derived through fitting the time elongation profile to the state of the art Self-Similar Expansion Fitting (SSEF) geometric technique (Davies et al., 2012). The technique assumes that, in the plane corresponding to the position angle of interest, CMEs can be modelled as circles subtending a fixed angular width to Sun-center and propagating anti-sunward in a fixed direction at a constant speed (we use an angular width of 30 degrees in our initial results). The model has advantages over previous geometric models (e.g. harmonic mean or fixed phi) as it allows one to predict whether a CME will 'hit' a specific heliospheric location, as well as to what degree (e.g. direct assault or glancing blow). We use correction formulae (Möstl and Davies, 2013) to convert CME speeds, direction and launch time to speed and arrival time at any in situ location. From the preliminary CME dataset, we derive arrival times for over 400 Earth-directed CMEs, and for over 100 Mercury-, Venus-, Mars- and Saturn-directed CMEs predicted to impact each planet. We present statistics of

  8. Time will show: real time predictions during interpersonal action perception.

    PubMed

    Manera, Valeria; Schouten, Ben; Verfaillie, Karl; Becchio, Cristina

    2013-01-01

    Predictive processes are crucial not only for interpreting the actions of individual agents, but also to predict how, in the context of a social interaction between two agents, the actions of one agent relate to the actions of a second agent. In the present study we investigated whether, in the context of a communicative interaction between two agents, observers can use the actions of one agent to predict when the action of a second agent will take place. Participants observed point-light displays of two agents (A and B) performing separate actions. In the communicative condition, the action performed by agent B responded to a communicative gesture performed by agent A. In the individual condition, agent A's communicative action was substituted with a non-communicative action. For each condition, we manipulated the temporal coupling of the actions of the two agents, by varying the onset of agent A's action. Using a simultaneous masking detection task, we demonstrated that the timing manipulation had a critical effect on the communicative condition, with the visual discrimination of agent B increasing linearly while approaching the original interaction timing. No effect of the timing manipulation was found for the individual condition. Our finding complements and extends previous evidence for interpersonal predictive coding, suggesting that the communicative gestures of one agent can serve not only to predict what the second agent will do, but also when his/her action will take place.

  9. On understanding and predicting groundwater response time.

    PubMed

    Sophocleous, Marios

    2012-01-01

    An aquifer system, when perturbed, has a tendency to evolve to a new equilibrium, a process that can take from just a few seconds to possibly millions of years. The time scale on which a system adjusts to a new equilibrium is often referred to as "response time" or "lag time." Because groundwater response time affects the physical and economic viability of various management options in a basin, natural resource managers are increasingly interested in incorporating it into policy. However, the processes of how groundwater responds to land-use change are not well understood, making it difficult to predict the timing of groundwater response to such change. The difficulty in estimating groundwater response time is further compounded because the data needed to quantify this process are not usually readily available. This article synthesizes disparate pieces of information on aquifer response times into a relatively brief but hopefully comprehensive review that the community of water professionals can use to better assess the impact of aquifer response time in future groundwater management investigations. A brief exposition on dimensional/scaling analysis is presented first, followed by an overview of aquifer response time for simplified aquifer systems. The aquifer response time is considered first from a water-quantity viewpoint and later expanded to incorporate groundwater age and water-quality aspects. Monitoring programs today, as well as water policies and regulations, should address this issue of aquifer response time so that more realistic management expectations can be reached.

  10. Managing distribution changes in time series prediction

    NASA Astrophysics Data System (ADS)

    Matias, J. M.; Gonzalez-Manteiga, W.; Taboada, J.; Ordonez, C.

    2006-07-01

    When a problem is modeled statistically, a single distribution model is usually postulated that is assumed to be valid for the entire space. Nonetheless, this practice may be somewhat unrealistic in certain application areas, in which the conditions of the process that generates the data may change; as far as we are aware, however, no techniques have been developed to tackle this problem.This article proposes a technique for modeling and predicting this change in time series with a view to improving estimates and predictions. The technique is applied, among other models, to the hypernormal distribution recently proposed. When tested on real data from a range of stock market indices the technique produces better results that when a single distribution model is assumed to be valid for the entire period of time studied.Moreover, when a global model is postulated, it is highly recommended to select the hypernormal distribution parameter in the same likelihood maximization process.

  11. Predicting road accidents: Structural time series approach

    NASA Astrophysics Data System (ADS)

    Junus, Noor Wahida Md; Ismail, Mohd Tahir

    2014-07-01

    In this paper, the model for occurrence of road accidents in Malaysia between the years of 1970 to 2010 was developed and throughout this model the number of road accidents have been predicted by using the structural time series approach. The models are developed by using stepwise method and the residual of each step has been analyzed. The accuracy of the model is analyzed by using the mean absolute percentage error (MAPE) and the best model is chosen based on the smallest Akaike information criterion (AIC) value. A structural time series approach found that local linear trend model is the best model to represent the road accidents. This model allows level and slope component to be varied over time. In addition, this approach also provides useful information on improving the conventional time series method.

  12. Predicting survival time for cold exposure

    NASA Astrophysics Data System (ADS)

    Tikuisis, Peter

    1995-06-01

    The prediction of survival time (ST) for cold exposure is speculative as reliable controlled data of deep hypothermia are unavailable. At best, guidance can be obtained from case histories of accidental exposure. This study describes the development of a mathematical model for the prediction of ST under sedentary conditions in the cold. The model is based on steady-state heat conduction in a single cylinder comprised of a core and two concentric annular shells representing the fat plus skin and the clothing plus still boundary layer, respectively. The ambient condition can be either air or water; the distinction is made by assigning different values of insulation to the still boundary layer. Metabolic heat production ( M) is comprised of resting and shivering components with the latter predicted by temperature signals from the core and skin. Where the cold exposure is too severe for M to balance heat loss, ST is largely determined by the rate of heat loss from the body. Where a balance occurs, ST is governed by the endurance time for shivering. End of survival is marked by the deep core temperature reaching a value of 30° C. The model was calibrated against survival data of cold water (0 to 20° C) immersion and then applied to cold air exposure. A sampling of ST predictions for the nude exposure of an average healthy male in relatively calm air (1 km/h wind speed) are the following: 1.8, 2.5, 4.1, 9.0, and >24 h for -30, -20, -10, 0, and 10° C, respectively. With two layers of loose clothing (average thickness of 1 mm each) in a 5 km/h wind, STs are 4.0, 5.6, 8.6, 15.4, and >24 h for -50, -40, -30, -20, and -10° C. The predicted STs must be weighted against the extrapolative nature of the model. At present, it would be prudent to use the predictions in a relative sense, that is, to compare or rank-order predicted STs for various combinations of ambient conditions and clothing protection.

  13. Globally disruptive events show predictable timing patterns

    NASA Astrophysics Data System (ADS)

    Gillman, Michael P.; Erenler, Hilary E.

    2017-01-01

    Globally disruptive events include asteroid/comet impacts, large igneous provinces and glaciations, all of which have been considered as contributors to mass extinctions. Understanding the overall relationship between the timings of the largest extinctions and their potential proximal causes remains one of science's great unsolved mysteries. Cycles of about 60 Myr in both fossil diversity and environmental data suggest external drivers such as the passage of the Solar System through the galactic plane. While cyclic phenomena are recognized statistically, a lack of coherent mechanisms and a failure to link key events has hampered wider acceptance of multi-million year periodicity and its relevance to earth science and evolution. The generation of a robust predictive model of timings, with a clear plausible primary mechanism, would signal a paradigm shift. Here, we present a model of the timings of globally disruptive events and a possible explanation of their ultimate cause. The proposed model is a symmetrical pattern of 63 Myr sequences around a central value, interpreted as the occurrence of events along, and parallel to, the galactic midplane. The symmetry is consistent with multiple dark matter disks, aligned parallel to the midplane. One implication of the precise pattern of timings and the underlying physical model is the ability to predict future events, such as a major extinction in 1-2 Myr.

  14. A real-time prediction of UTC

    NASA Astrophysics Data System (ADS)

    Thomas, Claudine; Allan, David W.

    1994-05-01

    The reference time scale for all scientific and technologic applications on the Earth, the Universal Coordinated Time (UTC), must be as stable, reliable, and accurate as possible. With this in view the BIPM and before it the BIH, have always calculated and then disseminated UTC with a delay of about 80 days. There are three fundamental reasons for doing this: (1) It takes some weeks for data, gathered from some 200 clocks spread world-wide, to be collected and for errors to be eliminated; (2) changes in clock rates can only be measured with high precision well after the fact; and (3) the measurement noise originating in time links, in particular using Loran-C, is smoothed out only when averaging over an extended period. Until mid-1992, the ultimate stability of UTC was reached at averaging times of about 100 days and corresponded to an Allan deviation sigma(sub y)(tau) of about 1,5x10(exp -14) then compared to the best primary clock in the world, the PTB CS2. For several years now, a predicted UTC has been computed by the USNO through an extrapolation of the values as published in deferred time by the BIPM. This is made available through the USNO Series 4, through the USNO Automated Data Service, and through GPS signals. Due to the instability of UTC, the poor predictability of the available clocks, and the intentional SA degradation of GPS signals, the real-time access to this extrapolated UTC has represented the true deferred-time UTC only to within several hundreds of nanoseconds.

  15. A real-time prediction of UTC

    NASA Technical Reports Server (NTRS)

    Thomas, Claudine; Allan, David W.

    1994-01-01

    The reference time scale for all scientific and technologic applications on the Earth, the Universal Coordinated Time (UTC), must be as stable, reliable, and accurate as possible. With this in view the BIPM and before it the BIH, have always calculated and then disseminated UTC with a delay of about 80 days. There are three fundamental reasons for doing this: (1) It takes some weeks for data, gathered from some 200 clocks spread world-wide, to be collected and for errors to be eliminated; (2) changes in clock rates can only be measured with high precision well after the fact; and (3) the measurement noise originating in time links, in particular using Loran-C, is smoothed out only when averaging over an extended period. Until mid-1992, the ultimate stability of UTC was reached at averaging times of about 100 days and corresponded to an Allan deviation sigma(sub y)(tau) of about 1,5x10(exp -14) then compared to the best primary clock in the world, the PTB CS2. For several years now, a predicted UTC has been computed by the USNO through an extrapolation of the values as published in deferred time by the BIPM. This is made available through the USNO Series 4, through the USNO Automated Data Service, and through GPS signals. Due to the instability of UTC, the poor predictability of the available clocks, and the intentional SA degradation of GPS signals, the real-time access to this extrapolated UTC has represented the true deferred-time UTC only to within several hundreds of nanoseconds.

  16. Time Series Prediction of Hurricane Landfall.

    DTIC Science & Technology

    1986-05-01

    8217 132 111112-2 11111111.8 MICROCOPY RESOLUTION TEST CHART NATIONAL BUR[AU OIf SIANARD lq A .5. 𔃿. SECURITY CLASSIFICATION OF THIS PAGE (When, Dta Entered...parameters to change as the storm moves to a new region of the ocean. For test cases, operational average 72 hour prediction error is at least three...comparatively accurate for forecast times of 24 hours or less. The SANBAR model (Sanders and Burpee , 1968), has been in use at NHC since 1970. It is a

  17. CRAFFT: An Activity Prediction Model based on Bayesian Networks.

    PubMed

    Nazerfard, Ehsan; Cook, Diane J

    2015-04-01

    Recent advances in the areas of pervasive computing, data mining, and machine learning offer unique opportunities to provide health monitoring and assistance for individuals facing difficulties to live independently in their homes. Several components have to work together to provide health monitoring for smart home residents including, but not limited to, activity recognition, activity discovery, activity prediction, and prompting system. Compared to the significant research done to discover and recognize activities, less attention has been given to predict the future activities that the resident is likely to perform. Activity prediction components can play a major role in design of a smart home. For instance, by taking advantage of an activity prediction module, a smart home can learn context-aware rules to prompt individuals to initiate important activities. In this paper, we propose an activity prediction model using Bayesian networks together with a novel two-step inference process to predict both the next activity features and the next activity label. We also propose an approach to predict the start time of the next activity which is based on modeling the relative start time of the predicted activity using the continuous normal distribution and outlier detection. To validate our proposed models, we used real data collected from physical smart environments.

  18. Predicting the Environmental Impact of Active Sonar

    NASA Astrophysics Data System (ADS)

    Duncan, Alec J.; McCauley, Robert D.; Maggi, Amos L.

    2004-11-01

    The effect of active sonar on marine animals, particularly mammals, has become a hot topic in recent times. The Australian Environmental Protection and Biodiversity Conservation Act 1999 obligates Defence to avoid significant environmental impacts from Navy activities including those which produce underwater sound such as active sonar. It is in the interests of all parties that these effects be modeled accurately to facilitate both the quantitative evaluation of the consequences of any proposed sonar trials, and the identification of suitable mitigation procedures. This paper discusses the received signal parameters that are of importance when predicting the effect of sonar systems on marine animals and techniques for modeling both the expected values of these parameters and their statistical fluctuations.

  19. Real-time Tsunami Inundation Prediction Using High Performance Computers

    NASA Astrophysics Data System (ADS)

    Oishi, Y.; Imamura, F.; Sugawara, D.

    2014-12-01

    Recently off-shore tsunami observation stations based on cabled ocean bottom pressure gauges are actively being deployed especially in Japan. These cabled systems are designed to provide real-time tsunami data before tsunamis reach coastlines for disaster mitigation purposes. To receive real benefits of these observations, real-time analysis techniques to make an effective use of these data are necessary. A representative study was made by Tsushima et al. (2009) that proposed a method to provide instant tsunami source prediction based on achieving tsunami waveform data. As time passes, the prediction is improved by using updated waveform data. After a tsunami source is predicted, tsunami waveforms are synthesized from pre-computed tsunami Green functions of linear long wave equations. Tsushima et al. (2014) updated the method by combining the tsunami waveform inversion with an instant inversion of coseismic crustal deformation and improved the prediction accuracy and speed in the early stages. For disaster mitigation purposes, real-time predictions of tsunami inundation are also important. In this study, we discuss the possibility of real-time tsunami inundation predictions, which require faster-than-real-time tsunami inundation simulation in addition to instant tsunami source analysis. Although the computational amount is large to solve non-linear shallow water equations for inundation predictions, it has become executable through the recent developments of high performance computing technologies. We conducted parallel computations of tsunami inundation and achieved 6.0 TFLOPS by using 19,000 CPU cores. We employed a leap-frog finite difference method with nested staggered grids of which resolution range from 405 m to 5 m. The resolution ratio of each nested domain was 1/3. Total number of grid points were 13 million, and the time step was 0.1 seconds. Tsunami sources of 2011 Tohoku-oki earthquake were tested. The inundation prediction up to 2 hours after the

  20. The neural substrate of predictive motor timing in spinocerebellar ataxia.

    PubMed

    Bares, Martin; Lungu, Ovidiu V; Liu, Tao; Waechter, Tobias; Gomez, Christopher M; Ashe, James

    2011-06-01

    The neural mechanisms involved in motor timing are subcortical, involving mainly cerebellum and basal ganglia. However, the role played by these structures in predictive motor timing is not well understood. Unlike motor timing, which is often tested using rhythm production tasks, predictive motor timing requires visuo-motor coordination in anticipation of a future event, and it is evident in behaviors such as catching a ball or shooting a moving target. We examined the role of the cerebellum and striatum in predictive motor timing in a target interception task in healthy (n = 12) individuals and in subjects (n = 9) with spinocerebellar ataxia types 6 and 8. The performance of the healthy subjects was better than that of the spinocerebellar ataxia. Successful performance in both groups was associated with increased activity in the cerebellum (right dentate nucleus, left uvula (lobule V), and lobule VI), thalamus, and in several cortical areas. The superior performance in the controls was related to activation in thalamus, putamen (lentiform nucleus) and cerebellum (right dentate nucleus and culmen-lobule IV), which were not activated either in the spinocerebellar subjects or within a subgroup of controls who performed poorly. Both the cerebellum and the basal ganglia are necessary for the predictive motor timing. The degeneration of the cerebellum associated with spinocerebellar types 6 and 8 appears to lead to quantitative rather than qualitative deficits in temporal processing. The lack of any areas with greater activity in the spinocerebellar group than in controls suggests that limited functional reorganization occurs in this condition.

  1. Daily update of motor predictions by physical activity.

    PubMed

    Gueugneau, Nicolas; Schweighofer, Nicolas; Papaxanthis, Charalambos

    2015-12-03

    Motor prediction, i.e., the ability to predict the sensory consequences of motor commands, is critical for adapted motor behavior. Like speed or force, the accuracy of motor prediction varies in a 24-hour basis. Although the prevailing view is that basic biological markers regulate this circadian modulation, behavioral factors such as physical activity, itself modulated by the alternation of night and day, can also regulate motor prediction. Here, we propose that physical activity updates motor prediction on a daily basis. We tested our hypothesis by up- and down-regulating physical activity via arm-immobilization and high-intensity training, respectively. Motor prediction was assessed by measuring the timing differences between actual and mental arm movements. Results show that although mental movement time was modulated during the day when the arm was unconstrained, it remained constant when the arm was immobilized. Additionally, increase of physical activity, via release from immobilization or intense bout of training, significantly reduced mental movement time. Finally, mental and actual times were similar in the afternoon in the unconstrained condition, indicating that predicted and actual movements match after sufficient amount of physical activity. Our study supports the view that physical activity calibrates motor predictions on a daily basis.

  2. Time Activities at the BIPM

    DTIC Science & Technology

    1994-12-01

    Time Section, and have been available, in the form of computer-readable files, in the BIPM INTERNET anonymous FTP since 5 April 1994. For yrars...TIME ACTIVITIES AT THE BIPM Claudine Thomas Bureau International des Poids et Mesures Pa,villion de Breteuil 32312 Skvres Cedex France...Abstract The generation and dissemination of International Atomic Time, TAI, and of Coordinated Universal Time, UTC, are explicitly mentioned in the list

  3. Time activities at the BIPM

    NASA Technical Reports Server (NTRS)

    Thomas, Claudine

    1995-01-01

    The generation and dissemination of International Atomic Time, TAI, and of Coordinated Universal Time, UTC, are explicitly mentioned in the list of the principal tasks of the BIPM, recalled in the Comptes Rendus of the 18th Conference Generale des Poids et Mesures, in 1987. These tasks are fulfilled by the BIPM Time Section, thanks to international cooperation with national timing centers, which maintain, under metrological conditions, the clocks used to generate TAI. Besides the current work of data collection and processing, research activities are carried out in order to adapt the computation of TAI to the most recent improvements occurring in the time and frequency domains. Studies concerning the application of general relativity and pulsar timing to time metrology are also actively pursued. This paper summarizes the work done in all these fields and outlines future projects.

  4. Shuttle program. Solar activity prediction of sunspot numbers, predicted solar radio flux

    NASA Technical Reports Server (NTRS)

    Johnson, G. G.; Newman, S. R.

    1980-01-01

    A solar activity prediction technique for monthly mean sunspot numbers over a period of approximately ten years from February 1979 to January 1989 is presented. This includes the predicted maximum epoch of solar cycle 21, approximately January 1980, and the predicted minimum epoch of solar cycle 22, approximately March 1987. Additionally, the solar radio flux 10.7 centimeter smooth values are included for the same time frame using a smooth 13 month empirical relationship. The incentive for predicting solar activity values is the requirement of solar flux data as input to upper atmosphere density models utilized in mission planning satellite orbital lifetime studies.

  5. Structure-Based Predictions of Activity Cliffs

    PubMed Central

    Husby, Jarmila; Bottegoni, Giovanni; Kufareva, Irina; Abagyan, Ruben; Cavalli, Andrea

    2015-01-01

    In drug discovery, it is generally accepted that neighboring molecules in a given descriptors' space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as ‘activity cliffs’. In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming co-crystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods. PMID:25918827

  6. Resource Selection Using Execution and Queue Wait Time Predictions

    NASA Technical Reports Server (NTRS)

    Warren, Smith; Wong, Parkson; Biegel, Bryan A. (Technical Monitor)

    2002-01-01

    Computational grids provide users with many possible places to execute their applications. We wish to help users select where to run their applications by providing predictions of the execution times of applications on space shared parallel computers and predictions of when scheduling systems for such parallel computers will start applications. Our predictions are based on instance based learning techniques and simulations of scheduling algorithms. We find that our execution time prediction techniques have an average error of 37 percent of the execution times for trace data recorded from SGI Origins at NASA Ames Research Center and that this error is 67 percent lower than the error of user estimates. We also find that the error when predicting how long applications will wait in scheduling queues is 95 percent of mean queue wait times when using our execution time predictions and this is 57 percent lower than if we use user execution time estimates.

  7. Eternal inflation predicts that time will end

    SciTech Connect

    Bousso, Raphael; Freivogel, Ben; Leichenauer, Stefan; Rosenhaus, Vladimir

    2011-01-15

    Present treatments of eternal inflation regulate infinities by imposing a geometric cutoff. We point out that some matter systems reach the cutoff in finite time. This implies a nonzero probability for a novel type of catastrophe. According to the most successful measure proposals, our galaxy is likely to encounter the cutoff within the next 5x10{sup 9} years.

  8. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

    PubMed Central

    Mansouri, Kamel; Abdelaziz, Ahmed; Rybacka, Aleksandra; Roncaglioni, Alessandra; Tropsha, Alexander; Varnek, Alexandre; Zakharov, Alexey; Worth, Andrew; Richard, Ann M.; Grulke, Christopher M.; Trisciuzzi, Daniela; Fourches, Denis; Horvath, Dragos; Benfenati, Emilio; Muratov, Eugene; Wedebye, Eva Bay; Grisoni, Francesca; Mangiatordi, Giuseppe F.; Incisivo, Giuseppina M.; Hong, Huixiao; Ng, Hui W.; Tetko, Igor V.; Balabin, Ilya; Kancherla, Jayaram; Shen, Jie; Burton, Julien; Nicklaus, Marc; Cassotti, Matteo; Nikolov, Nikolai G.; Nicolotti, Orazio; Andersson, Patrik L.; Zang, Qingda; Politi, Regina; Beger, Richard D.; Todeschini, Roberto; Huang, Ruili; Farag, Sherif; Rosenberg, Sine A.; Slavov, Svetoslav; Hu, Xin; Judson, Richard S.

    2016-01-01

    Background: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. Objectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. Methods: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure–activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. Results: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. Conclusion: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other

  9. Evaluation of Fast-Time Wake Vortex Prediction Models

    NASA Technical Reports Server (NTRS)

    Proctor, Fred H.; Hamilton, David W.

    2009-01-01

    Current fast-time wake models are reviewed and three basic types are defined. Predictions from several of the fast-time models are compared. Previous statistical evaluations of the APA-Sarpkaya and D2P fast-time models are discussed. Root Mean Square errors between fast-time model predictions and Lidar wake measurements are examined for a 24 hr period at Denver International Airport. Shortcomings in current methodology for evaluating wake errors are also discussed.

  10. PREDICTING EVAPORATION RATES AND TIMES FOR SPILLS OF CHEMICAL MIXTURES

    EPA Science Inventory


    Spreadsheet and short-cut methods have been developed for predicting evaporation rates and evaporation times for spills (and constrained baths) of chemical mixtures. Steady-state and time-varying predictions of evaporation rates can be made for six-component mixtures, includ...

  11. Real-time multi-model decadal climate predictions

    NASA Astrophysics Data System (ADS)

    Smith, Doug M.; Scaife, Adam A.; Boer, George J.; Caian, Mihaela; Doblas-Reyes, Francisco J.; Guemas, Virginie; Hawkins, Ed; Hazeleger, Wilco; Hermanson, Leon; Ho, Chun Kit; Ishii, Masayoshi; Kharin, Viatcheslav; Kimoto, Masahide; Kirtman, Ben; Lean, Judith; Matei, Daniela; Merryfield, William J.; Müller, Wolfgang A.; Pohlmann, Holger; Rosati, Anthony; Wouters, Bert; Wyser, Klaus

    2013-12-01

    We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the

  12. Prediction of energy expenditure and physical activity in preschoolers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Accurate, nonintrusive, and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) ...

  13. Physical activity behavior predicts endogenous pain modulation in older adults.

    PubMed

    Naugle, Kelly M; Ohlman, Thomas; Naugle, Keith E; Riley, Zachary A; Keith, NiCole R

    2017-03-01

    Older adults compared with younger adults are characterized by greater endogenous pain facilitation and a reduced capacity to endogenously inhibit pain, potentially placing them at a greater risk for chronic pain. Previous research suggests that higher levels of self-reported physical activity are associated with more effective pain inhibition and less pain facilitation on quantitative sensory tests in healthy adults. However, no studies have directly tested the relationship between physical activity behavior and pain modulatory function in older adults. This study examined whether objective measures of physical activity behavior cross-sectionally predicted pain inhibitory function on the conditioned pain modulation (CPM) test and pain facilitation on the temporal summation (TS) test in healthy older adults. Fifty-one older adults wore an accelerometer on the hip for 7 days and completed the CPM and TS tests. Measures of sedentary time, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) were obtained from the accelerometer. Hierarchical linear regressions were conducted to determine the relationship of TS and CPM with levels of physical activity, while controlling for demographic, psychological, and test variables. The results indicated that sedentary time and LPA significantly predicted pain inhibitory function on the CPM test, with less sedentary time and greater LPA per day associated with greater pain inhibitory capacity. Additionally, MVPA predicted pain facilitation on the TS test, with greater MVPA associated with less TS of pain. These results suggest that different types of physical activity behavior may differentially impact pain inhibitory and facilitatory processes in older adults.

  14. Solar activity affects avian timing of reproduction

    PubMed Central

    Visser, Marcel E.; Sanz, Juan José

    2009-01-01

    Avian timing of reproduction is strongly affected by ambient temperature. Here we show that there is an additional effect of sunspots on laying date, from five long-term population studies of great and blue tits (Parus major and Cyanistes caeruleus), demonstrating for the first time that solar activity not only has an effect on population numbers but that it also affects the timing of animal behaviour. This effect is statistically independent of ambient temperature. In years with few sunspots, birds initiate laying late while they are often early in years with many sunspots. The sunspot effect may be owing to a crucial difference between the method of temperature measurements by meteorological stations (in the shade) and the temperatures experienced by the birds. A better understanding of the impact of all the thermal components of weather on the phenology of ecosystems is essential when predicting their responses to climate change. PMID:19574283

  15. Method for Predicting Which Customers' Time Deposit Balances Will Increase

    NASA Astrophysics Data System (ADS)

    Ono, Toshiyuki; Yoshikawa, Hiroshi; Morita, Masahiro; Komoda, Norihisa

    This paper proposes a method of predicting which customers' account balances will increase by using data mining to effectively and efficiently promote sales. Prediction by mining all the data in a business is difficult because of much time required to collect, process, and calculate it. The selection of which features are used for prediction is a critical issue. We propose a method of selecting features to improve the accuracy of prediction within practical time limits. It consists of three parts: (1) converting collected features into financial behavior features that reflect customer actions, (2) extracting features affecting increases in account balances from these collected and financial behavior features, and (3) predicting customers whose account balances will increase based on the extracted features. We found the accuracy of prediction in an experiment with our method to be higher than with other conventional methods.

  16. 'It is Time to Prepare the Next patient' Real-Time Prediction of Procedure Duration in Laparoscopic Cholecystectomies.

    PubMed

    Guédon, Annetje C P; Paalvast, M; Meeuwsen, F C; Tax, D M J; van Dijke, A P; Wauben, L S G L; van der Elst, M; Dankelman, J; van den Dobbelsteen, J J

    2016-12-01

    Operating Room (OR) scheduling is crucial to allow efficient use of ORs. Currently, the predicted durations of surgical procedures are unreliable and the OR schedulers have to follow the progress of the procedures in order to update the daily planning accordingly. The OR schedulers often acquire the needed information through verbal communication with the OR staff, which causes undesired interruptions of the surgical process. The aim of this study was to develop a system that predicts in real-time the remaining procedure duration and to test this prediction system for reliability and usability in an OR. The prediction system was based on the activation pattern of one single piece of equipment, the electrosurgical device. The prediction system was tested during 21 laparoscopic cholecystectomies, in which the activation of the electrosurgical device was recorded and processed in real-time using pattern recognition methods. The remaining surgical procedure duration was estimated and the optimal timing to prepare the next patient for surgery was communicated to the OR staff. The mean absolute error was smaller for the prediction system (14 min) than for the OR staff (19 min). The OR staff doubted whether the prediction system could take all relevant factors into account but were positive about its potential to shorten waiting times for patients. The prediction system is a promising tool to automatically and objectively predict the remaining procedure duration, and thereby achieve optimal OR scheduling and streamline the patient flow from the nursing department to the OR.

  17. Predicting falls within the elderly community: comparison of postural sway, reaction time, the Berg balance scale and the Activities-specific Balance Confidence (ABC) scale for comparing fallers and non-fallers.

    PubMed

    Lajoie, Y; Gallagher, S P

    2004-01-01

    Simple reaction time, the Berg balance scale, the Activities-specific Balance Confidence (ABC) scale and postural sway were studied in order to determine cut-off scores as well as develop a model used in the prevention of fallers within the elderly community. One hundred and twenty-five subjects, 45 fallers and 80 non-fallers were evaluated throughout the study and results indicated that non-fallers have significantly faster reaction times, have higher scores on the Berg balance scale and the ABC scale as well as sway at slower frequencies when compared to fallers. Furthermore, all risk factors were subsequently entered into a logistic regression analysis and results showed that reaction time, the total Berg score and the total ABC score contributed significantly to the prediction of falls with 89% sensitivity and 96% specificity. A second logistic regression was carried out with the same previous variables as well as all questions of the Berg and ABC scales. Results from the logistic analysis revealed that three variables were associated with fall status with 91% sensitivity and 97% specificity. Results from the following study would seem rather valuable as an assessment tool for health care professionals in the identification and monitoring of potential fallers within nursing homes and throughout the community.

  18. Characterizing Complex Time Series from the Scaling of Prediction Error.

    NASA Astrophysics Data System (ADS)

    Hinrichs, Brant Eric

    This thesis concerns characterizing complex time series from the scaling of prediction error. We use the global modeling technique of radial basis function approximation to build models from a state-space reconstruction of a time series that otherwise appears complicated or random (i.e. aperiodic, irregular). Prediction error as a function of prediction horizon is obtained from the model using the direct method. The relationship between the underlying dynamics of the time series and the logarithmic scaling of prediction error as a function of prediction horizon is investigated. We use this relationship to characterize the dynamics of both a model chaotic system and physical data from the optic tectum of an attentive pigeon exhibiting the important phenomena of nonstationary neuronal oscillations in response to visual stimuli.

  19. Chaotic time series prediction using artificial neural networks

    SciTech Connect

    Bartlett, E.B.

    1991-12-31

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  20. Chaotic time series prediction using artificial neural networks

    SciTech Connect

    Bartlett, E.B.

    1991-01-01

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  1. Three Millennia of Seemingly Time-Predictable Earthquakes, Tell Ateret

    NASA Astrophysics Data System (ADS)

    Agnon, Amotz; Marco, Shmuel; Ellenblum, Ronnie

    2014-05-01

    Among various idealized recurrence models of large earthquakes, the "time-predictable" model has a straightforward mechanical interpretation, consistent with simple friction laws. On a time-predictable fault, the time interval between an earthquake and its predecessor is proportional to the slip during the predecessor. The alternative "slip-predictable" model states that the slip during earthquake rupture is proportional to the preceding time interval. Verifying these models requires extended records of high precision data for both timing and amount of slip. The precision of paleoearthquake data can rarely confirm or rule out predictability, and recent papers argue for either time- or slip-predictable behavior. The Ateret site, on the trace of the Dead Sea fault at the Jordan Gorge segment, offers unique precision for determining space-time patterns. Five consecutive slip events, each associated with deformed and offset sets of walls, are correlated with historical earthquakes. Two correlations are based on detailed archaeological, historical, and numismatic evidence. The other three are tentative. The offsets of three of the events are determined with high precision; the other two are not as certain. Accepting all five correlations, the fault exhibits a striking time-predictable behavior, with a long term slip rate of 3 mm/yr. However, the 30 October 1759 ~0.5 m rupture predicts a subsequent rupture along the Jordan Gorge toward the end of the last century. We speculate that earthquakres on secondary faults (the 25 November 1759 on the Rachaya branch and the 1 January 1837 on the Roum branch, both M≥7) have disrupted the 3 kyr time-predictable pattern.

  2. Model-free quantification of time-series predictability.

    PubMed

    Garland, Joshua; James, Ryan; Bradley, Elizabeth

    2014-11-01

    This paper provides insight into when, why, and how forecast strategies fail when they are applied to complicated time series. We conjecture that the inherent complexity of real-world time-series data, which results from the dimension, nonlinearity, and nonstationarity of the generating process, as well as from measurement issues such as noise, aggregation, and finite data length, is both empirically quantifiable and directly correlated with predictability. In particular, we argue that redundancy is an effective way to measure complexity and predictive structure in an experimental time series and that weighted permutation entropy is an effective way to estimate that redundancy. To validate these conjectures, we study 120 different time-series data sets. For each time series, we construct predictions using a wide variety of forecast models, then compare the accuracy of the predictions with the permutation entropy of that time series. We use the results to develop a model-free heuristic that can help practitioners recognize when a particular prediction method is not well matched to the task at hand: that is, when the time series has more predictive structure than that method can capture and exploit.

  3. Predictions of active region flaring probability using subsurface helicity measurements

    NASA Astrophysics Data System (ADS)

    Reinard, A. A.; Komm, R.; Hill, F.

    2010-12-01

    Solar flares are responsible for a number of hazardous effects on the earth such as disabling high-frequency radio communications, interfering with GPS measurements, and disrupting satellites. However, forecasting flare occurrence is currently very difficult. One possible means for predicting flare occurrence lies in helioseismology, i.e. analysis of the region below the active region for signs of an impending flare. Time series helioseismic data collected by the Global Oscillation Network Group (GONG) has been analyzed for a subset of active regions that produce large flares and a subset with very high magnetic field strength that produce no flares. A predictive parameter has been developed and analyzed using discriminant analysis as well as traditional forecasting tools such as the Heidke skill score. Preliminary results show that this parameter predicts the flaring probability of an active region 2-3 days in advance with a relatively high degree of success.

  4. Weight Suppression Predicts Time to Remission from Bulimia Nervosa

    ERIC Educational Resources Information Center

    Lowe, Michael R.; Berner, Laura A.; Swanson, Sonja A.; Clark, Vicki L.; Eddy, Kamryn T.; Franko, Debra L.; Shaw, Jena A.; Ross, Stephanie; Herzog, David B.

    2011-01-01

    Objective: To investigate whether, at study entry, (a) weight suppression (WS), the difference between highest past adult weight and current weight, prospectively predicts time to first full remission from bulimia nervosa (BN) over a follow-up period of 8 years, and (b) weight change over time mediates the relationship between WS and time to first…

  5. Improving Predictability in Embedded Real-Time Systems

    DTIC Science & Technology

    2000-12-01

    Systems CMU/SEI-2000-SR-011 Peter H. Feiler , Software Engineering Institute Bruce Lewis, U.S. Army Aviation and Missile Command Steve Vestal...SUBTITLE Improving Predictability in Embedded Real-Time Systems 5. FUNDING NUMBERS F19628-00-C-0003 6. AUTHOR(S) Peter H. Feiler , Bruce ...Carnegie Metton Software Engineering Institute Improving Predictability in Embedded Real-Time Systems Peter H. Feiler , Software Engineering

  6. Prediction of primary somatosensory neuron activity during active tactile exploration

    PubMed Central

    Campagner, Dario; Evans, Mathew Hywel; Bale, Michael Ross; Erskine, Andrew; Petersen, Rasmus Strange

    2016-01-01

    Primary sensory neurons form the interface between world and brain. Their function is well-understood during passive stimulation but, under natural behaving conditions, sense organs are under active, motor control. In an attempt to predict primary neuron firing under natural conditions of sensorimotor integration, we recorded from primary mechanosensory neurons of awake, head-fixed mice as they explored a pole with their whiskers, and simultaneously measured both whisker motion and forces with high-speed videography. Using Generalised Linear Models, we found that primary neuron responses were poorly predicted by whisker angle, but well-predicted by rotational forces acting on the whisker: both during touch and free-air whisker motion. These results are in apparent contrast to previous studies of passive stimulation, but could be reconciled by differences in the kinematics-force relationship between active and passive conditions. Thus, simple statistical models can predict rich neural activity elicited by natural, exploratory behaviour involving active movement of sense organs. DOI: http://dx.doi.org/10.7554/eLife.10696.001 PMID:26880559

  7. Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

    SciTech Connect

    Simmhan, Yogesh; Noor, Muhammad Usman

    2013-10-09

    Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.

  8. Time evolution of predictability of epidemics on networks

    NASA Astrophysics Data System (ADS)

    Holme, Petter; Takaguchi, Taro

    2015-04-01

    Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information—i.e., knowing the state of each individual with respect to the disease—the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak.

  9. Kalman Filtering USNO's GPS Observations for Improved Time Transfer Predictions

    NASA Technical Reports Server (NTRS)

    Hutsell, Steven T.

    1996-01-01

    The Global Positioning System (GPS) Master Control Station (MCS) performs the Coordinated Universal Time (UTC) time transfer mission by uploading and broadcasting predictions of the GPS-UTC offset in subframe 4 of the GS navigation message. These predictions are based on only two successive daily data points obtained from the US Naval Observatory (USNO). USNO produces these daily smoothed data points by performing a least-squares fit on roughly 38 hours worth of data from roughly 160 successive 13-minute tracks of GPS satellites. Though sufficient for helping to maintain a time transfer error specification of 28 ns (1 Sigma), the MCS's prediction algorithm does not make the best use of the available data from from USNO, and produces data that can degrade quickly over extended prediction spans. This paper investigates how, by applying Kalman filtering to the same available tracking data, the MCS could improve its estimate of GPS-UTC, and in particular, the GPS-UTC A(sub 1) term. By refining the A(sub 1) (frequency) estimate for GPS-UTC predictions, error in GPS time transfer could drop significantly. Additional, the risk of future spikes in GPS's time transfer error could similarly be minimized, by employing robust Kalman filtering for GPS-UTC predictions.

  10. Clinical time series prediction: towards a hierarchical dynamical system framework

    PubMed Central

    Liu, Zitao; Hauskrecht, Milos

    2014-01-01

    Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

  11. The Prediction of Teacher Turnover Employing Time Series Analysis.

    ERIC Educational Resources Information Center

    Costa, Crist H.

    The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…

  12. Advanced propeller noise prediction in the time domain

    NASA Technical Reports Server (NTRS)

    Farassat, F.; Dunn, M. H.; Spence, P. L.

    1992-01-01

    The time domain code ASSPIN gives acousticians a powerful technique of advanced propeller noise prediction. Except for nonlinear effects, the code uses exact solutions of the Ffowcs Williams-Hawkings equation with exact blade geometry and kinematics. By including nonaxial inflow, periodic loading noise, and adaptive time steps to accelerate computer execution, the development of this code becomes complete.

  13. Glass Fibre/Epoxy Resin Interface Life-Time Prediction.

    DTIC Science & Technology

    1983-04-01

    RD-Ai32 26 GLASS FIBRE /POXY RESIN INTERFACE LIFE-TIME PREDICTION 1/1 (U) BRISTOL UNIV (ENGLAND) H H WILLS PHYSICS LAB K H RSHBEE ET AL. APR 83...D 3005-MS GLASS FIBRE /EPOXY RESIN INTERFACE LIFE-TIME PREDICTION - Final Report by K H G Ashbee, Principal Investigator R Ho~l J P Sargent Elizabeth...REPORT h PERIOD COVERED. Glass Fibre /Epoxy Resin Interface Life-time F-inal Technical 11’ port PreictonApril 1981 - A:’ril 1983 6. PERFORMING ORG. REPORT

  14. Prospects for eruption prediction in near real-time

    USGS Publications Warehouse

    Voight, B.; Cornelius, R.R.

    1991-01-01

    THE 'materials science' method for eruption prediction1-3 arises from the application of a general law governing the failure of materials: ??-?? ??-A=0, where A and ?? are empirical constants, and ?? is an observable quantity such as ground deformation, seismicity or gas emission. This law leads to the idea of the 'inverse-rate' plot, in which the time of failure can be estimated by extrapolation of the curve of ??-1 versus time to a pre-deter-mined intercept. Here we suggest that this method can be combined with real-time seismic amplitude monitoring to provide a tool for near-real-time eruption prediction, and we demonstrate how it might have been used to predict two dome-growth episodes at Mount St Helens volcano in 1985 and 1986, and two explosive eruptions at Redoubt volcano in 1989-90.

  15. Time series prediction using artificial neural network for power stabilization

    SciTech Connect

    Puranik, G.; Philip, T.; Nail, B.

    1996-12-31

    Time series prediction has been applied to many business and scientific applications. Prominent among them are stock market prediction, weather forecasting, etc. Here, this technique has been applied to forecast plasma torch voltages to stabilize power using a backpropagation, a model of artificial neural network. The Extended-Delta-Bar-Delta algorithm is used to improve the convergence rate of the network and also to avoid local minima. Results from off-line data was quite promising to use in on-line.

  16. On the Prediction of α-Stable Time Series

    NASA Astrophysics Data System (ADS)

    Mohammadi, Mohammad; Mohammadpour, Adel

    2016-07-01

    This paper addresses the point prediction of α-stable time series. Our key idea is to define a new Hilbert space that contains α-stable processes. Then, we apply the advantage of Hilbert space theory for finding the best linear prediction. We show how to use the presented predictor practically for α-stable linear processes. The implementation of the presented method is easier than the implementation of the minimum dispersion method. We reveal the appropriateness of the presented method through an empirical study on predicting the natural logarithms of the volumes of SP500 market.

  17. Impaired predictive motor timing in patients with cerebellar disorders.

    PubMed

    Bares, Martin; Lungu, Ovidiu; Liu, Tao; Waechter, Tobias; Gomez, Christopher M; Ashe, James

    2007-06-01

    The ability to precisely time events is essential for both perception and action. There is evidence that the cerebellum is important for the neural representation of time in a variety of behaviors including time perception, the tapping of specific time intervals, and eye-blink conditioning. It has been difficult to assess the contribution of the cerebellum to timing during more dynamic motor behavior because the component movements themselves may be abnormal or any motor deficit may be due to an inability to combine the component movements into a complete action rather than timing per se. Here we investigated the performance of subjects with cerebellar disease in predictive motor timing using a task that involved mediated interception of a moving target, and we tested the effect of movement type (acceleration, deceleration, constant), speed (slow, medium, fast), and angle (0 degrees , 15 degrees and 30 degrees) on performance. The subjects with cerebellar damage were significantly worse at interception than healthy controls even when we controlled for basic motor impairments such as response time. Our data suggest that subjects with damage to the cerebellum have a fundamental problem with predictive motor timing and indicate that the cerebellum plays an essential role in integrating incoming visual information with motor output when making predictions about upcoming actions. The findings demonstrate that the cerebellum may have properties that would facilitate the processing or storage of internal models of motor behavior.

  18. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

    PubMed Central

    Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli

    2016-01-01

    In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices. PMID:27293423

  19. Symplectic geometry spectrum regression for prediction of noisy time series

    NASA Astrophysics Data System (ADS)

    Xie, Hong-Bo; Dokos, Socrates; Sivakumar, Bellie; Mengersen, Kerrie

    2016-05-01

    We present the symplectic geometry spectrum regression (SGSR) technique as well as a regularized method based on SGSR for prediction of nonlinear time series. The main tool of analysis is the symplectic geometry spectrum analysis, which decomposes a time series into the sum of a small number of independent and interpretable components. The key to successful regularization is to damp higher order symplectic geometry spectrum components. The effectiveness of SGSR and its superiority over local approximation using ordinary least squares are demonstrated through prediction of two noisy synthetic chaotic time series (Lorenz and Rössler series), and then tested for prediction of three real-world data sets (Mississippi River flow data and electromyographic and mechanomyographic signal recorded from human body).

  20. Prediction of Long-Memory Time Series: A Tutorial Review

    NASA Astrophysics Data System (ADS)

    Bhansali, R. J.; Kokoszka, P. S.

    Two different approaches, called Type-I and Type-II, to linear least-squares prediction of a long-memory time series are distinguished. In the former, no new theory is required and a long-memory time series is treated on par with a standard short-memory time series and its multistep predictions are obtained by using the existing modelling approaches to prediction of such time series. The latter, by contrast, seeks to model the long-memory stochastic characteristics of the observed time series by a fractional process such that its dth fractional difference, 0 < d < 0.5, follows a standard short-memory process. The various approaches to constructing long-memory stochastic models are reviewed, and the associated question of parameter estimation for these models is discussed. Having fitted a long-memory stochastic model to a time series, linear multi-step forecasts of its future values are constructed from the model itself. The question of how to evaluate the multistep prediction constants is considered and three different methods proposed for doing so are outlined; it is further noted that, under appropriate regularity conditions, these methods apply also to the class of linear long memory processes with infinite variance. In addition, a brief review of the class of non-linear chaotic maps implying long-memory is given.

  1. Solar activity cycle - History and predictions

    SciTech Connect

    Withbroe, G.L. )

    1989-12-01

    The solar output of short-wavelength radiation, solar wind, and energetic particles depends strongly on the solar cycle. These energy outputs from the sun control conditions in the interplanetary medium and in the terrestrial magnetosphere and upper atmosphere. Consequently, there is substantial interest in the behavior of the solar cycle and its effects. This review briefly discusses historical data on the solar cycle and methods for predicting its further behavior, particularly for the current cycle, which shows signs that it will have moderate to exceptionally high levels of activity. During the next few years, the solar flux of short-wavelength radiation and particles will be more intense than normal, and spacecraft in low earth orbit will reenter earlier than usual. 46 refs.

  2. Prediction limits of mobile phone activity modelling

    PubMed Central

    Grauwin, Sebastian; Kallus, Zsófia; Gódor, István; Sobolevsky, Stanislav; Ratti, Carlo

    2017-01-01

    Thanks to their widespread usage, mobile devices have become one of the main sensors of human behaviour and digital traces left behind can be used as a proxy to study urban environments. Exploring the nature of the spatio-temporal patterns of mobile phone activity could thus be a crucial step towards understanding the full spectrum of human activities. Using 10 months of mobile phone records from Greater London resolved in both space and time, we investigate the regularity of human telecommunication activity on urban scales. We evaluate several options for decomposing activity timelines into typical and residual patterns, accounting for the strong periodic and seasonal components. We carry out our analysis on various spatial scales, showing that regularity increases as we look at aggregated activity in larger spatial units with more activity in them. We examine the statistical properties of the residuals and show that it can be explained by noise and specific outliers. Also, we look at sources of deviations from the general trends, which we find to be explainable based on knowledge of the city structure and places of attractions. We show examples how some of the outliers can be related to external factors such as specific social events. PMID:28386443

  3. Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions

    PubMed Central

    Zheng, Zhihai; Hu, Zeng-Zhen; L’Heureux, Michelle

    2016-01-01

    The most predictable components of the El Niño-Southern Oscillation (ENSO) evolution in real-time multi-model predictions are identified by applying an empirical orthogonal function analysis of the model data that maximizes the signal-to-noise ratio (MSN EOF). The normalized Niño3.4 index is analyzed for nine 3-month overlapping seasons. In this sense, the first most predictable component (MSN EOF1) is the decaying phase of ENSO during the Northern Hemisphere spring, followed by persistence through autumn and winter. The second most predictable component of ENSO evolution, with lower prediction skill and smaller explained variance than MSN EOF1, corresponds to the growth during spring and then persistence in summer and autumn. This result suggests that decay phase of ENSO is more predictable than the growth phase. Also, the most predictable components and the forecast skills in dynamical and statistical models are similar overall, with some differences arising during spring season initial conditions. Finally, the reconstructed predictions, with only the first two MSN components, show higher skill than the model raw predictions. Therefore this method can be used as a diagnostic for model comparison and development, and it can provide a new perspective for the most predictable components of ENSO. PMID:27775016

  4. Time dependent patient no-show predictive modelling development.

    PubMed

    Huang, Yu-Li; Hanauer, David A

    2016-05-09

    Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

  5. Solar-terrestrial predictions proceedings. Volume 4: Prediction of terrestrial effects of solar activity

    NASA Technical Reports Server (NTRS)

    Donnelly, R. E. (Editor)

    1980-01-01

    Papers about prediction of ionospheric and radio propagation conditions based primarily on empirical or statistical relations is discussed. Predictions of sporadic E, spread F, and scintillations generally involve statistical or empirical predictions. The correlation between solar-activity and terrestrial seismic activity and the possible relation between solar activity and biological effects is discussed.

  6. Predicting mining activity with parallel genetic algorithms

    USGS Publications Warehouse

    Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.; Beyer, H.G.; O'Reilly, U.M.; Banzhaf, Arnold D.; Blum, W.; Bonabeau, C.; Cantu-Paz, E.W.; ,; ,

    2005-01-01

    We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.

  7. The Leisure-Time Activity of Citizens

    ERIC Educational Resources Information Center

    Sedova, N. N.

    2011-01-01

    Survey data show that Russians relegate free time and leisure activity to secondary status compared to work, and free time faces the threat of becoming devalued and losing its importance as a life value. At the same time, in the structure of Russians' leisure activities there is an ongoing tendency for leisure to become simpler, for active types…

  8. Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

    NASA Technical Reports Server (NTRS)

    Lee, Hanbong

    2016-01-01

    Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

  9. Broadband Trailing Edge Noise Predictions in the Time Domain. Revised

    NASA Technical Reports Server (NTRS)

    Casper, Jay; Farassat, Fereidoun

    2003-01-01

    A recently developed analytic result in acoustics, "Formulation 1B," is used to compute broadband trailing edge noise from an unsteady surface pressure distribution on a thin airfoil in the time domain. This formulation is a new solution of the Ffowcs Willliams-Hawkings equation with the loading source term, and has been shown in previous research to provide time domain predictions of broadband noise that are in excellent agreement with experimental results. Furthermore, this formulation lends itself readily to rotating reference frames and statistical analysis of broadband trailing edge noise. Formulation 1B is used to calculate the far field noise radiated from the trailing edge of a NACA 0012 airfoil in low Mach number flows, by using both analytical and experimental data on the airfoil surface. The acoustic predictions are compared with analytical results and experimental measurements that are available in the literature. Good agreement between predictions and measurements is obtained.

  10. 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.

  11. Navy Global Predictions for the Dynamo Time Period

    NASA Astrophysics Data System (ADS)

    Reynolds, C. A.; Ridout, J. A.; Flatau, M. K.; Chen, J.; Richman, J. G.; Jensen, T. G.; Shriver, J. F.

    2014-12-01

    The performance of 30-day simulations of the Navy Global Environmental Model (NAVGEM) is evaluated under several metrics. The time period of interest is the DYNAMO (Dynamics of Madden Julian Oscillation) field experiment period, starting late October 2011. The NAVGEM experiments are run at an effective 37-km resolution with several different SST configurations. The in the first set of experiments, the initial SST analysis, provided by the NCODA (Navy Coupled Ocean Data Assimilation) system, is either held fixed to the initial value (fixed SST) or updated every 6 hours. These forecasts are compared with forecasts in which the SST is updated with 3-h analyses from the Hybrid Coordinate Ocean Model (HYCOM), and forecasts in which NAVGEM is interactively coupled to HYCOM. Experiments are also performed with different physical parameterization options. The extended integrations are verified using observed OLR, TRMM precipitation estimates, and global analyses. The use of fixed SSTs is clearly sub-optimal. Biases in monthly mean fields are far more pronounced in the simulations where the SST is held fixed as compared to those in simulations where updated SST analyses are used. Biases in the monthly mean fields are further reduced when NAVGEM is coupled to HYCOM. Differences in SST can "migrate" to substantial changes in the time-mean land-surface temperatures, illustrating the substantial impact of SSTs over the full domain. Concerning the simulation of the MJO, some improvement is noted when the system is fully coupled, although the simulations still exhibit deficiencies such as eastward propagation that is too slow, and difficulty propagating over the maritime continent. Simulations that are started every 5 days indicate that the NAVGEM uncoupled system has difficulty predicting MJO initiation, but simulations started when the MJO is active in the Indian Ocean are able to capture eastward propagation characteristics. The coupled NAVGEM-HYCOM system shows ability to

  12. Long-term time series prediction using OP-ELM.

    PubMed

    Grigorievskiy, Alexander; Miche, Yoan; Ventelä, Anne-Mari; Séverin, Eric; Lendasse, Amaury

    2014-03-01

    In this paper, an Optimally Pruned Extreme Learning Machine (OP-ELM) is applied to the problem of long-term time series prediction. Three known strategies for the long-term time series prediction i.e. Recursive, Direct and DirRec are considered in combination with OP-ELM and compared with a baseline linear least squares model and Least-Squares Support Vector Machines (LS-SVM). Among these three strategies DirRec is the most time consuming and its usage with nonlinear models like LS-SVM, where several hyperparameters need to be adjusted, leads to relatively heavy computations. It is shown that OP-ELM, being also a nonlinear model, allows reasonable computational time for the DirRec strategy. In all our experiments, except one, OP-ELM with DirRec strategy outperforms the linear model with any strategy. In contrast to the proposed algorithm, LS-SVM behaves unstably without variable selection. It is also shown that there is no superior strategy for OP-ELM: any of three can be the best. In addition, the prediction accuracy of an ensemble of OP-ELM is studied and it is shown that averaging predictions of the ensemble can improve the accuracy (Mean Square Error) dramatically.

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

    PubMed

    Limongi, Roberto; Silva, Angélica M

    2016-11-01

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

  14. How young children spend their time: television and other activities.

    PubMed

    Huston, A C; Wright, J C; Marquis, J; Green, S B

    1999-07-01

    Time-use diaries were collected over a 3-year period for 2 cohorts of 2- and 4-year-old children. TV viewing declined with age. Time spent in reading and educational activities increased with age on weekdays but declined on weekends. Time-use patterns were sex-stereotyped, and sex differences increased with age. As individuals' time in educational activities, social interaction, and video games increased, their time watching entertainment TV declined, but time spent playing covaried positively with entertainment TV. Educational TV viewing was not related to time spent in non-TV activities. Maternal education and home environment quality predicted frequent viewing of educational TV programs and infrequent viewing of entertainment TV. The results do not support a simple displacement hypothesis; the relations of TV viewing to other activities depend on the program content, the nature of the competing activity, and the environmental context.

  15. Predictive influence in the accelerated failure time model.

    PubMed

    Bedrick, Edward J; Exuzides, Alex; Johnson, Wesley O; Thurmond, Mark C

    2002-09-01

    We develop case deletion diagnostics for prediction of future observations in the accelerated failure time model. We view prediction to be an important inferential goal in a survival analysis and thus it is important to identify whether particular observations may be influencing the quality of predictions. We use the Kullback-Leibler divergence as a measure of the discrepancy between the estimated probability distributions for the full and the case-deleted samples. In particular, we focus on the effect of case deletion on estimated survival curves but where we regard the survival curve estimate as a vehicle for prediction. We also develop a diagnostic for assessing the effect of case deletion on inferences for the median time to failure. The estimated median can be used with both predictive and estimative purposes in mind. We also discuss the relationship between our suggested measures and the corresponding Cook distance measure, which was designed with the goal of assessing estimative influence. Several applications of the proposed diagnostics are presented.

  16. The use of content and timing to predict turn transitions

    PubMed Central

    Garrod, Simon; Pickering, Martin J.

    2015-01-01

    For addressees to respond in a timely fashion, they cannot simply process the speaker's utterance as it occurs and wait till it finishes. Instead, they predict both when the speaker will conclude and what linguistic forms will be used. While doing this, they must also prepare their own response. To explain this, we draw on the account proposed by Pickering and Garrod (2013a), in which addressees covertly imitate the speaker's utterance and use this to determine the intention that underlies their upcoming utterance. They use this intention to predict when and how the utterance will end, and also to drive their own production mechanisms for preparing their response. Following Arnal and Giraud (2012), we distinguish between mechanisms that predict timing and content. In particular, we propose that the timing mechanism relies on entrainment of low-frequency oscillations between speech envelope and brain. This constrains the context that feeds into the determination of the speaker's intention and hence the timing and form of the upcoming utterance. This approach typically leads to well-timed contributions, but also provides a mechanism for resolving conflicts, for example when there is unintended speaker overlap. PMID:26124728

  17. The use of content and timing to predict turn transitions.

    PubMed

    Garrod, Simon; Pickering, Martin J

    2015-01-01

    For addressees to respond in a timely fashion, they cannot simply process the speaker's utterance as it occurs and wait till it finishes. Instead, they predict both when the speaker will conclude and what linguistic forms will be used. While doing this, they must also prepare their own response. To explain this, we draw on the account proposed by Pickering and Garrod (2013a), in which addressees covertly imitate the speaker's utterance and use this to determine the intention that underlies their upcoming utterance. They use this intention to predict when and how the utterance will end, and also to drive their own production mechanisms for preparing their response. Following Arnal and Giraud (2012), we distinguish between mechanisms that predict timing and content. In particular, we propose that the timing mechanism relies on entrainment of low-frequency oscillations between speech envelope and brain. This constrains the context that feeds into the determination of the speaker's intention and hence the timing and form of the upcoming utterance. This approach typically leads to well-timed contributions, but also provides a mechanism for resolving conflicts, for example when there is unintended speaker overlap.

  18. Individual differences in time perspective predict autonoetic experience.

    PubMed

    Arnold, Kathleen M; McDermott, Kathleen B; Szpunar, Karl K

    2011-09-01

    Tulving (1985) posited that the capacity to remember is one facet of a more general capacity-autonoetic (self-knowing) consciousness. Autonoetic consciousness was proposed to underlie the ability for "mental time travel" both into the past (remembering) and into the future to envision potential future episodes (episodic future thinking). The current study examines whether individual differences can predict autonoetic experience. Specifically, the Zimbardo Time Perspective Inventory (ZTPI, Zimbardo & Boyd, 1999) was administered to 133 undergraduate students, who also rated phenomenological experiences accompanying autobiographical remembering and episodic future thinking. Scores on two of the five subscales of the ZTPI (Future and Present-Hedonistic) predicted the degree to which people reported feelings of mentally traveling backward (or forward) in time and the degree to which they reported re- or pre-experiencing the event, but not ten other rated properties less related to autonoetic consciousness.

  19. Future missions studies: Combining Schatten's solar activity prediction model with a chaotic prediction model

    NASA Technical Reports Server (NTRS)

    Ashrafi, S.

    1991-01-01

    K. Schatten (1991) recently developed a method for combining his prediction model with our chaotic model. The philosophy behind this combined model and his method of combination is explained. Because the Schatten solar prediction model (KS) uses a dynamo to mimic solar dynamics, accurate prediction is limited to long-term solar behavior (10 to 20 years). The Chaotic prediction model (SA) uses the recently developed techniques of nonlinear dynamics to predict solar activity. It can be used to predict activity only up to the horizon. In theory, the chaotic prediction should be several orders of magnitude better than statistical predictions up to that horizon; beyond the horizon, chaotic predictions would theoretically be just as good as statistical predictions. Therefore, chaos theory puts a fundamental limit on predictability.

  20. Impaired Spatio-Temporal Predictive Motor Timing Associated with Spinocerebellar Ataxia Type 6

    PubMed Central

    Onuki, Yoshiyuki; Abdelgabar, Abdel R.; Owens, Cullen B.; Picard, Samuel; Willems, Jessica; Boele, Henk-Jan; Gazzola, Valeria; Van der Werf, Ysbrand D.; De Zeeuw, Chris I.

    2016-01-01

    Many daily life activities demand precise integration of spatial and temporal information of sensory inputs followed by appropriate motor actions. This type of integration is carried out in part by the cerebellum, which has been postulated to play a central role in learning and timing of movements. Cerebellar damage due to atrophy or lesions may compromise forward-model processing, in which both spatial and temporal cues are used to achieve prediction for future motor states. In the present study we sought to further investigate the cerebellar contribution to predictive and reactive motor timing, as well as to learning of sequential order and temporal intervals in these tasks. We tested patients with spinocerebellar ataxia type 6 (SCA6) and healthy controls for two related motor tasks; one requiring spatio-temporal prediction of dynamic visual stimuli and another one requiring reactive timing only. We found that healthy controls established spatio-temporal prediction in their responses with high temporal precision, which was absent in the cerebellar patients. SCA6 patients showed lower predictive motor timing, coinciding with a reduced number of correct responses during the ‘anticipatory’ period on the task. Moreover, on the task utilizing reactive motor timing functions, control participants showed both sequence order and temporal interval learning, whereas patients only showed sequence order learning. These results suggest that SCA6 affects predictive motor timing and temporal interval learning. Our results support and highlight cerebellar contribution to timing and argue for cerebellar engagement during spatio-temporal prediction of upcoming events. PMID:27571363

  1. Model predictive control of P-time event graphs

    NASA Astrophysics Data System (ADS)

    Hamri, H.; Kara, R.; Amari, S.

    2016-12-01

    This paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.

  2. Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

    NASA Technical Reports Server (NTRS)

    He, Yuning

    2015-01-01

    Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

  3. Optimal model-free prediction from multivariate time series

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  4. Optimal model-free prediction from multivariate time series.

    PubMed

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

    2015-05-01

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

  5. Program Predicts Time Courses of Human/Computer Interactions

    NASA Technical Reports Server (NTRS)

    Vera, Alonso; Howes, Andrew

    2005-01-01

    CPM X is a computer program that predicts sequences of, and amounts of time taken by, routine actions performed by a skilled person performing a task. Unlike programs that simulate the interaction of the person with the task environment, CPM X predicts the time course of events as consequences of encoded constraints on human behavior. The constraints determine which cognitive and environmental processes can occur simultaneously and which have sequential dependencies. The input to CPM X comprises (1) a description of a task and strategy in a hierarchical description language and (2) a description of architectural constraints in the form of rules governing interactions of fundamental cognitive, perceptual, and motor operations. The output of CPM X is a Program Evaluation Review Technique (PERT) chart that presents a schedule of predicted cognitive, motor, and perceptual operators interacting with a task environment. The CPM X program allows direct, a priori prediction of skilled user performance on complex human-machine systems, providing a way to assess critical interfaces before they are deployed in mission contexts.

  6. Financial Time Series Prediction Using Spiking Neural Networks

    PubMed Central

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. PMID:25170618

  7. Predicting physical time series using dynamic ridge polynomial neural networks.

    PubMed

    Al-Jumeily, Dhiya; Ghazali, Rozaida; Hussain, Abir

    2014-01-01

    Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.

  8. Financial time series prediction using spiking neural networks.

    PubMed

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  9. Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

    PubMed Central

    Al-Jumeily, Dhiya; Ghazali, Rozaida; Hussain, Abir

    2014-01-01

    Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques. PMID:25157950

  10. A Stochastic Semiclassical Time Front Prediction for Ocean Acoustics

    NASA Astrophysics Data System (ADS)

    Hegewisch, Katherine; Tomsovic, Steven

    2008-05-01

    Low frequency sound propagates in the ocean within a wave guide formed by the confining effects of temperature, salinity and pressure on the sound speed. This wave guide enables long range propagation upwards of 3000 km. Within the wave guide, sound scatters due to range dependent sound speed oscillations from internal waves and gives rise to wave chaos, where most of the classical rays are chaotic. This chaos poses challenges to ray predictions of the range and frequency dependence of properties of the 'time fronts', the acoustic arrivals in depth and time. Though semiclassical theory works well for strongly chaotic systemss, finding the necessary eigenrays for long ranges is unrealistic here. Instead, we utilize semiclassical and perturbation theories ONLY for short ranges and extend these results to long ranges using a previously introduced diffusive theory. We verify the diffusive assumptions and demonstrate the analytic results for these theories for short ranges before arriving at a stochastic prediction.

  11. Earthquake prediction in Japan and natural time analysis of seismicity

    NASA Astrophysics Data System (ADS)

    Uyeda, S.; Varotsos, P.

    2011-12-01

    M9 super-giant earthquake with huge tsunami devastated East Japan on 11 March, causing more than 20,000 casualties and serious damage of Fukushima nuclear plant. This earthquake was predicted neither short-term nor long-term. Seismologists were shocked because it was not even considered possible to happen at the East Japan subduction zone. However, it was not the only un-predicted earthquake. In fact, throughout several decades of the National Earthquake Prediction Project, not even a single earthquake was predicted. In reality, practically no effective research has been conducted for the most important short-term prediction. This happened because the Japanese National Project was devoted for construction of elaborate seismic networks, which was not the best way for short-term prediction. After the Kobe disaster, in order to parry the mounting criticism on their no success history, they defiantly changed their policy to "stop aiming at short-term prediction because it is impossible and concentrate resources on fundamental research", that meant to obtain "more funding for no prediction research". The public were and are not informed about this change. Obviously earthquake prediction would be possible only when reliable precursory phenomena are caught and we have insisted this would be done most likely through non-seismic means such as geochemical/hydrological and electromagnetic monitoring. Admittedly, the lack of convincing precursors for the M9 super-giant earthquake has adverse effect for us, although its epicenter was far out off shore of the range of operating monitoring systems. In this presentation, we show a new possibility of finding remarkable precursory signals, ironically, from ordinary seismological catalogs. In the frame of the new time domain termed natural time, an order parameter of seismicity, κ1, has been introduced. This is the variance of natural time kai weighted by normalised energy release at χ. In the case that Seismic Electric Signals

  12. DPIV prediction of flow induced platelet activation-comparison to numerical predictions.

    PubMed

    Raz, Sagi; Einav, Shmuel; Alemu, Yared; Bluestein, Danny

    2007-04-01

    Flow induced platelet activation (PA) can lead to platelet aggregation, deposition onto the blood vessel wall, and thrombus formation. PA was thoroughly studied under unidirectional flow conditions. However, in regions of complex flow, where the platelet is exposed to varying levels of shear stress for varying durations, the relationship between flow and PA is not well understood. Numerical models were developed for studying flow induced PA resulting from stress histories along Lagrangian trajectories in the flow field. However, experimental validation techniques such as Digital Particle Image Velocimetry (DPIV) were not extended to include such models. In this study, a general experimental tool for PA analysis by means of continuous DPIV was utilized and compared to numerical simulation in a model of coronary stenosis. A scaled up (5:1) 84% eccentric and axisymetric coronary stenosis model was used for analysis of shear stress and exposure time along particle trajectories. Flow induced PA was measured using the PA State (PAS) assay. An algorithm for computing the PA level in pertinent trajectories was developed as a tool for extracting information from DPIV measurements for predicting the flow induced thrombogenic potential. CFD, DPIV and PAS assay results agreed well in predicting the level of PA. In addition, the same trend predicted by the DPIV was measured in vitro using the Platelet Activity State (PAS) assay, namely, that the symmetric stenosis activated the platelets more as compared to the eccentric stenosis.

  13. Predicting the degradability of waste activated sludge.

    PubMed

    Jones, Richard; Parker, Wayne; Zhu, Henry; Houweling, Dwight; Murthy, Sudhir

    2009-08-01

    The objective of this study was to identify methods for estimating anaerobic digestibility of waste activated sludge (WAS). The WAS streams were generated in three sequencing batch reactors (SBRs) treating municipal wastewater. The wastewater and WAS properties were initially determined through simulation of SBR operation with BioWin (EnviroSim Associates Ltd., Flamborough, Ontario, Canada). Samples of WAS from the SBRs were subsequently characterized through respirometry and batch anaerobic digestion. Respirometry was an effective tool for characterizing the active fraction of WAS and could be a suitable technique for determining sludge composition for input to anaerobic models. Anaerobic digestion of the WAS revealed decreasing methane production and lower chemical oxygen demand removals as the SRT of the sludge increased. BioWin was capable of accurately describing the digestion of the WAS samples for typical digester SRTs. For extended digestion times (i.e., greater than 30 days), some degradation of the endogenous decay products was assumed to achieve accurate simulations for all sludge SRTs.

  14. Real-time Adaptive Control Using Neural Generalized Predictive Control

    NASA Technical Reports Server (NTRS)

    Haley, Pam; Soloway, Don; Gold, Brian

    1999-01-01

    The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

  15. Predictability and prediction of tropical cyclones on daily to interannual time scales

    NASA Astrophysics Data System (ADS)

    Belanger, James Ian

    The spatial and temporal complexity of tropical cyclones (TCs) raises a number of scientific questions regarding their genesis, movement, intensification, and variability. In this dissertation, the principal goal is to determine the current state of predictability for each of these processes using global numerical prediction systems. The predictability findings are then used in conjunction with several new statistical calibration techniques to develop a proof-of-concept, operational forecast system for North Atlantic TCs on daily to intraseasonal time scales. To quantify the current extent of tropical cyclone predictability, we assess probabilistic forecasts from the most advanced global numerical weather prediction system to date, the ECMWF Variable Resolution Ensemble Prediction System (VarEPS; Hamill et al. 2008, Hagedorn et al. 2012). Using a new false alarm clustering technique to maximize the utility of the VarEPS, the ensemble system is shown to provide well-calibrated probabilistic forecasts for TC genesis through a lead-time of one week and pregenesis track forecasts with similar skill compared to the VarEPS's postgenesis track forecasts. These findings provide evidence that skillful real-time TC genesis predictions may be made in the North Indian Ocean—a region that even today has limited forecast warning windows for TCs relative to other ocean basins. To quantify the predictability of TCs on intraseasonal time scales, forecasts from the ECMWF Monthly Forecast System (ECMFS) are examined for the North Atlantic Ocean. From this assessment, dynamically based forecasts from the ECMFS provide forecast skill exceeding climatology out to weeks three and four for portions of the southern Gulf of Mexico, western Caribbean and the Main Development Region. Forecast skill in these regions is traced to the model's ability to capture correctly the variability in deep-layer vertical wind shear as well as the relative frequency of easterly waves moving through these

  16. Chromospheric extents predicted by time-dependent acoustic wave models

    NASA Astrophysics Data System (ADS)

    Cuntz, Manfred

    1990-01-01

    Theoretical models for chromospheric structures of late-type giant stars are computed, including the time-dependent propagation of acoustic waves. Models with short-period monochromatic shock waves as well as a spectrum of acoustic waves are discussed, and the method is applied to the stars Arcturus, Aldebaran, and Betelgeuse. Chromospheric extent, defined as the monotonic decrease with height of the time-averaged electron densities, are found to be 1.12, 1.13, and 1.22 stellar radii for the three stars, respectively; this corresponds to a time-averaged electron density of 10 to the 7th/cu cm. Predictions of the extended chromospheric obtained using a simple scaling law agree well with those obtained by the time-dependent wave models; thus, the chromospheres of all stars for which the scaling law is valid consist of the same number of pressure scale heights.

  17. Chromospheric extents predicted by time-dependent acoustic wave models

    SciTech Connect

    Cuntz, M. Heidelberg Universitaet )

    1990-01-01

    Theoretical models for chromospheric structures of late-type giant stars are computed, including the time-dependent propagation of acoustic waves. Models with short-period monochromatic shock waves as well as a spectrum of acoustic waves are discussed, and the method is applied to the stars Arcturus, Aldebaran, and Betelgeuse. Chromospheric extent, defined as the monotonic decrease with height of the time-averaged electron densities, are found to be 1.12, 1.13, and 1.22 stellar radii for the three stars, respectively; this corresponds to a time-averaged electron density of 10 to the 7th/cu cm. Predictions of the extended chromospheric obtained using a simple scaling law agree well with those obtained by the time-dependent wave models; thus, the chromospheres of all stars for which the scaling law is valid consist of the same number of pressure scale heights. 74 refs.

  18. Chaos Time Series Prediction Based on Membrane Optimization Algorithms

    PubMed Central

    Li, Meng; Yi, Liangzhong; Pei, Zheng; Gao, Zhisheng

    2015-01-01

    This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ, m) and least squares support vector machine (LS-SVM) (γ, σ) by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). PMID:25874249

  19. Time-of-day-dependent adaptation of the HPA axis to predictable social defeat stress.

    PubMed

    Koch, C E; Bartlang, M S; Kiehn, J T; Lucke, L; Naujokat, N; Helfrich-Förster, C; Reber, S O; Oster, H

    2016-12-01

    In modern societies, the risk of developing a whole array of affective and somatic disorders is associated with the prevalence of frequent psychosocial stress. Therefore, a better understanding of adaptive stress responses and their underlying molecular mechanisms is of high clinical interest. In response to an acute stressor, each organism can either show passive freezing or active fight-or-flight behaviour, with activation of sympathetic nervous system and the hypothalamus-pituitary-adrenal (HPA) axis providing the necessary energy for the latter by releasing catecholamines and glucocorticoids (GC). Recent data suggest that stress responses are also regulated by the endogenous circadian clock. In consequence, the timing of stress may critically affect adaptive responses to and/or pathological effects of repetitive stressor exposure. In this article, we characterize the impact of predictable social defeat stress during daytime versus nighttime on bodyweight development and HPA axis activity in mice. While 19 days of social daytime stress led to a transient reduction in bodyweight without altering HPA axis activity at the predicted time of stressor exposure, more detrimental effects were seen in anticipation of nighttime stress. Repeated nighttime stressor exposure led to alterations in food metabolization and reduced HPA axis activity with lower circulating adrenocorticotropic hormone (ACTH) and GC concentrations at the time of predicted stressor exposure. Our data reveal a circadian gating of stress adaptation to predictable social defeat stress at the level of the HPA axis with impact on metabolic homeostasis underpinning the importance of timing for the body's adaptability to repetitive stress.

  20. A real-time prediction system for solar weather based on magnetic nonpotentiality (I)

    NASA Astrophysics Data System (ADS)

    Yang, Xiao; Lin, GangHua; Deng, YuanYong

    2016-07-01

    The Sun is the source of space weather. The characteristics and evolution of the solar active-region magnetic field closely relate to violent solar eruptions such as flares and coronal mass ejections. The Solar Magnetic Field Telescope in Huairou Solar Observing Station has accumulated numerous vector magnetogram data of solar photospheric active regions (AR) covering nearly 30 years. Utilizing these precious historical data to establish statistical prediction models for solar eruptive events, not only can provide a reference for the timely adjustment of observation mode to specific active regions, but also can offer valuable reference to the monitoring and forecasting departments of solar and space weather. In this part of work, we focus on the Yes/No and occurrence time predictions for AR-related solar flares, and the predictions independently rely on the vector magnetic-filed observation of the solar surface.

  1. Long Term Mean Local Time of the Ascending Node Prediction

    NASA Technical Reports Server (NTRS)

    McKinley, David P.

    2007-01-01

    Significant error has been observed in the long term prediction of the Mean Local Time of the Ascending Node on the Aqua spacecraft. This error of approximately 90 seconds over a two year prediction is a complication in planning and timing of maneuvers for all members of the Earth Observing System Afternoon Constellation, which use Aqua's MLTAN as the reference for their inclination maneuvers. It was determined that the source of the prediction error was the lack of a solid Earth tide model in the operational force models. The Love Model of the solid Earth tide potential was used to derive analytic corrections to the inclination and right ascension of the ascending node of Aqua's Sun-synchronous orbit. Additionally, it was determined that the resonance between the Sun and orbit plane of the Sun-synchronous orbit is the primary driver of this error. The analytic corrections have been added to the operational force models for the Aqua spacecraft reducing the two-year 90-second error to less than 7 seconds.

  2. Connectionist Architectures for Time Series Prediction of Dynamical Systems

    NASA Astrophysics Data System (ADS)

    Weigend, Andreas Sebastian

    We investigate the effectiveness of connectionist networks for predicting the future continuation of temporal sequences. The problem of overfitting, particularly serious for short records of noisy data, is addressed by the method of weight-elimination: a term penalizing network complexity is added to the usual cost function in back-propagation. We describe the dynamics of the procedure and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We analyze three time series. On the benchmark sunspot series, the networks outperform traditional statistical approaches. We show that the network performance does not deteriorate when there are more input units than needed. In the second example, the notoriously noisy foreign exchange rates series, we pick one weekday and one currency (DM vs. US). Given exchange rate information up to and including a Monday, the task is to predict the rate for the following Tuesday. Weight-elimination manages to extract a significant part of the dynamics and makes the solution interpretable. In the third example, the networks predict the resource utilization of a chaotic computational ecosystem for hundreds of steps forward in time.

  3. NASA AVOSS Fast-Time Wake Prediction Models: User's Guide

    NASA Technical Reports Server (NTRS)

    Ahmad, Nash'at N.; VanValkenburg, Randal L.; Pruis, Matthew

    2014-01-01

    The National Aeronautics and Space Administration (NASA) is developing and testing fast-time wake transport and decay models to safely enhance the capacity of the National Airspace System (NAS). The fast-time wake models are empirical algorithms used for real-time predictions of wake transport and decay based on aircraft parameters and ambient weather conditions. The aircraft dependent parameters include the initial vortex descent velocity and the vortex pair separation distance. The atmospheric initial conditions include vertical profiles of temperature or potential temperature, eddy dissipation rate, and crosswind. The current distribution includes the latest versions of the APA (3.4) and the TDP (2.1) models. This User's Guide provides detailed information on the model inputs, file formats, and the model output. An example of a model run and a brief description of the Memphis 1995 Wake Vortex Dataset is also provided.

  4. Urban air pollution by odor sources: Short time prediction

    NASA Astrophysics Data System (ADS)

    Pettarin, Nicola; Campolo, Marina; Soldati, Alfredo

    2015-12-01

    A numerical approach is proposed to predict the short time dispersion of odors in the urban environment. The model is based on (i) a three dimensional computational domain describing the urban topography at fine spatial scale (1 m) and on (ii) highly time resolved (1 min frequency) meteorological data used as inflow conditions. The time dependent, three dimensional wind velocity field is reconstructed in the Eulerian framework using a fast response finite volume solver of Navier-Stokes equations. Odor dispersion is calculated using a Lagrangian approach. An application of the model to the historic city of Verona (Italy) is presented. Results confirm that this type of odor dispersion simulations can be used (i) to assess the impact of odor emissions in urban areas and (ii) to evaluate the potential mitigation produced by odor abatement systems.

  5. Two-parameter Failure Model Improves Time-independent and Time-dependent Failure Predictions

    SciTech Connect

    Huddleston, R L

    2004-01-27

    A new analytical model for predicting failure under a generalized, triaxial stress state was developed by the author and initially reported in 1984. The model was validated for predicting failure under elevated-temperature creep-rupture conditions. Biaxial data for three alloy steels, Types 304 and 316 stainless steels and Inconel 600, demonstrated two to three orders of magnitude reduction in the scatter of predicted versus observed creep-rupture times as compared to the classical failure models of Mises, Tresca, and Rankine. In 1990, the new model was incorporated into American Society of Mechanical Engineers (ASME) Code Case N47-29 for design of components operating under creep-rupture conditions. The current report provides additional validation of the model for predicting failure under time-independent conditions and also outlines a methodology for predicting failure under cyclic, time-dependent, creep-fatigue conditions. The later extension of the methodology may have the potential to improve failure predictions there as well. These results are relevant to most design applications, but they have special relevance to high-performance design applications such as components for high-pressure equipment, nuclear reactors, and jet engines.

  6. Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts.

    PubMed

    Balasubramanian, A; Shamsuddin, R; Prabhakaran, B; Sawant, A

    2017-03-07

    Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5-91.4%); (ii) the predictive modeling yields lowest accuracies (50-60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96-0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable

  7. Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts

    NASA Astrophysics Data System (ADS)

    Balasubramanian, A.; Shamsuddin, R.; Prabhakaran, B.; Sawant, A.

    2017-03-01

    Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5–91.4%) (ii) the predictive modeling yields lowest accuracies (50–60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96–0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable

  8. Loneliness Predicts Reduced Physical Activity: Cross-Sectional & Longitudinal Analyses

    PubMed Central

    Hawkley, Louise C.; Thisted, Ronald A.; Cacioppo, John T.

    2009-01-01

    Objective To determine cross-sectional and prospective associations between loneliness and physical activity, and to evaluate the roles of social control and emotion regulation as mediators of these associations. Design A population-based sample of 229 White, Black, and Hispanic men and women, age 50 to 68 years at study onset, were tested annually for each of 3 years. Main Outcome Measures Physical activity probability, and changes in physical activity probability over a 3-year period. Results Replicating and extending prior cross-sectional research, loneliness was associated with a significantly reduced odds of physical activity (OR = 0.65 per SD of loneliness) net of sociodemographic variables (age, gender, ethnicity, education, income), psychosocial variables (depressive symptoms, perceived stress, hostility, social support), and self-rated health. This association was mediated by hedonic emotion regulation, but not by social control as indexed by measures of social network size, marital status, contact with close ties, group membership, or religious group affiliation. Longitudinal analyses revealed that loneliness predicted diminished odds of physical activity in the next two years (OR = 0.61), and greater likelihood of transitioning from physical activity to inactivity (OR = 1.58). Conclusion Loneliness among middle and older age adults is an independent risk factor for physical inactivity and increases the likelihood that physical activity will be discontinued over time. PMID:19450042

  9. Predicting the decay time of solid body electric guitar tones.

    PubMed

    Paté, Arthur; Le Carrou, Jean-Loïc; Fabre, Benoît

    2014-05-01

    Although it can be transformed by various electronic devices, the sound of the solid body electric guitar originates from, and is strongly linked with, the string vibration. The coupling of the string with the guitar alters its vibration and can lead to decay time inhomogeneities. This paper implements and justifies a framework for the study of decay times of electric guitar tones. Two damping mechanisms are theoretically and experimentally identified: the string intrinsic damping and the damping due to mechanical coupling with the neck of the guitar. The electromagnetic pickup is shown to not provide any additional damping to the string. The pickup is also shown to be far more sensitive to the out-of-plane polarization of the string. Finally, an accurate prediction of the decay time of electric guitar tones is made possible, whose only requirements are the knowledge of the isolated string dampings and the out-of-plane conductance at the neck of the guitar. This prediction can be of great help for instrument makers and manufacturers.

  10. Computer Aided Prediction of Biological Activity Spectra: Study of Correlation between Predicted and Observed Activities for Coumarin-4-Acetic Acids

    PubMed Central

    Basanagouda, M.; Jadhav, V. B.; Kulkarni, M. V.; Rao, R. Nagendra

    2011-01-01

    Coumarin-4-acetic acids have been synthesized from various phenols and citric acid under Pechmann cyclisation conditions. All the compounds have been evaluated for antiinflammatory and analgesic activity in acute models. Compounds have also been evaluated for their ulcerogenic potential. Using the computer program, prediction of activity spectra for substances, prediction results and their Pharma Expert software, we have found a correlation between the observed and predicted antiinflammatory activity. PMID:22131629

  11. Incorporating Retention Time to Refine Models Predicting Thermal Regimes of Stream Networks Across New England

    EPA Science Inventory

    Thermal regimes are a critical factor in models predicting effects of watershed management activities on fish habitat suitability. We have assembled a database of lotic temperature time series across New England (> 7000 station-year combinations) from state and Federal data s...

  12. Around-the-World Atomic Clocks: Predicted Relativistic Time Gains.

    PubMed

    Hafele, J C; Keating, R E

    1972-07-14

    During October 1971, four cesium beam atomic clocks were flown on regularly scheduled commercial jet flights around the world twice, once eastward and once westward, to test Einstein's theory of relativity with macroscopic clocks. From the actual flight paths of each trip, the theory predicts that the flying clocks, compared with reference clocks at the U.S. Naval Observatory, should have lost 40 +/- 23 nanoseconds during the eastward trip, and should have gained 275 +/- 21 nanoseconds during the westward trip. The observed time differences are presented in the report that follows this one.

  13. Discriminability of Prediction Artifacts in a Time Delayed Virtual Environment

    NASA Technical Reports Server (NTRS)

    Adelstein, Bernard D.; Jung, Jae Y.; Ellis, Stephen R.

    2001-01-01

    Overall latency remains an impediment to perceived image stability and consequently to human performance in virtual environment (VE) systems. Predictive compensators have been proposed as a means to mitigate these shortcomings, but they introduce rendering errors because of induced motion overshoot and heightened noise. Discriminability of these compensator artifacts was investigated by a protocol in which head tracked image stability for 35 ms baseline VE system latency was compared against artificially added (16.7 to 100 ms) latency compensated by a previously studied Kalman Filter (K-F) predictor. A control study in which uncompensated 16.7 to 100 ms latencies were compared against the baseline was also performed. Results from 10 subjects in the main study and 8 in the control group indicate that predictive compensation artifacts are less discernible than the disruptions of uncompensated time delay for the shorter but not the longer added latencies. We propose that noise magnification and overshoot are contributory cues to the presence of predictive compensation.

  14. Predict or classify: The deceptive role of time-locking in brain signal classification

    PubMed Central

    Rusconi, Marco; Valleriani, Angelo

    2016-01-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal. PMID:27320688

  15. Predict or classify: The deceptive role of time-locking in brain signal classification

    NASA Astrophysics Data System (ADS)

    Rusconi, Marco; Valleriani, Angelo

    2016-06-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

  16. Predict or classify: The deceptive role of time-locking in brain signal classification.

    PubMed

    Rusconi, Marco; Valleriani, Angelo

    2016-06-20

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

  17. Data assimialation for real-time prediction and reanalysis

    NASA Astrophysics Data System (ADS)

    Shprits, Y.; Kellerman, A. C.; Podladchikova, T.; Kondrashov, D. A.; Ghil, M.

    2015-12-01

    We discuss the how data assimilation can be used for the analysis of individual satellite anomalies, development of long-term evolution reconstruction that can be used for the specification models, and use of data assimilation to improve the now-casting and focusing of the radiation belts. We also discuss advanced data assimilation methods such as parameter estimation and smoothing.The 3D data assimilative VERB allows us to blend together data from GOES, RBSP A and RBSP B. Real-time prediction framework operating on our web site based on GOES, RBSP A, B and ACE data and 3D VERB is presented and discussed. In this paper we present a number of application of the data assimilation with the VERB 3D code. 1) Model with data assimilation allows to propagate data to different pitch angles, energies, and L-shells and blends them together with the physics based VERB code in an optimal way. We illustrate how we use this capability for the analysis of the previous events and for obtaining a global and statistical view of the system. 2) The model predictions strongly depend on initial conditions that are set up for the model. Therefore the model is as good as the initial conditions that it uses. To produce the best possible initial condition data from different sources ( GOES, RBSP A, B, our empirical model predictions based on ACE) are all blended together in an optimal way by means of data assimilation as described above. The resulting initial condition does not have gaps. That allows us to make a more accurate predictions.

  18. Planning for subacute care: predicting demand using acute activity data.

    PubMed

    Green, Janette P; McNamee, Jennifer P; Kobel, Conrad; Seraji, Md Habibur R; Lawrence, Suanne J

    2016-04-07

    Objective The aim of the present study was to develop a robust model that uses the concept of 'rehabilitation-sensitive' Diagnosis Related Groups (DRGs) in predicting demand for rehabilitation and geriatric evaluation and management (GEM) care following acute in-patient episodes provided in Australian hospitals.Methods The model was developed using statistical analyses of national datasets, informed by a panel of expert clinicians and jurisdictional advice. Logistic regression analysis was undertaken using acute in-patient data, published national hospital statistics and data from the Australasian Rehabilitation Outcomes Centre.Results The predictive model comprises tables of probabilities that patients will require rehabilitation or GEM care after an acute episode, with columns defined by age group and rows defined by grouped Australian Refined (AR)-DRGs.Conclusions The existing concept of rehabilitation-sensitive DRGs was revised and extended. When applied to national data, the model provided a conservative estimate of 83% of the activity actually provided. An example demonstrates the application of the model for service planning.What is known about the topic? Health service planning is core business for jurisdictions and local areas. With populations ageing and an acknowledgement of the underservicing of subacute care, it is timely to find improved methods of estimating demand for this type of care. Traditionally, age-sex standardised utilisation rates for individual DRGs have been applied to Australian Bureau of Statistics (ABS) population projections to predict the future need for subacute services. Improved predictions became possible when some AR-DRGs were designated 'rehabilitation-sensitive'. This improved methodology has been used in several Australian jurisdictions.What does this paper add? This paper presents a new tool, or model, to predict demand for rehabilitation and GEM services based on in-patient acute activity. In this model, the methodology

  19. Time-dependent Predictive Values of Prognostic Biomarkers with Failure Time Outcome.

    PubMed

    Zheng, Yingye; Cai, Tianxi; Pepe, Margaret S; Levy, Wayne C

    2008-01-01

    In a prospective cohort study, information on clinical parameters, tests and molecular markers is often collected. Such information is useful to predict patient prognosis and to select patients for targeted therapy. We propose a new graphical approach, the positive predictive value (PPV) curve, to quantify the predictive accuracy of prognostic markers measured on a continuous scale with censored failure time outcome. The proposed method highlights the need to consider both predictive values and the marker distribution in the population when evaluating a marker, and it provides a common scale for comparing different markers. We consider both semiparametric and nonparametric based estimating procedures. In addition, we provide asymptotic distribution theory and resampling based procedures for making statistical inference. We illustrate our approach with numerical studies and datasets from the Seattle Heart Failure Study.

  20. Time-predictable recurrence model for large earthquakes

    SciTech Connect

    Shimazaki, K.; Nakata, T.

    1980-04-01

    We present historical and geomorphological evidence of a regularity in earthquake recurrence at three different sites of plate convergence around the Japan arcs. The regularity shows that the larger an earthquake is, the longer is the following quiet period. In other words, the time interval between two successive large earthquakes is approximately proportional to the amount of coseismic displacement of the preceding earthquake and not of the following earthquake. The regularity enables us, in principle, to predict the approximate occurrence time of earthquakes. The data set includes 1) a historical document describing repeated measurements of water depth at Murotsu near the focal region of Nankaido earthquakes, 2) precise levelling and /sup 14/C dating of Holocene uplifted terraces in the southern boso peninsula facing the Sagami trough, and 3) similar geomorphological data on exposed Holocene coral reefs in Kikai Island along the Ryukyu arc.

  1. Satellite attitude prediction by multiple time scales method

    NASA Technical Reports Server (NTRS)

    Tao, Y. C.; Ramnath, R.

    1975-01-01

    An investigation is made of the problem of predicting the attitude of satellites under the influence of external disturbing torques. The attitude dynamics are first expressed in a perturbation formulation which is then solved by the multiple scales approach. The independent variable, time, is extended into new scales, fast, slow, etc., and the integration is carried out separately in the new variables. The theory is applied to two different satellite configurations, rigid body and dual spin, each of which may have an asymmetric mass distribution. The disturbing torques considered are gravity gradient and geomagnetic. Finally, as multiple time scales approach separates slow and fast behaviors of satellite attitude motion, this property is used for the design of an attitude control device. A nutation damping control loop, using the geomagnetic torque for an earth pointing dual spin satellite, is designed in terms of the slow equation.

  2. A New Time Domain Formulation for Broadband Noise Predictions

    NASA Technical Reports Server (NTRS)

    Casper, J.; Farassat, F.

    2002-01-01

    A new analytic result in acoustics called "Formulation 1B," proposed by Farassat, is used to compute the loading noise from an unsteady surface pressure distribution on a thin airfoil in the time domain. This formulation is a new solution of the Ffowcs Williams-Hawkings equation with the loading source term. The formulation contains a far field surface integral that depends on the time derivative and the surface gradient of the pressure on the airfoil, as well as a contour integral on the boundary of the airfoil surface. As a first test case, the new formulation is used to compute the noise radiated from a flat plate, moving through a sinusoidal gust of constant frequency. The unsteady surface pressure for this test case is analytically specified from a result based on linear airfoil theory. This test case is used to examine the velocity scaling properties of Formulation 1B and to demonstrate its equivalence to Formulation 1A of Farassat. The new acoustic formulation, again with an analytic surface pressure, is then used to predict broadband noise radiated from an airfoil immersed in homogeneous, isotropic turbulence. The results are compared with experimental data previously reported by Paterson and Amiet. Good agreement between predictions and measurements is obtained. Finally, an alternative form of Formulation 1B is described for statistical analysis of broadband noise.

  3. A New Time Domain Formulation for Broadband Noise Predictions

    NASA Technical Reports Server (NTRS)

    Casper, Jay H.; Farassat, Fereidoun

    2002-01-01

    A new analytic result in acoustics called "Formulation 1B," proposed by Farassat, is used to compute the loading noise from an unsteady surface pressure distribution on a thin airfoil in the time domain. This formulation is a new solution of the Ffowcs Williams-Hawkings equation with the loading source term. The formulation contains a far field surface integral that depends on the time derivative and the surface gradient of the pressure on the airfoil, as well as a contour integral on the boundary of the airfoil surface. As a first test case, the new formulation is used to compute the noise radiated from a flat plate, moving through a sinusoidal gust of constant frequency. The unsteady surface pressure for this test case is analytically specied from a result based on linear airfoil theory. This test case is used to examine the velocity scaling properties of Formulation 1B and to demonstrate its equivalence to Formulation 1A of Farassat. The new acoustic formulation, again with an analytic surface pressure, is then used to predict broadband noise radiated from an airfoil immersed in homogeneous, isotropic turbulence. The results are compared with experimental data previously reported by Paterson and Amiet. Good agreement between predictions and measurements is obtained. Finally, an alternative form of Formulation 1B is described for statistical analysis of broadband noise.

  4. The Built Environment Predicts Observed Physical Activity

    PubMed Central

    Kelly, Cheryl; Wilson, Jeffrey S.; Schootman, Mario; Clennin, Morgan; Baker, Elizabeth A.; Miller, Douglas K.

    2014-01-01

    Background: In order to improve our understanding of the relationship between the built environment and physical activity, it is important to identify associations between specific geographic characteristics and physical activity behaviors. Purpose: Examine relationships between observed physical activity behavior and measures of the built environment collected on 291 street segments in Indianapolis and St. Louis. Methods: Street segments were selected using a stratified geographic sampling design to ensure representation of neighborhoods with different land use and socioeconomic characteristics. Characteristics of the built environment on-street segments were audited using two methods: in-person field audits and audits based on interpretation of Google Street View imagery with each method blinded to results from the other. Segments were dichotomized as having a particular characteristic (e.g., sidewalk present or not) based on the two auditing methods separately. Counts of individuals engaged in different forms of physical activity on each segment were assessed using direct observation. Non-parametric statistics were used to compare counts of physically active individuals on each segment with built environment characteristic. Results: Counts of individuals engaged in physical activity were significantly higher on segments with mixed land use or all non-residential land use, and on segments with pedestrian infrastructure (e.g., crosswalks and sidewalks) and public transit. Conclusion: Several micro-level built environment characteristics were associated with physical activity. These data provide support for theories that suggest changing the built environment and related policies may encourage more physical activity. PMID:24904916

  5. Using timing of ice retreat to predict timing of fall freeze-up in the Arctic

    NASA Astrophysics Data System (ADS)

    Stroeve, Julienne C.; Crawford, Alex D.; Stammerjohn, Sharon

    2016-06-01

    Reliable forecasts of the timing of sea ice advance are needed in order to reduce risks associated with operating in the Arctic as well as planning of human and environmental emergencies. This study investigates the use of a simple statistical model relating the timing of ice retreat to the timing of ice advance, taking advantage of the inherent predictive power supplied by the seasonal ice-albedo feedback and ocean heat uptake. Results show that using the last retreat date to predict the first advance date is applicable in some regions, such as Baffin Bay and the Laptev and East Siberian seas, where a predictive skill is found even after accounting for the long-term trend in both variables. Elsewhere, in the Arctic, there is some predictive skills depending on the year (e.g., Kara and Beaufort seas), but none in regions such as the Barents and Bering seas or the Sea of Okhotsk. While there is some suggestion that the relationship is strengthening over time, this may reflect that higher correlations are expected during periods when the underlying trend is strong.

  6. Gut microbiota may predict host divergence time during Glires evolution.

    PubMed

    Li, Huan; Qu, Jiapeng; Li, Tongtong; Yao, Minjie; Li, Jiaying; Li, Xiangzhen

    2017-01-29

    The gut microbial communities of animals play key roles in host evolution. However, the possible relationship between gut microbiota and host divergence time remains unknown. Here, we investigated the gut microbiota of eight Glires species (four lagomorpha species and four rodent species) distributed throughout the Qinghai-Tibet plateau and Inner Mongolia grassland. Lagomorphs and rodents had distinct gut microbial compositions. Three out of four lagomorpha species were dominated by Firmicutes, while rodents were dominated by Bacteroidetes in general. The alpha diversity values (Shannon diversity and evenness) exhibited significant differences between any two species within lagomorphs, whereas there were no significant differences among rodents. The structure of the gut microbiota between lagomorphs and rodents showed significant differences. In addition, we calculated host phylogeny and divergence times, and used a phylogenetic approach to reconstruct how the animal gut microbiota has diverged from their ancestral species. Some core bacterial genera (e.g. Prevotella and Clostridium) shared by more than nine-tenths of all the Glires individuals associated with plant polysaccharide degradation showed marked changes within lagomorphs. Differences in Glires gut microbiota (based on weighted UniFrac and Bray-Curtis dissimilarity metrics) were positively correlated with host divergence time. Our results thus suggest the gut microbial composition is associated with host phylogeny, and further suggest that dissimilarity of animal gut microbiota may predict host divergence time.

  7. Amino acid composition predicts prion activity.

    PubMed

    Afsar Minhas, Fayyaz Ul Amir; Ross, Eric D; Ben-Hur, Asa

    2017-04-10

    Many prion-forming proteins contain glutamine/asparagine (Q/N) rich domains, and there are conflicting opinions as to the role of primary sequence in their conversion to the prion form: is this phenomenon driven primarily by amino acid composition, or, as a recent computational analysis suggested, dependent on the presence of short sequence elements with high amyloid-forming potential. The argument for the importance of short sequence elements hinged on the relatively-high accuracy obtained using a method that utilizes a collection of length-six sequence elements with known amyloid-forming potential. We weigh in on this question and demonstrate that when those sequence elements are permuted, even higher accuracy is obtained; we also propose a novel multiple-instance machine learning method that uses sequence composition alone, and achieves better accuracy than all existing prion prediction approaches. While we expect there to be elements of primary sequence that affect the process, our experiments suggest that sequence composition alone is sufficient for predicting protein sequences that are likely to form prions. A web-server for the proposed method is available at http://faculty.pieas.edu.pk/fayyaz/prank.html, and the code for reproducing our experiments is available at http://doi.org/10.5281/zenodo.167136.

  8. Timing Correlations in Proteins Predict Functional Modules and Dynamic Allostery.

    PubMed

    Lin, Milo M

    2016-04-20

    How protein structure encodes functionality is not fully understood. For example, long-range intraprotein communication can occur without measurable conformational change and is often not captured by existing structural correlation functions. It is shown here that important functional information is encoded in the timing of protein motions, rather than motion itself. I introduce the conditional activity function to quantify such timing correlations among the degrees of freedom within proteins. For three proteins, the conditional activities between side-chain dihedral angles were computed using the output of microseconds-long atomistic simulations. The new approach demonstrates that a sparse fraction of side-chain pairs are dynamically correlated over long distances (spanning protein lengths up to 7 nm), in sharp contrast to structural correlations, which are short-ranged (<1 nm). Regions of high self- and inter-side-chain dynamical correlations are found, corresponding to experimentally determined functional modules and allosteric connections, respectively.

  9. Predicting Prostate Cancer Progression at Time of Diagnosis

    DTIC Science & Technology

    2013-07-01

    Active surveillance incorporates serial PSA measurements , physical examinations, and repeat prostate biopsies to monitor for either the presence of occult...reflecting events throughout the prostate gland and suitable for repeat measurements over time. PCA3 and the TMPRSS2:ERG fusion are 2 prostate cancer... measurements of TMPRSS2:ERG transcript levels associate with cancer volume and grade at prosta- tectomy, and upgrading from biopsy histologic assess- ments (31

  10. Ceramide structure predicts tumor ganglioside immunosuppressive activity.

    PubMed Central

    Ladisch, S; Li, R; Olson, E

    1994-01-01

    Molecular determinants of biological activity of gangliosides are generally believed to be carbohydrate in nature. However, our studies of immunomodulation by highly purified naturally occurring tumor gangliosides provide another perspective: while the immunosuppressive activity of gangliosides requires the intact molecule (both carbohydrate and ceramide moieties), ceramide structure strikingly influences ganglioside immunosuppressive activity. Molecular species of human neuroblastoma GD2 ganglioside in which the ceramide contains a shorter fatty acyl chain (C16:0, C18:0) were 6- to 10-fold more active than those with a longer fatty acyl chain (C22:0/C24:1, C24:0). These findings were confirmed in studies of ceramide species of human leukemia sialosylparagloboside and murine lymphoma GalNAcGM1b. Gangliosides that contain shorter-chain fatty acids (and are most immunosuppressive) are known to be preferentially shed by tumor cells. Therefore, the results suggest that the tumor cell is optimized to protect itself from host immune destruction by selective shedding of highly active ceramide species of gangliosides. Images PMID:8127917

  11. Real-time prediction of cell division timing in developing zebrafish embryo

    PubMed Central

    Kozawa, Satoshi; Akanuma, Takashi; Sato, Tetsuo; Sato, Yasuomi D.; Ikeda, Kazushi; Sato, Thomas N.

    2016-01-01

    Combination of live-imaging and live-manipulation of developing embryos in vivo provides a useful tool to study developmental processes. Identification and selection of target cells for an in vivo live-manipulation are generally performed by experience- and knowledge-based decision-making of the observer. Computer-assisted live-prediction method would be an additional approach to facilitate the identification and selection of the appropriate target cells. Herein we report such a method using developing zebrafish embryos. We choose V2 neural progenitor cells in developing zebrafish embryo as their successive shape changes can be visualized in real-time in vivo. We developed a relatively simple mathematical method of describing cellular geometry of V2 cells to predict cell division-timing based on their successively changing shapes in vivo. Using quantitatively measured 4D live-imaging data, features of V2 cell-shape at each time point prior to division were extracted and a statistical model capturing the successive changes of the V2 cell-shape was developed. By applying sequential Bayesian inference method to the model, we successfully predicted division-timing of randomly selected individual V2 cells while the cell behavior was being live-imaged. This system could assist pre-selecting target cells desirable for real-time manipulation–thus, presenting a new opportunity for in vivo experimental systems. PMID:27597656

  12. Real-time prediction of cell division timing in developing zebrafish embryo.

    PubMed

    Kozawa, Satoshi; Akanuma, Takashi; Sato, Tetsuo; Sato, Yasuomi D; Ikeda, Kazushi; Sato, Thomas N

    2016-09-06

    Combination of live-imaging and live-manipulation of developing embryos in vivo provides a useful tool to study developmental processes. Identification and selection of target cells for an in vivo live-manipulation are generally performed by experience- and knowledge-based decision-making of the observer. Computer-assisted live-prediction method would be an additional approach to facilitate the identification and selection of the appropriate target cells. Herein we report such a method using developing zebrafish embryos. We choose V2 neural progenitor cells in developing zebrafish embryo as their successive shape changes can be visualized in real-time in vivo. We developed a relatively simple mathematical method of describing cellular geometry of V2 cells to predict cell division-timing based on their successively changing shapes in vivo. Using quantitatively measured 4D live-imaging data, features of V2 cell-shape at each time point prior to division were extracted and a statistical model capturing the successive changes of the V2 cell-shape was developed. By applying sequential Bayesian inference method to the model, we successfully predicted division-timing of randomly selected individual V2 cells while the cell behavior was being live-imaged. This system could assist pre-selecting target cells desirable for real-time manipulation-thus, presenting a new opportunity for in vivo experimental systems.

  13. Real-time prediction of cell division timing in developing zebrafish embryo

    NASA Astrophysics Data System (ADS)

    Kozawa, Satoshi; Akanuma, Takashi; Sato, Tetsuo; Sato, Yasuomi D.; Ikeda, Kazushi; Sato, Thomas N.

    2016-09-01

    Combination of live-imaging and live-manipulation of developing embryos in vivo provides a useful tool to study developmental processes. Identification and selection of target cells for an in vivo live-manipulation are generally performed by experience- and knowledge-based decision-making of the observer. Computer-assisted live-prediction method would be an additional approach to facilitate the identification and selection of the appropriate target cells. Herein we report such a method using developing zebrafish embryos. We choose V2 neural progenitor cells in developing zebrafish embryo as their successive shape changes can be visualized in real-time in vivo. We developed a relatively simple mathematical method of describing cellular geometry of V2 cells to predict cell division-timing based on their successively changing shapes in vivo. Using quantitatively measured 4D live-imaging data, features of V2 cell-shape at each time point prior to division were extracted and a statistical model capturing the successive changes of the V2 cell-shape was developed. By applying sequential Bayesian inference method to the model, we successfully predicted division-timing of randomly selected individual V2 cells while the cell behavior was being live-imaged. This system could assist pre-selecting target cells desirable for real-time manipulation–thus, presenting a new opportunity for in vivo experimental systems.

  14. Prediction of Active-Region CME Productivity from Magnetograms

    NASA Technical Reports Server (NTRS)

    Falconer, D. A.; Moore, R. L.; Gary, G. A.

    2004-01-01

    We report results of an expanded evaluation of whole-active-region magnetic measures as predictors of active-region coronal mass ejection (CME) productivity. Previously, in a sample of 17 vector magnetograms of 12 bipolar active regions observed by the Marshall Space Flight Center (MSFC) vector magnetograph, from each magnetogram we extracted a measure of the size of the active region (the active region s total magnetic flux a) and four measures of the nonpotentiality of the active region: the strong-shear length L(sub SS), the strong-gradient length L(sub SG), the net vertical electric current I(sub N), and the net-current magnetic twist parameter alpha (sub IN). This sample size allowed us to show that each of the four nonpotentiality measures was statistically significantly correlated with active-region CME productivity in time windows of a few days centered on the day of the magnetogram. We have now added a fifth measure of active-region nonpotentiality (the best-constant-alpha magnetic twist parameter (alpha sub BC)), and have expanded the sample to 36 MSFC vector magnetograms of 31 bipolar active regions. This larger sample allows us to demonstrate statistically significant correlations of each of the five nonpotentiality measures with future CME productivity, in time windows of a few days starting from the day of the magnetogram. The two magnetic twist parameters (alpha (sub 1N) and alpha (sub BC)) are normalized measures of an active region s nonpotentially in that they do not depend directly on the size of the active region, while the other three nonpotentiality measures (L(sub SS), L(sub SG), and I(sub N)) are non-normalized measures in that they do depend directly on active-region size. We find (1) Each of the five nonpotentiality measures is statistically significantly correlated (correlation confidence level greater than 95%) with future CME productivity and has a CME prediction success rate of approximately 80%. (2) None of the nonpotentiality

  15. A simple approach for predicting time-optimal slew capability

    NASA Astrophysics Data System (ADS)

    King, Jeffery T.; Karpenko, Mark

    2016-03-01

    The productivity of space-based imaging satellite sensors to collect images is directly related to the agility of the spacecraft. Increasing the satellite agility, without changing the attitude control hardware, can be accomplished by using optimal control to design shortest-time maneuvers. The performance improvement that can be obtained using optimal control is tied to the specific configuration of the satellite, e.g. mass properties and reaction wheel array geometry. Therefore, it is generally difficult to predict performance without an extensive simulation study. This paper presents a simple idea for estimating the agility enhancement that can be obtained using optimal control without the need to solve any optimal control problems. The approach is based on the concept of the agility envelope, which expresses the capability of a spacecraft in terms of a three-dimensional agility volume. Validation of this new approach is conducted using both simulation and on-orbit data.

  16. Predicting Rocket or Jet Noise in Real Time

    NASA Technical Reports Server (NTRS)

    Frendi, Kader

    2007-01-01

    A semi-empirical theoretical model and a C++ computer program that implements the model have been developed for use in predicting the noise generated by a rocket or jet engine. The computer program, entitled the Realtime Rocket and Jet Engine Noise Analysis and Prediction Software, is one of two main subsystems of the Acoustic Prediction/Measurement Tool, which comprises software, acoustic instrumentation, and electronic hardware combined to afford integrated capabilities for real-time prediction and measurement of noise emitted by rocket and jet engines. [The other main subsystem, consisting largely of acoustic instrumentation and electronic hardware, is described in Wireless Acoustic Measurement System, which appears elsewhere in this section.] The theoretical model was derived from the fundamental laws of fluid mechanics, as first was done by M. J. Lighthill in his now famous theory of aerodynamically generated sound. The far-field approximation of the Lighthill theory is incorporated into this model. Many other contributions from various researchers have also been introduced into the model. The model accounts for two noise components: shear noise and self noise. The final result of the model is expressed in terms of a volume integral of the acoustic intensities attributable to these two components, subject to various directivity coefficients. The computer program was written to solve the volume integral. The inputs required by the program are two data files from a computational fluid dynamics (CFD) simulation of the flow of interest: the computational-grid file and the solution file. The CFD solution should be one that has been obtained for conditions that closely approximate those of an experimental test that is yet to be performed. In the current state of development of the model and software, it is recommended that the observation points lie along a radius at an angle >60 from the jet axis. The software provides, and is driven via, a graphical user interface

  17. Mouse Activity across Time Scales: Fractal Scenarios

    PubMed Central

    Lima, G. Z. dos Santos; Lobão-Soares, B.; do Nascimento, G. C.; França, Arthur S. C.; Muratori, L.; Ribeiro, S.; Corso, G.

    2014-01-01

    In this work we devise a classification of mouse activity patterns based on accelerometer data using Detrended Fluctuation Analysis. We use two characteristic mouse behavioural states as benchmarks in this study: waking in free activity and slow-wave sleep (SWS). In both situations we find roughly the same pattern: for short time intervals we observe high correlation in activity - a typical 1/f complex pattern - while for large time intervals there is anti-correlation. High correlation of short intervals ( to : waking state and to : SWS) is related to highly coordinated muscle activity. In the waking state we associate high correlation both to muscle activity and to mouse stereotyped movements (grooming, waking, etc.). On the other side, the observed anti-correlation over large time scales ( to : waking state and to : SWS) during SWS appears related to a feedback autonomic response. The transition from correlated regime at short scales to an anti-correlated regime at large scales during SWS is given by the respiratory cycle interval, while during the waking state this transition occurs at the time scale corresponding to the duration of the stereotyped mouse movements. Furthermore, we find that the waking state is characterized by longer time scales than SWS and by a softer transition from correlation to anti-correlation. Moreover, this soft transition in the waking state encompass a behavioural time scale window that gives rise to a multifractal pattern. We believe that the observed multifractality in mouse activity is formed by the integration of several stereotyped movements each one with a characteristic time correlation. Finally, we compare scaling properties of body acceleration fluctuation time series during sleep and wake periods for healthy mice. Interestingly, differences between sleep and wake in the scaling exponents are comparable to previous works regarding human heartbeat. Complementarily, the nature of these sleep-wake dynamics could lead to a better

  18. Measuring Active Learning to Predict Course Quality

    ERIC Educational Resources Information Center

    Taylor, John E.; Ku, Heng-Yu

    2011-01-01

    This study investigated whether active learning within computer-based training courses can be measured and whether it serves as a predictor of learner-perceived course quality. A major corporation participated in this research, providing access to internal employee training courses, training representatives, and historical course evaluation data.…

  19. Time-driven activity-based costing.

    PubMed

    Kaplan, Robert S; Anderson, Steven R

    2004-11-01

    In the classroom, activity-based costing (ABC) looks like a great way to manage a company's limited resources. But executives who have tried to implement ABC in their organizations on any significant scale have often abandoned the attempt in the face of rising costs and employee irritation. They should try again, because a new approach sidesteps the difficulties associated with large-scale ABC implementation. In the revised model, managers estimate the resource demands imposed by each transaction, product, or customer, rather than relying on time-consuming and costly employee surveys. This method is simpler since it requires, for each group of resources, estimates of only two parameters: how much it costs per time unit to supply resources to the business's activities (the total overhead expenditure of a department divided by the total number of minutes of employee time available) and how much time it takes to carry out one unit of each kind of activity (as estimated or observed by the manager). This approach also overcomes a serious technical problem associated with employee surveys: the fact that, when asked to estimate time spent on activities, employees invariably report percentages that add up to 100. Under the new system, managers take into account time that is idle or unused. Armed with the data, managers then construct time equations, a new feature that enables the model to reflect the complexity of real-world operations by showing how specific order, customer, and activity characteristics cause processing times to vary. This Tool Kit uses concrete examples to demonstrate how managers can obtain meaningful cost and profitability information, quickly and inexpensively. Rather than endlessly updating and maintaining ABC data,they can now spend their time addressing the deficiencies the model reveals: inefficient processes, unprofitable products and customers, and excess capacity.

  20. Identifying healthcare activities using a real-time location system.

    PubMed

    Cagle, Rick; Darling, Erika; Kim, Bo

    2014-01-01

    This article discusses human resource allocation in a Veterans Health Administration audiology clinic as a model for clinics facing similar challenges in maximizing quality, safety, and effectiveness of care. A framework is proposed combining automatic identification technology with simulation and visualization software, asserting a relationship between location of staff within the facility and clinical activity, focusing healthcare staff on high-value activities to deliver safe, quality care. This enables "what-if" analyses of potential resource allocation scenarios, correlating location information from radiofrequency identification tags worn by clinicians and technicians in the clinic as part of a real-time location system, then inferring probable activity from the data. Once the baseline "as-is" can be established, the model will be refined to supply predictive analyses of resource allocation and management. Simulations of activities in specialized spaces saves time managing resources, which means more time can be spent on patient safety and increased satisfaction.

  1. Predicting eruptions from precursory activity using remote sensing data hybridization

    NASA Astrophysics Data System (ADS)

    Reath, K. A.; Ramsey, M. S.; Dehn, J.; Webley, P. W.

    2016-07-01

    Many volcanoes produce some level of precursory activity prior to an eruption. This activity may or may not be detected depending on the available monitoring technology. In certain cases, precursors such as thermal output can be interpreted to make forecasts about the time and magnitude of the impending eruption. Kamchatka (Russia) provides an ideal natural laboratory to study a wide variety of eruption styles and precursory activity prior to an eruption. At Bezymianny volcano for example, a clear increase in thermal activity commonly occurs before an eruption, which has allowed predictions to be made months ahead of time. Conversely, the eruption of Tolbachik volcano in 2012 produced no discernable thermal precursors before the large scale effusive eruption. However, most volcanoes fall between the extremes of consistently behaved and completely undetectable, which is the case with neighboring Kliuchevskoi volcano. This study tests the effectiveness of using thermal infrared (TIR) remote sensing to track volcanic thermal precursors using data from both the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Very High Resolution Radiometer (AVHRR) sensors. It focuses on three large eruptions that produced different levels and durations of effusive and explosive behavior at Kliuchevskoi. Before each of these eruptions, TIR spaceborne sensors detected thermal anomalies (i.e., pixels with brightness temperatures > 2 °C above the background temperature). High-temporal, low-spatial resolution (i.e., ~ hours and 1 km) AVHRR data are ideal for detecting large thermal events occurring over shorter time scales, such as the hot material ejected following strombolian eruptions. In contrast, high-spatial, low-temporal resolution (i.e., days to weeks and 90 m) ASTER data enables the detection of much lower thermal activity; however, activity with a shorter duration will commonly be missed. ASTER and AVHRR data are combined to track low

  2. Time Granularity Transformation of Time Series Data for Failure Prediction of Overhead Line

    NASA Astrophysics Data System (ADS)

    Ma, Yan; Zhu, Wenbing; Yao, Jinxia; Gu, Chao; Bai, Demeng; Wang, Kun

    2017-01-01

    In this paper, we give an approach of transforming time series data with different time granularities into the same plane, which is the basis of further association analysis. We focus on the application of overhead line tripping. First all the relative state variables with line tripping are collected into our big data platform. We collect line account, line fault, lightning, power load and meteorological data. Second we respectively pre-process the five kinds of data to guarantee the integrality of data and simplicity of analysis. We use a representation way combining the aggregated representation and trend extraction methods, which considers both short term variation and long term trend of time sequence. Last we use extensive experiments to demonstrate that the proposed time granularity transformation approach not only lets multiple variables analysed on the same plane, but also has a high prediction accuracy and low running time no matter for SVM or logistic regression algorithm.

  3. Cortical alpha activity predicts the confidence in an impending action

    PubMed Central

    Kubanek, Jan; Hill, N. Jeremy; Snyder, Lawrence H.; Schalk, Gerwin

    2015-01-01

    When we make a decision, we experience a degree of confidence that our choice may lead to a desirable outcome. Recent studies in animals have probed the subjective aspects of the choice confidence using confidence-reporting tasks. These studies showed that estimates of the choice confidence substantially modulate neural activity in multiple regions of the brain. Building on these findings, we investigated the neural representation of the confidence in a choice in humans who explicitly reported the confidence in their choice. Subjects performed a perceptual decision task in which they decided between choosing a button press or a saccade while we recorded EEG activity. Following each choice, subjects indicated whether they were sure or unsure about the choice. We found that alpha activity strongly encodes a subject's confidence level in a forthcoming button press choice. The neural effect of the subjects' confidence was independent of the reaction time and independent of the sensory input modeled as a decision variable. Furthermore, the effect is not due to a general cognitive state, such as reward expectation, because the effect was specifically observed during button press choices and not during saccade choices. The neural effect of the confidence in the ensuing button press choice was strong enough that we could predict, from independent single trial neural signals, whether a subject was going to be sure or unsure of an ensuing button press choice. In sum, alpha activity in human cortex provides a window into the commitment to make a hand movement. PMID:26283892

  4. Sunspot Time Series: Passive and Active Intervals

    NASA Astrophysics Data System (ADS)

    Zięba, S.; Nieckarz, Z.

    2014-07-01

    Solar activity slowly and irregularly decreases from the first spotless day (FSD) in the declining phase of the old sunspot cycle and systematically, but also in an irregular way, increases to the new cycle maximum after the last spotless day (LSD). The time interval between the first and the last spotless day can be called the passive interval (PI), while the time interval from the last spotless day to the first one after the new cycle maximum is the related active interval (AI). Minima of solar cycles are inside PIs, while maxima are inside AIs. In this article, we study the properties of passive and active intervals to determine the relation between them. We have found that some properties of PIs, and related AIs, differ significantly between two group of solar cycles; this has allowed us to classify Cycles 8 - 15 as passive cycles, and Cycles 17 - 23 as active ones. We conclude that the solar activity in the PI declining phase (a descending phase of the previous cycle) determines the strength of the approaching maximum in the case of active cycles, while the activity of the PI rising phase (a phase of the ongoing cycle early growth) determines the strength of passive cycles. This can have implications for solar dynamo models. Our approach indicates the important role of solar activity during the declining and the rising phases of the solar-cycle minimum.

  5. Predictive Motor Timing and the Cerebellar Vermis in Schizophrenia: An fMRI Study.

    PubMed

    Lošák, Jan; Hüttlová, Jitka; Lipová, Petra; Mareček, Radek; Bareš, Martin; Filip, Pavel; Žůbor, Jozef; Ustohal, Libor; Vaníček, Jiří; Kašpárek, Tomáš

    2016-11-01

    Abnormalities in both time processing and dopamine (DA) neurotransmission have been observed in schizophrenia. Time processing seems to be linked to DA neurotransmission. The cognitive dysmetria hypothesis postulates that psychosis might be a manifestation of the loss of coordination of mental processes due to impaired timing. The objective of the present study was to analyze timing abilities and their corresponding functional neuroanatomy in schizophrenia. We performed a functional magnetic resonance imaging (fMRI) study using a predictive motor timing paradigm in 28 schizophrenia patients and 27 matched healthy controls (HC). The schizophrenia patients showed accelerated time processing compared to HC; the amount of the acceleration positively correlated with the degree of positive psychotic symptoms and negatively correlated with antipsychotic dose. This dysfunctional predictive timing was associated with BOLD signal activity alterations in several brain networks, especially those previously described as timing networks (basal ganglia, cerebellum, SMA, and insula) and reward networks (hippocampus, amygdala, and NAcc). BOLD signal activity in the cerebellar vermis was negatively associated with accelerated time processing. Several lines of evidence suggest a direct link between DA transmission and the cerebellar vermis that could explain their relevance for the neurobiology of schizophrenia.

  6. Prediction of shock arrival times from CME and flare data

    NASA Astrophysics Data System (ADS)

    Núñez, Marlon; Nieves-Chinchilla, Teresa; Pulkkinen, Antti

    2016-08-01

    This paper presents the Shock Arrival Model (SARM) for predicting shock arrival times for distances from 0.72 AU to 8.7 AU by using coronal mass ejections (CME) and flare data. SARM is an aerodynamic drag model described by a differential equation that has been calibrated with a data set of 120 shocks observed from 1997 to 2010 by minimizing the mean absolute error (MAE), normalized to 1 AU. SARM should be used with CME data (radial, earthward, or plane-of-sky speeds) and flare data (peak flux, duration, and location). In the case of 1 AU, the MAE and the median of absolute errors were 7.0 h and 5.0 h, respectively, using the available CME/flare data. The best results for 1 AU (an MAE of 5.8 h) were obtained using both CME data, either radial or cone model-estimated speeds, and flare data. For the prediction of shock arrivals at distances from 0.72 AU to 8.7 AU, the normalized MAE and the median were 7.1 h and 5.1 h, respectively, using the available CME/flare data. SARM was also calibrated to be used with CME data alone or flare data alone, obtaining normalized MAE errors of 8.9 h and 8.6 h, respectively, for all shock events. The model verification was carried out with an additional data set of 20 shocks observed from 2010 to 2012 with radial CME speeds to compare SARM with the empirical ESA model and the numerical MHD-based ENLIL model. The results show that the ENLIL's MAE was lower than the SARM's MAE, which was lower than the ESA's MAE. The SARM's best results were obtained when both flare and true CME speeds were used.

  7. Preference as a Function of Active Interresponse Times: A Test of the Active Time Model

    ERIC Educational Resources Information Center

    Misak, Paul; Cleaveland, J. Mark

    2011-01-01

    In this article, we describe a test of the active time model for concurrent variable interval (VI) choice. The active time model (ATM) suggests that the time since the most recent response is one of the variables controlling choice in concurrent VI VI schedules of reinforcement. In our experiment, pigeons were trained in a multiple concurrent…

  8. Less-structured time in children's daily lives predicts self-directed executive functioning.

    PubMed

    Barker, Jane E; Semenov, Andrei D; Michaelson, Laura; Provan, Lindsay S; Snyder, Hannah R; Munakata, Yuko

    2014-01-01

    Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6-7 year-old children's daily, annual, and typical schedules. We categorized children's activities as "structured" or "less-structured" based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up.

  9. Less-structured time in children's daily lives predicts self-directed executive functioning

    PubMed Central

    Barker, Jane E.; Semenov, Andrei D.; Michaelson, Laura; Provan, Lindsay S.; Snyder, Hannah R.; Munakata, Yuko

    2014-01-01

    Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6–7 year-old children's daily, annual, and typical schedules. We categorized children's activities as “structured” or “less-structured” based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up

  10. New Model Predicts Fire Activity in South America

    NASA Video Gallery

    UC Irvine scientist Jim Randerson discusses a new model that is able to predict fire activity in South America using sea surface temperature observations of the Pacific and Atlantic Ocean. The find...

  11. The timing and precision of action prediction in the aging brain

    PubMed Central

    Jones, Alex L.; Cross, Emily S.

    2015-01-01

    Abstract Successful social interactions depend on the ability to anticipate other people's actions. Current conceptualizations of brain function propose that causes of sensory input are inferred through their integration with internal predictions generated in the observer's motor system during action observation. Less is known concerning how action prediction changes with age. Previously we showed that internal action representations are less specific in older compared with younger adults at behavioral and neural levels. Here, we characterize how neural activity varies while healthy older adults aged 56–71 years predict the time‐course of an unfolding action as well as the relation to task performance. By using fMRI, brain activity was measured while participants observed partly occluded actions and judged the temporal coherence of the action continuation that was manipulated. We found that neural activity in frontoparietal and occipitotemporal regions increased the more an action continuation was shifted backwards in time. Action continuations that were shifted towards the future preferentially engaged early visual cortices. Increasing age was associated with neural activity that extended from posterior to anterior regions in frontal and superior temporal cortices. Lower sensitivity in action prediction resulted in activity increases in the caudate. These results imply that the neural implementation of predicting actions undergoes similar changes as the neural process of executing actions in older adults. The comparison between internal predictions and sensory input seems to become less precise with age leading to difficulties in anticipating observed actions accurately, possibly due to less specific internal action models. Hum Brain Mapp 37:54–66, 2016. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. PMID:26503586

  12. MSFC solar activity predictions for satellite orbital lifetime estimation

    NASA Technical Reports Server (NTRS)

    Fuler, H. C.; Lundquist, C. A.; Vaughan, W. W.

    1979-01-01

    The procedure to predict solar activity indexes for use in upper atmosphere density models is given together with an example of the performance. The prediction procedure employs a least square linear regression model to generate the predicted smoothed vinculum R sub 13 and geomagnetic vinculum A sub p(13) values. Linear regression equations are then employed to compute corresponding vinculum F sub 10.7(13) solar flux values from the predicted vinculum R sub 13 values. The output is issued principally for satellite orbital lifetime estimations.

  13. Abnormal activated partial thromboplastin time and malignancy.

    PubMed

    Delicata, M; Hambley, H

    2011-08-01

    Malignancy often results in clotting abnormalities. The aetiology of haemostasis problems in cancer is complex, and is still not completely understood. We describe a case of a patient with malignant mesothelioma, who was found to have elevated activated partial thromboplastin time, due to lupus anticoagulant. We suggest that patients with malignancy should have their coagulation checked prior to any invasive procedures.

  14. Understanding and Predicting Time-Dependent Dune Erosion

    NASA Astrophysics Data System (ADS)

    Long, J.; Stockdon, H. F.; Smith, J. R.

    2014-12-01

    The vulnerability of coastal ecosystems, habitats, and infrastructure is largely dictated by how protective sand dunes respond to extreme waves and water levels during storms. Predicting the type of dune response (e.g., scarping, overwashing, breaching) is often done with conditional storm-impact scale models (e.g. Sallenger 2000) however, these models do not describe the magnitude of expected changes or account for the continuum of dune responses throughout the duration of a storm event. Alternatively, process-based dune erosion models like XBeach explicitly compute interactions between waves, water levels, and sediment transport but are limited in regional applications due to computational requirements and inadequate knowledge of required boundary conditions. Using historical observations of storm-induced coastal change, we are developing and testing a variety of new static, probabilistic, and time-dependent models for dune erosion. Model development is informed by the observed dune response from four events that impacted geomorphically diverse regions along the U.S. Atlantic and Gulf of Mexico coastlines. Results from the static models indicate that alongshore differences in the magnitude of dune elevation change can be related to the depth of water over of the dune crest (e.g. freeboard) but that increasing freeboard does not always correspond to an increased lowering of the dune crest. Applying the concept of dune freeboard in a time-dependent approach that incorporates rising water levels that cause a dune to sequentially experience collision, overwash and then inundation shows that reasonable estimates of dune erosion are obtained. The accuracy of each of the models is now being evaluated along the large and diverse regions of coast that were impacted by Hurricane Sandy in 2012 where dune response was highly variable.

  15. Evidence toads may modulate landing preparation without predicting impact time

    PubMed Central

    Cox, S. M.; Gillis, Gary

    2017-01-01

    ABSTRACT Within anurans (frogs and toads), cane toads (Bufo marinus) perform particularly controlled landings in which the forelimbs are exclusively used to decelerate and stabilize the body after impact. Here we explore how toads achieve dynamic stability across a wide range of landing conditions. Specifically, we suggest that torques during landing could be reduced by aligning forelimbs with the body's instantaneous velocity vector at impact (impact angle). To test whether toad forelimb orientation varies with landing conditions, we used high-speed video to collect forelimb and body kinematic data from six animals hopping off platforms of different heights (0, 5 and 9 cm). We found that toads do align forelimbs with the impact angle. Further, toads align forelimbs with the instantaneous velocity vector well before landing and then track its changes until touchdown. This suggests that toads may be prepared to land well before they hit the ground rather than preparing for impact at a specific moment, and that they may use a motor control strategy that allows them to perform controlled landings without the need to predict impact time. PMID:27895052

  16. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    USGS Publications Warehouse

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  17. Prediction of Universal Time using the artificial neural network

    NASA Astrophysics Data System (ADS)

    Richard, J. Y.; Lopes, P.; Barache, C.; Bizouard, C.; Gambis, D.

    2014-12-01

    The monitoring of the Earth Orientation Parameters (EOP) variations is the main task of the Earth orientation Center of the IERS. In addition, for various applications linked in particular to navigation, precise orbit determination or leap seconds announcements, short and long term predictions are required. The method which is currently applied for predictions is based on deterministic processes, Least Square fitting, autoregressive filtering (Gambis and Luzum 2011). We present an alternative method, the Artificial Neural Networks (ANN) which has have already been successfully applied for pattern recognition. It has been tested as well by various authors for EOP predictions but with so far no real improvement compared to the current methods (Schuh et. al. 2002). New formalisms recently developed allow reconsidering the use of neural networks for EOP predictions. Series of simulations were performed for both short and long term predictions. Statistics and comparisons with the current methods are presented.

  18. PREDICTION OF SOLAR FLARE SIZE AND TIME-TO-FLARE USING SUPPORT VECTOR MACHINE REGRESSION

    SciTech Connect

    Boucheron, Laura E.; Al-Ghraibah, Amani; McAteer, R. T. James

    2015-10-10

    We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field. This work uses support vector regression to formulate a mapping from the 38-dimensional feature space to a continuous-valued label vector representing flare size or time-to-flare. When we consider flaring regions only, we find an average error in estimating flare size of approximately half a geostationary operational environmental satellite (GOES) class. When we additionally consider non-flaring regions, we find an increased average error of approximately three-fourths a GOES class. We also consider thresholding the regressed flare size for the experiment containing both flaring and non-flaring regions and find a true positive rate of 0.69 and a true negative rate of 0.86 for flare prediction. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This is supported by our larger error rates of some 40 hr in the time-to-flare regression problem. The 38 magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem.

  19. Airport noise predicts song timing of European birds.

    PubMed

    Dominoni, Davide M; Greif, Stefan; Nemeth, Erwin; Brumm, Henrik

    2016-09-01

    Anthropogenic noise is of increasing concern to biologists and medical scientists. Its detrimental effects on human health have been well studied, with the high noise levels from air traffic being of particular concern. However, less is known about the effects of airport noise pollution on signal masking in wild animals. Here, we report a relationship between aircraft noise and two major features of the singing behavior of birds. We found that five of ten songbird species began singing significantly earlier in the morning in the vicinity of a major European airport than their conspecifics at a quieter control site. As birds at both sites started singing before the onset of air traffic in the morning, this suggests that the birds in the vicinity of the airport advanced their activity to gain more time for unimpaired singing before the massive plane noise set in. In addition, we found that during the day, chaffinches avoided singing during airplane takeoffs, but only when the noise exceeded a certain threshold, further suggesting that the massive noise caused by the airport can impair acoustic communication in birds. Overall, our study indicates that birds may be adjusting their mating signals and time budgets in response to aircraft noise.

  20. Timing predictability enhances regularity encoding in the human subcortical auditory pathway.

    PubMed

    Gorina-Careta, Natàlia; Zarnowiec, Katarzyna; Costa-Faidella, Jordi; Escera, Carles

    2016-11-17

    The encoding of temporal regularities is a critical property of the auditory system, as short-term neural representations of environmental statistics serve to auditory object formation and detection of potentially relevant novel stimuli. A putative neural mechanism underlying regularity encoding is repetition suppression, the reduction of neural activity to repeated stimulation. Although repetitive stimulation per se has shown to reduce auditory neural activity in animal cortical and subcortical levels and in the human cerebral cortex, other factors such as timing may influence the encoding of statistical regularities. This study was set out to investigate whether temporal predictability in the ongoing auditory input modulates repetition suppression in subcortical stages of the auditory processing hierarchy. Human auditory frequency-following responses (FFR) were recorded to a repeating consonant-vowel stimuli (/wa/) delivered in temporally predictable and unpredictable conditions. FFR amplitude was attenuated by repetition independently of temporal predictability, yet we observed an accentuated suppression when the incoming stimulation was temporally predictable. These findings support the view that regularity encoding spans across the auditory hierarchy and point to temporal predictability as a modulatory factor of regularity encoding in early stages of the auditory pathway.

  1. Timing predictability enhances regularity encoding in the human subcortical auditory pathway

    PubMed Central

    Gorina-Careta, Natàlia; Zarnowiec, Katarzyna; Costa-Faidella, Jordi; Escera, Carles

    2016-01-01

    The encoding of temporal regularities is a critical property of the auditory system, as short-term neural representations of environmental statistics serve to auditory object formation and detection of potentially relevant novel stimuli. A putative neural mechanism underlying regularity encoding is repetition suppression, the reduction of neural activity to repeated stimulation. Although repetitive stimulation per se has shown to reduce auditory neural activity in animal cortical and subcortical levels and in the human cerebral cortex, other factors such as timing may influence the encoding of statistical regularities. This study was set out to investigate whether temporal predictability in the ongoing auditory input modulates repetition suppression in subcortical stages of the auditory processing hierarchy. Human auditory frequency–following responses (FFR) were recorded to a repeating consonant–vowel stimuli (/wa/) delivered in temporally predictable and unpredictable conditions. FFR amplitude was attenuated by repetition independently of temporal predictability, yet we observed an accentuated suppression when the incoming stimulation was temporally predictable. These findings support the view that regularity encoding spans across the auditory hierarchy and point to temporal predictability as a modulatory factor of regularity encoding in early stages of the auditory pathway. PMID:27853313

  2. Prediction of color changes using the time-temperature superposition principle in liquid formulations.

    PubMed

    Mochizuki, Koji; Takayama, Kozo

    2014-01-01

    This study reports the results of applying the time-temperature superposition principle (TTSP) to the prediction of color changes in liquid formulations. A sample solution consisting of L-tryptophan and glucose was used as the model liquid formulation for the Maillard reaction. After accelerated aging treatment at elevated temperatures, the Commission Internationale de l'Eclairage (CIE) LAB color parameters (a*, b*, L*, and E*ab) of the sample solution were measured using a spectrophotometer. The TTSP was then applied to a kinetic analysis of the color changes. The calculated values of the apparent activation energy of a*, b*, L*, and ΔE*ab were 105.2, 109.8, 91.6, and 103.7 kJ/mol, respectively. The predicted values of the color parameters at 40°C were calculated using Arrhenius plots for each of the color parameters. A comparison of the relationships between the experimental and predicted values of each color parameter revealed the coefficients of determination for a*, b*, L*, and ΔE*ab to be 0.961, 0.979, 0.960, and 0.979, respectively. All the R(2) values were sufficiently high, and these results suggested that the prediction was highly reliable. Kinetic analysis using the TTSP was successfully applied to calculating the apparent activation energy and to predicting the color changes at any temperature or duration.

  3. Global cortical activity predicts shape of hand during grasping

    PubMed Central

    Agashe, Harshavardhan A.; Paek, Andrew Y.; Zhang, Yuhang; Contreras-Vidal, José L.

    2015-01-01

    Recent studies show that the amplitude of cortical field potentials is modulated in the time domain by grasping kinematics. However, it is unknown if these low frequency modulations persist and contain enough information to decode grasp kinematics in macro-scale activity measured at the scalp via electroencephalography (EEG). Further, it is unclear as to whether joint angle velocities or movement synergies are the optimal kinematics spaces to decode. In this offline decoding study, we infer from human EEG, hand joint angular velocities as well as synergistic trajectories as subjects perform natural reach-to-grasp movements. Decoding accuracy, measured as the correlation coefficient (r) between the predicted and actual movement kinematics, was r = 0.49 ± 0.02 across 15 hand joints. Across the first three kinematic synergies, decoding accuracies were r = 0.59 ± 0.04, 0.47 ± 0.06, and 0.32 ± 0.05. The spatial-temporal pattern of EEG channel recruitment showed early involvement of contralateral frontal-central scalp areas followed by later activation of central electrodes over primary sensorimotor cortical areas. Information content in EEG about the grasp type peaked at 250 ms after movement onset. The high decoding accuracies in this study are significant not only as evidence for time-domain modulation in macro-scale brain activity, but for the field of brain-machine interfaces as well. Our decoding strategy, which harnesses the neural “symphony” as opposed to local members of the neural ensemble (as in intracranial approaches), may provide a means of extracting information about motor intent for grasping without the need for penetrating electrodes and suggests that it may be soon possible to develop non-invasive neural interfaces for the control of prosthetic limbs. PMID:25914616

  4. Predicting reading and mathematics from neural activity for feedback learning.

    PubMed

    Peters, Sabine; Van der Meulen, Mara; Zanolie, Kiki; Crone, Eveline A

    2017-01-01

    Although many studies use feedback learning paradigms to study the process of learning in laboratory settings, little is known about their relevance for real-world learning settings such as school. In a large developmental sample (N = 228, 8-25 years), we investigated whether performance and neural activity during a feedback learning task predicted reading and mathematics performance 2 years later. The results indicated that feedback learning performance predicted both reading and mathematics performance. Activity during feedback learning in left superior dorsolateral prefrontal cortex (DLPFC) predicted reading performance, whereas activity in presupplementary motor area/anterior cingulate cortex (pre-SMA/ACC) predicted mathematical performance. Moreover, left superior DLPFC and pre-SMA/ACC activity predicted unique variance in reading and mathematics ability over behavioral testing of feedback learning performance alone. These results provide valuable insights into the relationship between laboratory-based learning tasks and learning in school settings, and the value of neural assessments for prediction of school performance over behavioral testing alone. (PsycINFO Database Record

  5. Neural sensitivity to eudaimonic and hedonic rewards differentially predict adolescent depressive symptoms over time

    PubMed Central

    Telzer, Eva H.; Fuligni, Andrew J.; Lieberman, Matthew D.; Galván, Adriana

    2014-01-01

    The pursuit of happiness and reward is an impetus for everyday human behavior and the basis of well-being. Although optimal well-being may be achieved through eudaimonic activities (e.g., meaning and purpose), individuals tend to orient toward hedonic activities (e.g., pleasure seeking), potentially placing them at risk for ill-being. We implemented a longitudinal study and followed adolescents over 1 y to examine whether neural sensitivity to eudaimonic (e.g., prosocial decisions) and hedonic (e.g., selfish rewards and risky decisions) rewards differentially predicts longitudinal changes in depressive symptoms. Ventral striatum activation during eudaimonic decisions predicted longitudinal declines in depressive symptoms, whereas ventral striatum activation to hedonic decisions related to longitudinal increases in depressive symptoms. These findings underscore how the motivational context underlying neural sensitivity to rewards can differentially predict changes in well-being over time. Importantly, to our knowledge, this is the first study to show that striatal activation within an individual can be both a source of risk and protection. PMID:24753574

  6. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

    PubMed Central

    Manikandan, Narayanan; Subha, Srinivasan

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

  7. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

    PubMed

    Manikandan, Narayanan; Subha, Srinivasan

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

  8. Can we predict solar radiation at seasonal time-scale over Europe? A renewable energy perspective.

    NASA Astrophysics Data System (ADS)

    De Felice, Matteo; Alessandri, Andrea

    2015-04-01

    Surface solar radiation can be an important variable for the activities related to renewable energies (photovoltaic) and agriculture. Having accurate forecast may be of potential use for planning and operational tasks. This study examines the predictability of seasonal surface solar radiation comparing ECMWF System4 Seasonal operational forecasts with reanalyses (ERA-INTERIM, MERRA) and other datasets (NASA/GEWEX SRB, WFDEI). This work is focused on the period 1984-2007 and it tries to answer the following questions: 1) How similar are the chosen datasets looking at average and interannual variability? 2) What is the skill of seasonal forecasts in predicting solar radiation? 3) Is it useful for solar power operations and planning the seasonal prediction of solar radiation? It is important to assess the capability of climate datasets in describing surface solar radiation but at the same time it is critical to understand the needs of solar power industry in order to find the right problems to tackle.

  9. Flutter prediction for a wing with active aileron control

    NASA Technical Reports Server (NTRS)

    Penning, K.; Sandlin, D. R.

    1983-01-01

    A method for predicting the vibrational stability of an aircraft with an analog active aileron flutter suppression system (FSS) is expained. Active aileron refers to the use of an active control system connected to the aileron to damp vibrations. Wing vibrations are sensed by accelerometers and the information is used to deflect the aileron. Aerodynamic force caused by the aileron deflection oppose wing vibrations and effectively add additional damping to the system.

  10. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean.

    PubMed

    Schüürmann, Gerrit; Ebert, Ralf-Uwe; Chen, Jingwen; Wang, Bin; Kühne, Ralph

    2008-11-01

    The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient, q (2). This index relates the predictive residual sum of squares, PRESS, to the activity sum of squares, SS, without postprocessing of the model output, the latter of which is automatically done when calculating the conventional squared correlation coefficient, r (2). According to the current OECD guidelines, q (2) for external validation should be calculated with SS referring to the training set activity mean. Our present findings including a mathematical proof demonstrate that this approach yields a systematic overestimation of the prediction capability that is triggered by the difference between the training and test set activity means. Example calculations with three regression models and data sets taken from literature show further that for external test sets, q (2) based on the training set activity mean may become even larger than r (2). As a consequence, we suggest to always use the test set activity mean when quantifying the external prediction capability through q (2) and to revise the respective OECD guidance document accordingly. The discussion includes a comparison between r (2) and q (2) value ranges and the q (2) statistics for cross-validation.

  11. Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

    NASA Astrophysics Data System (ADS)

    Osman, Marisol; Vera, C. S.

    2016-11-01

    This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to

  12. Binding Activity Prediction of Cyclin-Dependent Inhibitors.

    PubMed

    Saha, Indrajit; Rak, Benedykt; Bhowmick, Shib Sankar; Maulik, Ujjwal; Bhattacharjee, Debotosh; Koch, Uwe; Lazniewski, Michal; Plewczynski, Dariusz

    2015-07-27

    The Cyclin-Dependent Kinases (CDKs) are the core components coordinating eukaryotic cell division cycle. Generally the crystal structure of CDKs provides information on possible molecular mechanisms of ligand binding. However, reliable and robust estimation of ligand binding activity has been a challenging task in drug design. In this regard, various machine learning techniques, such as Support Vector Machine, Naive Bayesian classifier, Decision Tree, and K-Nearest Neighbor classifier, have been used. The performance of these heterogeneous classification techniques depends on proper selection of features from the data set. This fact motivated us to propose an integrated classification technique using Genetic Algorithm (GA), Rotational Feature Selection (RFS) scheme, and Ensemble of Machine Learning methods, named as the Genetic Algorithm integrated Rotational Ensemble based classification technique, for the prediction of ligand binding activity of CDKs. This technique can automatically find the important features and the ensemble size. For this purpose, GA encodes the features and ensemble size in a chromosome as a binary string. Such encoded features are then used to create diverse sets of training points using RFS in order to train the machine learning method multiple times. The RFS scheme works on Principal Component Analysis (PCA) to preserve the variability information of the rotational nonoverlapping subsets of original data. Thereafter, the testing points are fed to the different instances of trained machine learning method in order to produce the ensemble result. Here accuracy is computed as a final result after 10-fold cross validation, which also used as an objective function for GA to maximize. The effectiveness of the proposed classification technique has been demonstrated quantitatively and visually in comparison with different machine learning methods for 16 ligand binding CDK docking and rescoring data sets. In addition, the best possible features

  13. Physical Activity Predicts Performance in an Unpracticed Bimanual Coordination Task

    PubMed Central

    Boisgontier, Matthieu P.; Serbruyns, Leen; Swinnen, Stephan P.

    2017-01-01

    Practice of a given physical activity is known to improve the motor skills related to this activity. However, whether unrelated skills are also improved is still unclear. To test the impact of physical activity on an unpracticed motor task, 26 young adults completed the international physical activity questionnaire and performed a bimanual coordination task they had never practiced before. Results showed that higher total physical activity predicted higher performance in the bimanual task, controlling for multiple factors such as age, physical inactivity, music practice, and computer games practice. Linear mixed models allowed this effect of physical activity to be generalized to a large population of bimanual coordination conditions. This finding runs counter to the notion that generalized motor abilities do not exist and supports the existence of a “learning to learn” skill that could be improved through physical activity and that impacts performance in tasks that are not necessarily related to the practiced activity. PMID:28265253

  14. Solar Activity Forecasting for use in Orbit Prediction

    NASA Technical Reports Server (NTRS)

    Schatten, Kenneth

    2001-01-01

    Orbital prediction for satellites in low Earth orbit (LEO) or low planetary orbit depends strongly on exospheric densities. Solar activity forecasting is important in orbital prediction, as the solar UV and EUV inflate the upper atmospheric layers of the Earth and planets, forming the exosphere in which satellites orbit. Geomagnetic effects also relate to solar activity. Because of the complex and ephemeral nature of solar activity, with different cycles varying in strength by more than 100%, many different forecasting techniques have been utilized. The methods range from purely numerical techniques (essentially curve fitting) to numerous oddball schemes, as well as a small subset, called 'Precursor techniques.' The situation can be puzzling, owing to the numerous methodologies involved, somewhat akin to the numerous ether theories near the turn of the last century. Nevertheless, the Precursor techniques alone have a physical basis, namely dynamo theory, which provides a physical explanation for why this subset seems to work. I discuss this solar cycle's predictions, as well as the Sun's observed activity. I also discuss the SODA (Solar Dynamo Amplitude) index, which provides the user with the ability to track the Sun's hidden, interior dynamo magnetic fields. As a result, one may then update solar activity predictions continuously, by monitoring the solar magnetic fields as they change throughout the solar cycle. This paper ends by providing a glimpse into what the next solar cycle (#24) portends.

  15. The Validity of College Grade Prediction Equations Over Time.

    ERIC Educational Resources Information Center

    Sawyer, Richard L.; Maxey, James

    A sample of 260 colleges was surveyed during the years 1972-1976 to determine the validity of predicting college freshmen grades from standardized test scores and high school grades using the American College Testing (ACT) Assessment Program, an evaluative and placement service for students and educators involved in the transition from high school…

  16. Predicting the Timing and Location of the next Hawaiian Volcano

    ERIC Educational Resources Information Center

    Russo, Joseph; Mattox, Stephen; Kildau, Nicole

    2010-01-01

    The wealth of geologic data on Hawaiian volcanoes makes them ideal for study by middle school students. In this paper the authors use existing data on the age and location of Hawaiian volcanoes to predict the location of the next Hawaiian volcano and when it will begin to grow on the floor of the Pacific Ocean. An inquiry-based lesson is also…

  17. Activated partial thromboplastin time and minor coagulopathies.

    PubMed

    Hathaway, W E; Assmus, S L; Montgomery, R R; Dubansky, A S

    1979-01-01

    Five commercially available activated partial thromboplastin time (APTT) test systems were compared with the kaolin partial thromboplastin time (KPTT) method to determine sensitivity in detecting minor coagulation defects. All reagent systems detected severe factor VIII-, IX-, and XI-deficient hemophilia. Homozygous states of factor XII deficiency, Fletcher factor deficiency, and high-molecular-weight kininogen deficiency (Fitzgerald trait) also showed abnormally long APTTs by all systems. Of 19 samples from patients with deficiencies of factors XII, VIII, IX, XI, and II ranging from 2.5 to 52%, eight had deficiencies that were not detected by reagent A (ellagic acid); two, by reagent B (ellagic acid); two, by reagent C (kaolin); one, by reagent D (silica); one, by the KPTT method. All deficiencies were detected by reagent E (celite). Heparin effect on plasma was less well detected by reagent A (ellagic acid) than with the other test systems. APTT test systems can vary greatly in their abilities to detect minor coagulation abnormalities.

  18. Bayesian prediction of earthquake network based on space-time influence domain

    NASA Astrophysics Data System (ADS)

    Zhang, Ya; Zhao, Hai; He, Xuan; Pei, Fan-Dong; Li, Guang-Guang

    2016-03-01

    Bayesian networks (BNs) are used to analyze the conditional dependencies among different events, which are expressed by conditional probability. Scientists have already investigated the seismic activities by using BNs. Recently, earthquake network is used as a novel methodology to analyze the relationships among the earthquake events. In this paper, we propose a way to predict earthquake from a new perspective. The BN is constructed after processing, which is derived from the earthquake network based on space-time influence domain. And then, the BN parameters are learnt by using the cases which are designed from the seismic data in the period between 00:00:00 on January 1, 1992 and 00:00:00 on January 1, 2012. At last, predictions are done for the data in the period between 00:00:00 on January 1, 2012 and 00:00:00 on January 1, 2015 combining the BN with the parameters. The results show that the success rate of the prediction including delayed prediction is about 65%. It is also discovered that the predictions for some nodes have high rate of accuracy under investigation.

  19. Stock Price Change Rate Prediction by Utilizing Social Network Activities

    PubMed Central

    Mitsubuchi, Takashi; Sakurai, Akito

    2014-01-01

    Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques. PMID:24790586

  20. Effects of timing and movement uncertainty implicate the temporo-parietal junction in the prediction of forthcoming motor actions.

    PubMed

    Jakobs, Oliver; Wang, Ling E; Dafotakis, Manuel; Grefkes, Christian; Zilles, Karl; Eickhoff, Simon B

    2009-08-15

    The concept of predictive coding supposes the brain to build predictions of forthcoming events in order to decrease the computational load, thereby facilitating efficient reactions. In contrast, increasing uncertainty, i.e., lower predictability, should increase reaction time and neural activity due to reactive processing and believe updating. We used functional magnetic resonance imaging (fMRI) to scan subjects reacting to briefly presented arrows pointing to either side by pressing a button with the corresponding index finger. Predictability of these stimuli was manipulated along the independently varied factors "response type" (known hand or random, i.e., unknown order) and "timing" (fixed or variable intervals between stimuli). Behavioural data showed a significant reaction-time advantage when either factor was predictable, confirming the hypothesised reduction in computational load. On the neural level, only the right temporo-parietal junction showed enhanced activation upon both increased task and timing uncertainty. Moreover, activity in this region also positively correlated with reaction time. There was, however, a dissociation between both factors in the frontal lobe, as increased timing uncertainty recruited right BA 44, whereas increased response uncertainty activated the right ventral premotor cortex, the pre-SMA and the DLPFC. In line with the theoretical framework of predictive coding as a load-saving mechanism no brain region showed significantly increased activity in the lower uncertainty conditions or correlated negatively with reaction times. This study hence provided behavioural and neuroimaging evidence for predictive motor coding and points to a key role of the right temporo-parietal junction in its implementation.

  1. Physical activity as an indicator of predictive functional disability in elderly.

    PubMed

    Virtuoso Júnior, Jair Sindra; Tribess, Sheilla; Paulo, Thais Reis Silva De; Martins, Cristiane Alves; Romo-Perez, Vicente

    2012-01-01

    To analyze the time spent on physical activity in female and male individuals as a predictor of the absence of functional disability in older adults, a cross-sectional study was conducted with 624 individuals. Receiver Operating Characteristic curves (ROC) were constructed and compared to areas of physical activity by gender and the absence of functional disability. We identified cutoffs of physical activity (minutes / week) to predict the absence of functional disability (CI 95%). It was found that there is a higher area under the ROC curve for the time spent on physical activities in females. It was observed that 280 minutes / week (women) or 410 minutes / week (men) were the best cutoff points for predicting the absence of functional disability. Time spent on physical activity practices can serve as an important indicator to sort priority groups for certain interventions.

  2. Generalized dualities in one-time physics as holographic predictions from two-time physics

    NASA Astrophysics Data System (ADS)

    Araya, Ignacio J.; Bars, Itzhak

    2014-03-01

    In the conventional formalism of physics, with one time, systems with different Hamiltonians or Lagrangians have different physical interpretations and are considered to be independent systems unrelated to each other. However, in this paper we construct explicitly canonical maps in one-time (1T) phase space (including timelike components, specifically the Hamiltonian) to show that it is appropriate to regard various 1T physics systems, with different Lagrangians or Hamiltonians, as being duals of each other. This concept is similar in spirit to dualities discovered in more complicated examples in field theory or string theory. Our approach makes it evident that such generalized dualities are widespread. This suggests that, as a general phenomenon, there are hidden relations and hidden symmetries that conventional 1T physics does not capture, implying the existence of a more unified formulation of physics that naturally supplies the hidden information. In fact, we show that two-time (2T) physics in (d +2) dimensions is the generator of these dualities in 1T physics in d dimensions by providing a holographic perspective that unifies all the dual 1T systems into one. The unifying ingredient is a gauge symmetry in phase space. Via such dualities it is then possible to gain new insights toward new physical predictions not suspected before, and suggest new methods of computation that yield results not obtained before. As an illustration, we will provide concrete examples of 1T systems in classical mechanics that are solved analytically for the first time via our dualities. These dualities in classical mechanics have counterparts in quantum mechanics and field theory, and in some simpler cases they have already been constructed in field theory. We comment on the impact of our approach on the meaning of space-time and on the development of new computational methods based on dualities.

  3. Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

    NASA Technical Reports Server (NTRS)

    Hunter, H. E.; Amato, R. A.

    1972-01-01

    The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

  4. A new mathematical solution for predicting char activation reactions

    USGS Publications Warehouse

    Rafsanjani, H.H.; Jamshidi, E.; Rostam-Abadi, M.

    2002-01-01

    The differential conservation equations that describe typical gas-solid reactions, such as activation of coal chars, yield a set of coupled second-order partial differential equations. The solution of these coupled equations by exact analytical methods is impossible. In addition, an approximate or exact solution only provides predictions for either reaction- or diffusion-controlling cases. A new mathematical solution, the quantize method (QM), was applied to predict the gasification rates of coal char when both chemical reaction and diffusion through the porous char are present. Carbon conversion rates predicted by the QM were in closer agreement with the experimental data than those predicted by the random pore model and the simple particle model. ?? 2002 Elsevier Science Ltd. All rights reserved.

  5. Use of prediction markets to forecast infectious disease activity.

    PubMed

    Polgreen, Philip M; Nelson, Forrest D; Neumann, George R

    2007-01-15

    Prediction markets have accurately forecasted the outcomes of a wide range of future events, including sales of computer printers, elections, and the Federal Reserve's decisions about interest rates. We propose that prediction markets may be useful for tracking and forecasting emerging infectious diseases, such as severe acute respiratory syndrome and avian influenza, by aggregating expert opinion quickly, accurately, and inexpensively. Data from a pilot study in the state of Iowa suggest that these markets can accurately predict statewide seasonal influenza activity 2-4 weeks in advance by using clinical data volunteered from participating health care workers. Information revealed by prediction markets may help to inform treatment, prevention, and policy decisions. Also, these markets could help to refine existing surveillance systems.

  6. Forecasts and predictions of eruptive activity at Mount St. Helens, USA: 1975-1984

    USGS Publications Warehouse

    Swanson, D.A.; Casadevall, T.J.; Dzurisin, D.; Holcomb, R.T.; Newhall, C.G.; Malone, S.D.; Weaver, C.S.

    1985-01-01

    Public statements about volcanic activity at Mount St. Helens include factual statements, forecasts, and predictions. A factual statement describes current conditions but does not anticipate future events. A forecast is a comparatively imprecise statement of the time, place, and nature of expected activity. A prediction is a comparatively precise statement of the time, place, and ideally, the nature and size of impending activity. A prediction usually covers a shorter time period than a forecast and is generally based dominantly on interpretations and measurements of ongoing processes and secondarily on a projection of past history. The three types of statements grade from one to another, and distinctions are sometimes arbitrary. Forecasts and predictions at Mount St. Helens became increasingly precise from 1975 to 1982. Stratigraphic studies led to a long-range forecast in 1975 of renewed eruptive activity at Mount St. Helens, possibly before the end of the century. On the basis of seismic, geodetic and geologic data, general forecasts for a landslide and eruption were issued in April 1980, before the catastrophic blast and landslide on 18 May 1980. All extrusions except two from June 1980 to the end of 1984 were predicted on the basis of integrated geophysical, geochemical, and geologic monitoring. The two extrusions that were not predicted were preceded by explosions that removed a substantial part of the dome, reducing confining pressure and essentially short-circuiting the normal precursors. ?? 1985.

  7. Predicting participation in meaningful activity for older adults with cancer

    PubMed Central

    Pergolotti, Mackenzi; Cutchin, Malcolm P.; Muss, Hyman B.

    2015-01-01

    Purpose Participation in activity that is personally meaningful leads to improved emotional and physical well-being and quality of life. However, little is known about what predicts participation in meaningful activity by older adults with cancer. Methods Seventy-one adults aged 65 years and older with a diagnosis of cancer were enrolled. All adults were evaluated with the following: a brief geriatric assessment, the meaningful activity participation assessment (MAPA), and the Possibilities for Activity Scale (PActS). The MAPA measures participation in meaningful activity, and the PActS measures what older adults believe they should and could be doing. A regression approach was used to assess the predictors of meaningful activity participation. Results The PActS (B = .56, p < .001) was the strongest predictor of meaningful activity participation. Conclusions What older adults with cancer feel they should and could do significantly predicted meaningful participation in activities above and beyond clinical and demographic factors. In future research, perceptions of possibilities for activity may be useful in the design of interventions targeted to improve meaningful participation in older adults with cancer. PMID:25381123

  8. Prefrontal Brain Activity Predicts Temporally Extended Decision-Making Behavior

    ERIC Educational Resources Information Center

    Yarkoni, Tal; Braver, Todd S.; Gray, Jeremy R.; Green, Leonard

    2005-01-01

    Although functional neuroimaging studies of human decision-making processes are increasingly common, most of the research in this area has relied on passive tasks that generate little individual variability. Relatively little attention has been paid to the ability of brain activity to predict overt behavior. Using functional magnetic resonance…

  9. Predicting Physical Activity Promotion in Health Care Settings.

    ERIC Educational Resources Information Center

    Faulkner, Guy; Biddle, Stuart

    2001-01-01

    Tested the theory of planned behavior's (TPB) ability to predict stage of change for physical activity promotion among health professionals. Researchers measured attitudes, subjective norms, intentions, perceived behavioral control, and stage of change, then later reassessed stage of change. TPB variables of attitude, subjective norms, perceived…

  10. Prediction of color changes in acetaminophen solution using the time-temperature superposition principle.

    PubMed

    Mochizuki, Koji; Takayama, Kozo

    2016-01-01

    A prediction method for color changes based on the time-temperature superposition principle (TTSP) was developed for acetaminophen solution. Color changes of acetaminophen solution are caused by the degradation of acetaminophen, such as hydrolysis and oxidation. In principle, the TTSP can be applied to only thermal aging. Therefore, the impact of oxidation on the color changes of acetaminophen solution was verified. The results of our experiment suggested that the oxidation products enhanced the color changes in acetaminophen solution. Next, the color changes of acetaminophen solution samples of the same head space volume after accelerated aging at various temperatures were investigated using the Commission Internationale de l'Eclairage (CIE) LAB color space (a*, b*, L* and ΔE*ab), following which the TTSP was adopted to kinetic analysis of the color changes. The apparent activation energies using the time-temperature shift factor of a*, b*, L* and ΔE*ab were calculated as 72.4, 69.2, 72.3 and 70.9 (kJ/mol), respectively, which are similar to the values for acetaminophen hydrolysis reported in the literature. The predicted values of a*, b*, L* and ΔE*ab at 40 °C were obtained by calculation using Arrhenius plots. A comparison between the experimental and predicted values for each color parameter revealed sufficiently high R(2) values (>0.98), suggesting the high reliability of the prediction. The kinetic analysis using TTSP was successfully applied to predicting the color changes under the controlled oxygen amount at any temperature and for any length of time.

  11. Active Mining from Process Time Series by Learning Classifier System

    NASA Astrophysics Data System (ADS)

    Kurahashi, Setsuya; Terano, Takao

    Continuation processes in chemical and/or biotechnical plants always generate a large amount of time series data. However, since conventional process models are described as a set of control models, it is difficult to explain the complicated and active plant behaviors. Based on the background, this research proposes a novel method to develop a process response model from continuous time-series data. The method consists of the following phases: 1) Collect continuous process data at each tag point in a target plant; 2) Normalize the data in the interval between zero and one; 3) Get the delay time, which maximizes the correlation between given two time series data; 4) Select tags with the higher correlation; 5) Develop a process response model to describe the relations among the process data using the delay time and the correlation values; 6) Develop a process prediction model via several tag points data using a neural network; 1) Discover control rules from the process prediction model using Learning Classifier system. The main contribution of the research is to establish a method to mine a set of meaningful control rules from Learning Classifier System using the Minimal Description Length criteria. The proposed method has been applied to an actual process of a biochemical plant and has shown the validity and the effectiveness.

  12. Neural Network of Predictive Motor Timing in the Context of Gender Differences

    PubMed Central

    Lošák, Jan; Kašpárek, Tomáš; Vaníček, Jiří; Bareš, Martin

    2016-01-01

    Time perception is an essential part of our everyday lives, in both the prospective and the retrospective domains. However, our knowledge of temporal processing is mainly limited to the networks responsible for comparing or maintaining specific intervals or frequencies. In the presented fMRI study, we sought to characterize the neural nodes engaged specifically in predictive temporal analysis, the estimation of the future position of an object with varying movement parameters, and the contingent neuroanatomical signature of differences in behavioral performance between genders. The established dominant cerebellar engagement offers novel evidence in favor of a pivotal role of this structure in predictive short-term timing, overshadowing the basal ganglia reported together with the frontal cortex as dominant in retrospective temporal processing in the subsecond spectrum. Furthermore, we discovered lower performance in this task and massively increased cerebellar activity in women compared to men, indicative of strategy differences between the genders. This promotes the view that predictive temporal computing utilizes comparable structures in the retrospective timing processes, but with a definite dominance of the cerebellum. PMID:27019753

  13. Neural Network of Predictive Motor Timing in the Context of Gender Differences.

    PubMed

    Filip, Pavel; Lošák, Jan; Kašpárek, Tomáš; Vaníček, Jiří; Bareš, Martin

    2016-01-01

    Time perception is an essential part of our everyday lives, in both the prospective and the retrospective domains. However, our knowledge of temporal processing is mainly limited to the networks responsible for comparing or maintaining specific intervals or frequencies. In the presented fMRI study, we sought to characterize the neural nodes engaged specifically in predictive temporal analysis, the estimation of the future position of an object with varying movement parameters, and the contingent neuroanatomical signature of differences in behavioral performance between genders. The established dominant cerebellar engagement offers novel evidence in favor of a pivotal role of this structure in predictive short-term timing, overshadowing the basal ganglia reported together with the frontal cortex as dominant in retrospective temporal processing in the subsecond spectrum. Furthermore, we discovered lower performance in this task and massively increased cerebellar activity in women compared to men, indicative of strategy differences between the genders. This promotes the view that predictive temporal computing utilizes comparable structures in the retrospective timing processes, but with a definite dominance of the cerebellum.

  14. Prediction of adolescents doing physical activity after completing secondary education.

    PubMed

    Moreno-Murcia, Juan Antonio; Huéscar, Elisa; Cervelló, Eduardo

    2012-03-01

    The purpose of this study, based on the self-determination theory (Ryan & Deci, 2000) was to test the prediction power of student's responsibility, psychological mediators, intrinsic motivation and the importance attached to physical education in the intention to continue to practice some form of physical activity and/or sport, and the possible relationships that exist between these variables. We used a sample of 482 adolescent students in physical education classes, with a mean age of 14.3 years, which were measured for responsibility, psychological mediators, sports motivation, the importance of physical education and intention to be physically active. We completed an analysis of structural equations modelling. The results showed that the responsibility positively predicted psychological mediators, and this predicted intrinsic motivation, which positively predicted the importance students attach to physical education, and this, finally, positively predicted the intention of the student to continue doing sport. Results are discussed in relation to the promotion of student's responsibility towards a greater commitment to the practice of physical exercise.

  15. Using Time-Series Regression to Predict Academic Library Circulations.

    ERIC Educational Resources Information Center

    Brooks, Terrence A.

    1984-01-01

    Four methods were used to forecast monthly circulation totals in 15 midwestern academic libraries: dummy time-series regression, lagged time-series regression, simple average (straight-line forecasting), monthly average (naive forecasting). In tests of forecasting accuracy, dummy regression method and monthly mean method exhibited smallest average…

  16. Predicting active users' personality based on micro-blogging behaviors.

    PubMed

    Li, Lin; Li, Ang; Hao, Bibo; Guan, Zengda; Zhu, Tingshao

    2014-01-01

    Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 839 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory [corrected]. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors.

  17. REAL TIME DATA FOR REMEDIATION ACTIVITIES [11505

    SciTech Connect

    BROCK CT

    2011-01-13

    Health physicists from the CH2M HILL Plateau Remediation Company collaborated with Berkeley Nucleonics Corporation to modify the SAM 940 isotope identifier instrument to be used for nuclear waste remediation. These modifications coupled with existing capabilities of the SAM 940 have proven to be invaluable during remediation activities, reducing disposal costs by allowing swift remediation of targeted areas that have been identified as having isotopes of concern (IOC), and eliminating multiple visits to sites by declaring an excavation site clear of IOCs before demobilizing from the site. These advantages are enabled by accumulating spectral data for specific isotopes that is nearly 100 percent free of false positives, which are filtered out in 'real time.'

  18. Nestling activity levels during begging behaviour predicts activity level and body mass in adulthood

    PubMed Central

    Griffith, Simon C.

    2014-01-01

    Across a range of species including humans, personality traits, or differences in behaviour between individuals that are consistent over time, have been demonstrated. However, few studies have measured whether these consistent differences are evident in very young animals, and whether they persist over an individual’s entire lifespan. Here we investigated the begging behaviour of very young cross-fostered zebra finch nestlings and the relationship between that and adult activity levels. We found a link between the nestling activity behaviour head movements during begging, measured at just five and seven days after hatching, and adult activity levels, measured when individuals were between three and three and a half years old. Moreover, body mass was found to be negatively correlated with both nestling and adult activity levels, suggesting that individuals which carry less body fat as adults are less active both as adults and during begging as nestlings. Our work suggests that the personality traits identified here in both very young nestlings and adults may be linked to physiological factors such as metabolism or environmental sources of variation. Moreover, our work suggests it may be possible to predict an individual’s future adult personality at a very young age, opening up new avenues for future work to explore the relationship between personality and a number of aspects of individual life history and survival. PMID:25279258

  19. Predicting brain activity using a Bayesian spatial model.

    PubMed

    Derado, Gordana; Bowman, F Dubois; Zhang, Lijun

    2013-08-01

    Increasing the clinical applicability of functional neuroimaging technology is an emerging objective, e.g. for diagnostic and treatment purposes. We propose a novel Bayesian spatial hierarchical framework for predicting follow-up neural activity based on an individual's baseline functional neuroimaging data. Our approach attempts to overcome some shortcomings of the modeling methods used in other neuroimaging settings, by borrowing strength from the spatial correlations present in the data. Our proposed methodology is applicable to data from various imaging modalities including functional magnetic resonance imaging and positron emission tomography, and we provide an illustration here using positron emission tomography data from a study of Alzheimer's disease to predict disease progression.

  20. Assessing time-integrated dissolved concentrations and predicting toxicity of metals during diel cycling in streams

    USGS Publications Warehouse

    Balistrieri, Laurie S.; Nimick, David A.; Mebane, Christopher A.

    2012-01-01

    Evaluating water quality and the health of aquatic organisms is challenging in systems with systematic diel (24 hour) or less predictable runoff-induced changes in water composition. To advance our understanding of how to evaluate environmental health in these dynamic systems, field studies of diel cycling were conducted in two streams (Silver Bow Creek and High Ore Creek) affected by historical mining activities in southwestern Montana. A combination of sampling and modeling tools were used to assess the toxicity of metals in these systems. Diffusive Gradients in Thin Films (DGT) samplers were deployed at multiple time intervals during diel sampling to confirm that DGT integrates time-varying concentrations of dissolved metals. Thermodynamic speciation calculations using site specific water compositions, including time-integrated dissolved metal concentrations determined from DGT, and a competitive, multiple-metal biotic ligand model incorporated into the Windemere Humic Aqueous Model Version 6.0 (WHAM VI) were used to determine the chemical speciation of dissolved metals and biotic ligands. The model results were combined with previously collected toxicity data on cutthroat trout to derive a relationship that predicts the relative survivability of these fish at a given site. This integrative approach may prove useful for assessing water quality and toxicity of metals to aquatic organisms in dynamic systems and evaluating whether potential changes in environmental health of aquatic systems are due to anthropogenic activities or natural variability.

  1. Combining Satellite Observations of Fire Activity and Numerical Weather Prediction to Improve the Prediction of Smoke Emissions

    NASA Astrophysics Data System (ADS)

    Peterson, D. A.; Wang, J.; Hyer, E. J.; Ichoku, C. M.

    2012-12-01

    Smoke emissions estimates used in air quality and visibility forecasting applications are currently limited by the information content of satellite fire observations, and the lack of a skillful short-term forecast of changes in fire activity. This study explores the potential benefits of a recently developed sub-pixel-based calculation of fire radiative power (FRPf) from the MODerate Resolution Imaging Spectroradiometer (MODIS), which provides more precise estimates of the radiant energy (over the retrieved fire area) that in turn, improves estimates of the thermal buoyancy of smoke plumes and may be helpful characterizing the meteorological effects on fire activity for large fire events. Results show that unlike the current FRP product, the incorporation of FRPf produces a statistically significant correlation (R = 0.42) with smoke plume height data provided by the Multi-angle Imaging SpectroRadiometer (MISR) and several meteorological variables, such as surface wind speed and temperature, which may be useful for discerning cases where smoke was injected above the boundary layer. Drawing from recent advances in numerical weather prediction (NWP), this study also examines the meteorological conditions characteristic of fire ignition, growth, decay, and extinction, which are used to develop an automated, 24-hour prediction of satellite fire activity. Satellite fire observations from MODIS and geostationary sensors show that the fire prediction model is an improvement (RMSE reduction of 13 - 20%) over the forecast of persistence commonly used by near-real-time fire emission inventories. The ultimate goal is to combine NWP data and satellite fire observations to improve both analysis and prediction of biomass-burning emissions, through improved understanding of the interactions between fire activity and weather at scales appropriate for operational modeling. This is a critical step toward producing a global fire prediction model and improving operational forecasts of

  2. Intelligent real-time performance monitoring and quality prediction for batch/fed-batch cultivations.

    PubMed

    Undey, Cenk; Tatara, Eric; Cinar, Ali

    2004-02-19

    Supervision of batch bioprocess operations in real-time during the progress of a batch run offers many advantages over end-of-batch quality control. Multivariate statistical techniques such as multiway partial least squares (MPLS) provide an efficient modeling and supervision framework. A new type of MPLS modeling technique that is especially suitable for online real-time process monitoring and the multivariate monitoring charts are presented. This online process monitoring technique is also extended to include predictions of end-of-batch quality measurements during the progress of a batch run. Process monitoring, quality estimation and fault diagnosis activities are automated and supervised by embedding them into a real-time knowledge-based system (RTKBS). Interpretation of multivariate charts is also automated through a generic rule-base for efficient alarm handling. The integrated RTKBS and the implementation of MPLS-based process monitoring and quality control are illustrated using a fed-batch penicillin production benchmark process simulator.

  3. Platelet Serotonin Transporter Function Predicts Default-Mode Network Activity

    PubMed Central

    Kasess, Christian H.; Meyer, Bernhard M.; Hofmaier, Tina; Diers, Kersten; Bartova, Lucie; Pail, Gerald; Huf, Wolfgang; Uzelac, Zeljko; Hartinger, Beate; Kalcher, Klaudius; Perkmann, Thomas; Haslacher, Helmuth; Meyer-Lindenberg, Andreas; Kasper, Siegfried; Freissmuth, Michael; Windischberger, Christian; Willeit, Matthäus; Lanzenberger, Rupert; Esterbauer, Harald; Brocke, Burkhard; Moser, Ewald; Sitte, Harald H.; Pezawas, Lukas

    2014-01-01

    Background The serotonin transporter (5-HTT) is abundantly expressed in humans by the serotonin transporter gene SLC6A4 and removes serotonin (5-HT) from extracellular space. A blood-brain relationship between platelet and synaptosomal 5-HT reuptake has been suggested, but it is unknown today, if platelet 5-HT uptake can predict neural activation of human brain networks that are known to be under serotonergic influence. Methods A functional magnetic resonance study was performed in 48 healthy subjects and maximal 5-HT uptake velocity (Vmax) was assessed in blood platelets. We used a mixed-effects multilevel analysis technique (MEMA) to test for linear relationships between whole-brain, blood-oxygen-level dependent (BOLD) activity and platelet Vmax. Results The present study demonstrates that increases in platelet Vmax significantly predict default-mode network (DMN) suppression in healthy subjects independent of genetic variation within SLC6A4. Furthermore, functional connectivity analyses indicate that platelet Vmax is related to global DMN activation and not intrinsic DMN connectivity. Conclusion This study provides evidence that platelet Vmax predicts global DMN activation changes in healthy subjects. Given previous reports on platelet-synaptosomal Vmax coupling, results further suggest an important role of neuronal 5-HT reuptake in DMN regulation. PMID:24667541

  4. Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations

    SciTech Connect

    Marre, O.; El Boustani, S.; Fregnac, Y.; Destexhe, A.

    2009-04-03

    We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.

  5. Predicting Individual Research Productivity: More than a Question of Time

    ERIC Educational Resources Information Center

    Ito, Jack K.; Brotheridge, Celeste M.

    2007-01-01

    Despite professors' education and socialization and the significant rewards they receive for research activities and output, the 80/20 rule seems to apply; that is, there exists a system of stars who produce a disproportionate volume of research such that most research tends to be undertaken by a small percentage of the academy (Erkut, 2002).…

  6. Predicting complex syntactic structure in real time: Processing of negative sentences in Russian.

    PubMed

    Kazanina, Nina

    2016-09-19

    In Russian negative sentences the verb's direct object may appear either in the accusative case, which is licensed by the verb (as is common cross-linguistically), or in the genitive case, which is licensed by the negation (Russian-specific "genitive-of-negation" phenomenon). Such sentences were used to investigate whether case marking is employed for anticipating syntactic structure, and whether lexical heads other than the verb can be predicted on the basis of a case-marked noun phrase. Experiment 1, a completion task, confirmed that genitive-of-negation is part of Russian speakers' active grammatical repertoire. In Experiments 2 and 3, the genitive/accusative case manipulation on the preverbal object led to shorter reading times at the negation and verb in the genitive versus accusative condition. Furthermore, Experiment 3 manipulated linear order of the direct object and the negated verb in order to distinguish whether the abovementioned facilitatory effect was predictive or integrative in nature, and concluded that the parser actively predicts a verb and (otherwise optional) negation on the basis of a preceding genitive-marked object. Similarly to a head-final language, case-marking information on preverbal noun phrases (NPs) is used by the parser to enable incremental structure building in a free-word-order language such as Russian.

  7. Strainrange partitioning life predictions of the long time metal properties council creep-fatigue tests

    NASA Technical Reports Server (NTRS)

    Saltsman, J. F.; Halford, G. R.

    1979-01-01

    The method of strainrange partitioning is used to predict the cyclic lives of the Metal Properties Council's long time creep-fatigue interspersion tests of several steel alloys. Comparisons are made with predictions based upon the time- and cycle-fraction approach. The method of strainrange partitioning is shown to give consistently more accurate predictions of cyclic life than is given by the time- and cycle-fraction approach.

  8. SALSA3D: A Tomographic Model of Compressional Wave Slowness in the Earth’s Mantle for Improved Travel-Time Prediction and Travel-Time Prediction Uncertainty

    SciTech Connect

    Ballard, Sanford; Hipp, James R.; Begnaud, Michael L.; Young, Christopher J.; Encarnacao, Andre V.; Chael, Eric P.; Phillips, W. Scott

    2016-10-11

    The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. In this study, motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel-time predictions for P and Pn ray paths through the model. Path-dependent travel-time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source to receiver. Path-dependent travel-time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel-time prediction uncertainty is a direct result of the off-diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel-time prediction uncertainty. In addition, the computed pattern of uncertainty differs significantly from that of 1D distance-dependent travel-time uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.

  9. SALSA3D: A Tomographic Model of Compressional Wave Slowness in the Earth’s Mantle for Improved Travel-Time Prediction and Travel-Time Prediction Uncertainty

    DOE PAGES

    Ballard, Sanford; Hipp, James R.; Begnaud, Michael L.; ...

    2016-10-11

    The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. In this study, motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel-time predictions for P and Pn ray paths through the model. Path-dependent travel-time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source tomore » receiver. Path-dependent travel-time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel-time prediction uncertainty is a direct result of the off-diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel-time prediction uncertainty. In addition, the computed pattern of uncertainty differs significantly from that of 1D distance-dependent travel-time uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.« less

  10. A New Prediction Method for the Arrival Time of Interplanetary Shocks

    NASA Astrophysics Data System (ADS)

    Feng, Xueshang; Zhao, Xinhua

    2006-10-01

    Solar transient activities such as solar flares, disappearing filaments, and coronal mass ejections (CMEs) are solar manifestations of interplanetary (IP) disturbances. Forecasting the arrival time at the near Earth space of the associated interplanetary shocks following these solar disturbances is an important aspect in space weather forecasting because the shock arrival usually marks the geomagnetic storm sudden commencement (SSC) when the IMF Bz component is appropriately southward and/or the solar wind dynamic pressure behind the shock is sufficiently large. Combining the analytical study for the propagation of the blast wave from a point source in a moving, steady-state, medium with variable density (wei, 1982; wei and dryer 1991) with the energy estimation method in the ISPM model (smith and dryer 1990, 1995), we present a new shock propagation model (called SPM below) for predicting the arrival time of interplanetary shocks at Earth. The duration of the X-ray flare, the initial shock speed and the total energy of the transient event are used for predicting the arrival of the associated shocks in our model. Especially, the background speed, i.e., the convection effect of the solar wind is considered in this model. Applying this model to 165 solar events during the periods of January 1979 to October 1989 and February 1997 to August 2002, we found that our model could be practically equivalent to the prevalent models of STOA, ISPM and HAFv.2 in forecasting the shock arrival time. The absolute error in the transit time in our model is not larger than those of the other three models for the same sample events. Also, the prediction test shows that the relative error of our model is ≤10% for 27.88% of all events, ≤30% for 71.52%, and ≤50% for 85.46%, which is comparable to the relative errors of the other models. These results might demonstrate a potential capability of our model in terms of real-time forecasting.

  11. Predicting Developmental Timing for Immature Canada Thistle Stem-Mining Weevils, Hadroplontus litura (Coleoptera: Curculionidae).

    PubMed

    Gramig, Greta G; Burns, Erin E; Prischmann-Voldseth, Deirdre A

    2015-08-01

    Predictions of phenological development for insect biological control agents may facilitate post-release monitoring efforts by allowing land managers to optimize the timing of monitoring activities. A logistic thermal time model was tested to predict phenology of immature stem-mining weevils, Hadroplontus litura F. (Coleoptera: Curculionidae), a biological control agent for Canada thistle, Cirsium arvense L. (Asterales: Asteraceae). Weevil eggs and larvae were collected weekly from Canada thistle stems in eastern North Dakota from May through July during 2010 and 2011. Head capsule widths of sampled larvae were measured at the widest point and plotted on a frequency histogram to establish ranges of head capsule widths associated with each instar. We found head capsule width ranges for first-, second-, and third-instar H. litura larvae were 165-324 µm, 346-490 µm, and 506-736 µm, respectively. Logistic regression models were developed to estimate the proportions of H. litura eggs, first-, and second-instar larvae in the weevil population as a function of thermal time. Model estimates of median development time for eggs, first instars, and second instars ranged from 219 ± 23 degree-days (DD) to 255 ± 27 DD, 556 ± 77 DD to 595 ± 81 DD, and 595 ± 109 DD to 653 ± 108 DD, respectively. Based on model validation statistics, model estimates for development timing were the most accurate for eggs and first instars and somewhat less accurate for second instars. These model predictions will help biological control practitioners obtain more accurate estimates of weevil population densities during post-release monitoring.

  12. Predicting the Timing of Women's Departure from Abusive Relationships

    ERIC Educational Resources Information Center

    Panchanadeswaran, Subadra; McCloskey, Laura A.

    2007-01-01

    The aim of this study was to investigate forces that affect the timing of women's exit from violent relationships with men. Abused women were recruited from posters in the community and battered women's shelters, interviewed, and followed up for 10 years. Data for this study are based on 100 women and were analyzed using event history analysis.…

  13. Predicting the unpredictable: critical analysis and practical implications of predictive anticipatory activity

    PubMed Central

    Mossbridge, Julia A.; Tressoldi, Patrizio; Utts, Jessica; Ives, John A.; Radin, Dean; Jonas, Wayne B.

    2014-01-01

    A recent meta-analysis of experiments from seven independent laboratories (n = 26) indicates that the human body can apparently detect randomly delivered stimuli occurring 1–10 s in the future (Mossbridge etal., 2012). The key observation in these studies is that human physiology appears to be able to distinguish between unpredictable dichotomous future stimuli, such as emotional vs. neutral images or sound vs. silence. This phenomenon has been called presentiment (as in “feeling the future”). In this paper we call it predictive anticipatory activity (PAA). The phenomenon is “predictive” because it can distinguish between upcoming stimuli; it is “anticipatory” because the physiological changes occur before a future event; and it is an “activity” because it involves changes in the cardiopulmonary, skin, and/or nervous systems. PAA is an unconscious phenomenon that seems to be a time-reversed reflection of the usual physiological response to a stimulus. It appears to resemble precognition (consciously knowing something is going to happen before it does), but PAA specifically refers to unconscious physiological reactions as opposed to conscious premonitions. Though it is possible that PAA underlies the conscious experience of precognition, experiments testing this idea have not produced clear results. The first part of this paper reviews the evidence for PAA and examines the two most difficult challenges for obtaining valid evidence for it: expectation bias and multiple analyses. The second part speculates on possible mechanisms and the theoretical implications of PAA for understanding physiology and consciousness. The third part examines potential practical applications. PMID:24723870

  14. A new fingerprint to predict nonribosomal peptides activity

    NASA Astrophysics Data System (ADS)

    Abdo, Ammar; Caboche, Ségolène; Leclère, Valérie; Jacques, Philippe; Pupin, Maude

    2012-10-01

    Bacteria and fungi use a set of enzymes called nonribosomal peptide synthetases to provide a wide range of natural peptides displaying structural and biological diversity. So, nonribosomal peptides (NRPs) are the basis for some efficient drugs. While discovering new NRPs is very desirable, the process of identifying their biological activity to be used as drugs is a challenge. In this paper, we present a novel peptide fingerprint based on monomer composition (MCFP) of NRPs. MCFP is a novel method for obtaining a representative description of NRP structures from their monomer composition in fingerprint form. Experiments with Norine NRPs database and MCFP show high prediction accuracy (>93 %). Also a high recall rate (>82 %) is obtained when MCFP is used for screening NRPs database. From this study it appears that our fingerprint, built from monomer composition, allows an effective screening and prediction of biological activities of NRPs database.

  15. Predicting survival time for metastatic castration resistant prostate cancer: An iterative imputation approach

    PubMed Central

    Deng, Detian; Du, Yu; Ji, Zhicheng; Rao, Karthik; Wu, Zhenke; Zhu, Yuxin; Coley, R. Yates

    2016-01-01

    In this paper, we present our winning method for survival time prediction in the 2015 Prostate Cancer DREAM Challenge, a recent crowdsourced competition focused on risk and survival time predictions for patients with metastatic castration-resistant prostate cancer (mCRPC). We are interested in using a patient's covariates to predict his or her time until death after initiating standard therapy. We propose an iterative algorithm to multiply impute right-censored survival times and use ensemble learning methods to characterize the dependence of these imputed survival times on possibly many covariates. We show that by iterating over imputation and ensemble learning steps, we guide imputation with patient covariates and, subsequently, optimize the accuracy of survival time prediction. This method is generally applicable to time-to-event prediction problems in the presence of right-censoring. We demonstrate the proposed method's performance with training and validation results from the DREAM Challenge and compare its accuracy with existing methods. PMID:28299176

  16. Time delay between cardiac and brain activity during sleep transitions

    NASA Astrophysics Data System (ADS)

    Long, Xi; Arends, Johan B.; Aarts, Ronald M.; Haakma, Reinder; Fonseca, Pedro; Rolink, Jérôme

    2015-04-01

    Human sleep consists of wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) sleep that includes light and deep sleep stages. This work investigated the time delay between changes of cardiac and brain activity for sleep transitions. Here, the brain activity was quantified by electroencephalographic (EEG) mean frequency and the cardiac parameters included heart rate, standard deviation of heartbeat intervals, and their low- and high-frequency spectral powers. Using a cross-correlation analysis, we found that the cardiac variations during wake-sleep and NREM sleep transitions preceded the EEG changes by 1-3 min but this was not the case for REM sleep transitions. These important findings can be further used to predict the onset and ending of some sleep stages in an early manner.

  17. Meditation-induced states predict attentional control over time.

    PubMed

    Colzato, Lorenza S; Sellaro, Roberta; Samara, Iliana; Baas, Matthijs; Hommel, Bernhard

    2015-12-01

    Meditation is becoming an increasingly popular topic for scientific research and various effects of extensive meditation practice (ranging from weeks to several years) on cognitive processes have been demonstrated. Here we show that extensive practice may not be necessary to achieve those effects. Healthy adult non-meditators underwent a brief single session of either focused attention meditation (FAM), which is assumed to increase top-down control, or open monitoring meditation (OMM), which is assumed to weaken top-down control, before performing an Attentional Blink (AB) task - which assesses the efficiency of allocating attention over time. The size of the AB was considerably smaller after OMM than after FAM, which suggests that engaging in meditation immediately creates a cognitive-control state that has a specific impact on how people allocate their attention over time.

  18. Prediction of time to go of IR imaging GIF

    NASA Astrophysics Data System (ADS)

    Fan, Min-ge; Peng, Zhi-yong; Luo, Xiao-Liang; Lu, Jin

    2011-08-01

    During the infrared imaging guided missile-target terminal impact, the remaining time estimating accuracy plays a very important role to missile burst control. The precision of anti-aircraft missile is sensitive to the angle-measured error by using infrared imaging GIF technology. But in theory, the distance information can be introduced to lower the negative effect of the angle-measured error. So, how to get missile-target distance is a key. The use of laser fuze is common solution, which but makes the system more complexity and cost higher. The paper proposes a distance-measured method, which the missile-target distance is obtained by using the grey value of target tracking point in successive infrared image frame. Then the distance and angle information is integrated together to estimate the missile-target impact time.

  19. Predicted Water Immersion Survival Times for Anti-Exposure Ensembles

    DTIC Science & Technology

    2005-10-01

    level of garment insulation and anthropometrics to provide guidelines for safe immersed exposure times. 176 Report Documentation Page Form ApprovedOMB...males only. The model can account for variations in weight, body fat (BF), and metabolic rate. For this study, three anthropometric cases were used: (1...chest, abdomen, right and left thighs, calves, feet, biceps, forearms and hands. A clo value can be specified for each area. Clo is a measure of

  20. Predicting Activity Energy Expenditure Using the Actical[R] Activity Monitor

    ERIC Educational Resources Information Center

    Heil, Daniel P.

    2006-01-01

    This study developed algorithms for predicting activity energy expenditure (AEE) in children (n = 24) and adults (n = 24) from the Actical[R] activity monitor. Each participant performed 10 activities (supine resting, three sitting, three house cleaning, and three locomotion) while wearing monitors on the ankle, hip, and wrist; AEE was computed…

  1. Improving active space telescope wavefront control using predictive thermal modeling

    NASA Astrophysics Data System (ADS)

    Gersh-Range, Jessica; Perrin, Marshall D.

    2015-01-01

    Active control algorithms for space telescopes are less mature than those for large ground telescopes due to differences in the wavefront control problems. Active wavefront control for space telescopes at L2, such as the James Webb Space Telescope (JWST), requires weighing control costs against the benefits of correcting wavefront perturbations that are a predictable byproduct of the observing schedule, which is known and determined in advance. To improve the control algorithms for these telescopes, we have developed a model that calculates the temperature and wavefront evolution during a hypothetical mission, assuming the dominant wavefront perturbations are due to changes in the spacecraft attitude with respect to the sun. Using this model, we show that the wavefront can be controlled passively by introducing scheduling constraints that limit the allowable attitudes for an observation based on the observation duration and the mean telescope temperature. We also describe the implementation of a predictive controller designed to prevent the wavefront error (WFE) from exceeding a desired threshold. This controller outperforms simpler algorithms even with substantial model error, achieving a lower WFE without requiring significantly more corrections. Consequently, predictive wavefront control based on known spacecraft attitude plans is a promising approach for JWST and other future active space observatories.

  2. Mothers' Prenatal Activities Predict Adjustment to Pregnancy and Early Parenting.

    ERIC Educational Resources Information Center

    Abraham, Ronalda; Turner, Nita

    This study examined the activities of pregnant women and how these activities facilitated a positive adjustment to pregnancy and early parenting. Subjects were 49 expectant first-time mothers ranging in age from 20 to 41 and attending a childhood preparation class. Eighty-two percent of the women were married. Subjects completed two questionnaires…

  3. Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

    NASA Technical Reports Server (NTRS)

    Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

    2015-01-01

    activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity-building applications that extend numerical weather prediction to developing countries are intended to provide near real-time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather-related hazards, or impacts that local weather events may have during the recovery phase.

  4. Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

    NASA Astrophysics Data System (ADS)

    Anggraeni, Novia Antika

    2015-04-01

    The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano's inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 - 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between -2.86 up to 5.49 days.

  5. Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

    SciTech Connect

    Anggraeni, Novia Antika

    2015-04-24

    The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days.

  6. Early prediction of movie box office success based on Wikipedia activity big data.

    PubMed

    Mestyán, Márton; Yasseri, Taha; Kertész, János

    2013-01-01

    Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

  7. Improving predictability of time series using maximum entropy methods

    NASA Astrophysics Data System (ADS)

    Chliamovitch, G.; Dupuis, A.; Golub, A.; Chopard, B.

    2015-04-01

    We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, in low dimension, there exists a subset of the space of stochastic matrices for which the MaxEnt method is more efficient than sampling, in the sense that shorter historical samples have to be considered to reach the same accuracy. Considering short samples is of particular interest when modelling smoothly non-stationary processes, which provides, under some conditions, a powerful forecasting tool. The method is illustrated for a discretized empirical series of exchange rates.

  8. Sphingoid Base Metabolism in Yeast: Mapping Gene Expression Patterns Into Qualitative Metabolite Time Course Predictions

    PubMed Central

    2001-01-01

    Can qualitative metabolite time course predictions be inferred from measured mRNA expression patterns? Speaking against this possibility is the large number of ‘decoupling’ control points that lie between these variables, i.e. translation, protein degradation, enzyme inhibition and enzyme activation. Speaking for it is the notion that these control points might be coordinately regulated such that action exerted on the mRNA level is informative of action exerted on the protein and metabolite levels. A simple kinetic model of sphingoid base metabolism in yeast is postulated. When the enzyme activities in this model are modulated proportional to mRNA expression levels measured in heat shocked yeast, the model yields a transient rise and fall in sphingoid bases followed by a permanent rise in ceramide. This finding is in qualitative agreement with experiments and is thus consistent with the aforementioned coordinated control system hypothesis. PMID:18629242

  9. Variability in affective activation predicts non-suicidal self-injury in eating disorders.

    PubMed

    Vansteelandt, Kristof; Claes, Laurence; Muehlenkamp, Jennifer; De Cuyper, Kathleen; Lemmens, Jos; Probst, Michel; Vanderlinden, Johan; Pieters, Guido

    2013-03-01

    We examined whether affective variability can predict non-suicidal self-injury (NSSI) in eating disorders. Affect was represented by valence (positive versus negative) and activation (high versus low). Twenty-one patients with anorexia nervosa-restricting type, 18 patients with anorexia nervosa-binge-purging type and 20 patients with bulimia nervosa reported their momentary affect at nine random times a day during a one week period using a hand-held computer. Affective variability was calculated as the within-person standard deviation of valence and activation over time. Results indicate that patients displaying greater variability in activation and using selective serotonin reuptake inhibitors have a higher probability to engage in lifetime NSSI after adjustment for depression and borderline personality disorder. Neither variability of valence nor mean level of valence and activation had any predictive association with engaging in NSSI. It is suggested that the treatment of NSSI should focus on affect stabilization rather than reducing negative affect.

  10. Allometric scaling and prediction of concentration-time profiles of coagulation factors in humans from animals.

    PubMed

    Mahmood, Iftekhar

    2013-09-01

    Allometric scaling is a useful tool in early drug development and can be used for the prediction of human pharmacokinetic (PK) parameters from animal PK parameters. The main objective of this work was to predict concentration-time profiles of coagulation factors in humans in a multi-compartment system using animal PK parameters. The prediction of concentration-time profiles in humans in a multi-compartment system was based on the predicted values of clearance and volumes of distribution (V(c), V(ss) and V(β)) from animals. Five coagulation factors from the literature were chosen that were described by two-compartment model in both humans and animals. Clearance and volumes of distribution from animals were allometrically scaled to humans and then were used to predict concentration-time profiles in humans. The predicted concentration-time profile for a given coagulation factor was accurate for most of the time points. Percent prediction error range varied across coagulation factors. The prediction error >50% was observed either at 1 or a maximum of two time points for a given drug. The study indicated that the allometric scaling can be useful in the prediction of concentration-time profiles of coagulation factors in humans from animals and may be helpful in designing a first-in-human study.

  11. Solar geomagnetic activity prediction using the fractal analysis and neural network

    NASA Astrophysics Data System (ADS)

    Ouadfeul, Sid-Ali; Aliouane, Leila

    2010-05-01

    The main goal of this work is to predict the Solar geomagnetic field activity using the neural network combined with the fractal analysis, first a multilayer perceptron neural network model is proposed to predict the future Solar geomagnetic field, the input of this machine is the geographic Coordinates and the time .The output is the three geomagnetic field components and the total field intensity recorded by the Orsted Satellite Mission. Holder Exponents of the measured geomagnetic field components and the total field intensity are calculated using the continuous wavelet transform. The Set of Holder exponents is used to train a Kohonen's Self-Organizing Map (SOM) neural machine which will become a classifier of the solar magnetic activity nature. The SOM neural network machine is used to predict the future solar magnetic storms, in this step the input is the calculated set of the Holder exponents of the predicted geomagnetic field components and the total field intensity. Obtained results show that the proposed technique is a powerful tool and can enhance the solar magnetic field activity prediction. Keywords: Solar geomagnetic activity, neural network, prediction, Orsted, Holder Exponents, Solar magnetic storms.

  12. Mackey-Glass noisy chaotic time series prediction by a swarm-optimized neural network

    NASA Astrophysics Data System (ADS)

    López-Caraballo, C. H.; Salfate, I.; Lazzús, J. A.; Rojas, P.; Rivera, M.; Palma-Chilla, L.

    2016-05-01

    In this study, an artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass noiseless chaotic time series in the short-term and long-term prediction. The performance prediction is evaluated and compared with similar work in the literature, particularly for the long-term forecast. Also, we present properties of the dynamical system via the study of chaotic behaviour obtained from the time series prediction. Then, this standard hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions that also allowed us compute uncertainties of predictions for noisy Mackey-Glass chaotic time series. We study the impact of noise for three cases with a white noise level (σ N ) contribution of 0.01, 0.05 and 0.1.

  13. Real Time Volcanic Cloud Products and Predictions for Aviation Alerts

    NASA Technical Reports Server (NTRS)

    Krotkov, Nickolay A.; Habib, Shahid; da Silva, Arlindo; Hughes, Eric; Yang, Kai; Brentzel, Kelvin; Seftor, Colin; Li, Jason Y.; Schneider, David; Guffanti, Marianne; Hoffman, Robert L.; Myers, Tim; Tamminen, Johanna; Hassinen, Seppo

    2014-01-01

    Volcanic eruptions can inject significant amounts of sulfur dioxide (SO2) and volcanic ash into the atmosphere, posing a substantial risk to aviation safety. Ingesting near-real time and Direct Readout satellite volcanic cloud data is vital for improving reliability of volcanic ash forecasts and mitigating the effects of volcanic eruptions on aviation and the economy. NASA volcanic products from the Ozone Monitoring Insrument (OMI) aboard the Aura satellite have been incorporated into Decision Support Systems of many operational agencies. With the Aura mission approaching its 10th anniversary, there is an urgent need to replace OMI data with those from the next generation operational NASA/NOAA Suomi National Polar Partnership (SNPP) satellite. The data provided from these instruments are being incorporated into forecasting models to provide quantitative ash forecasts for air traffic management. This study demonstrates the feasibility of the volcanic near-real time and Direct Readout data products from the new Ozone Monitoring and Profiling Suite (OMPS) ultraviolet sensor onboard SNPP for monitoring and forecasting volcanic clouds. The transition of NASA data production to our operational partners is outlined. Satellite observations are used to constrain volcanic cloud simulations and improve estimates of eruption parameters, resulting in more accurate forecasts. This is demonstrated for the 2012 eruption of Copahue. Volcanic eruptions are modeled using the Goddard Earth Observing System, Version 5 (GEOS-5) and the Goddard Chemistry Aerosol and Radiation Transport (GOCART) model. A hindcast of the disruptive eruption from Iceland's Eyjafjallajokull is used to estimate aviation re-routing costs using Metron Aviation's ATM Tools.

  14. Real-time index for predicting successful golf putting motion using multichannel EEG.

    PubMed

    Muangjaroen, Piyachat; Wongsawat, Yodchanan

    2012-01-01

    A skill in goal-directed sport performance is an ability involving with many factors of both external and internal concernment. External factors are still developed while internal factors are challenged topic to understand for improving the performance. Internal concernment is explained an effective performance as estimation, solving strategy, planning and decision on the brain. These conjunctions are relevant to somatosensory information, focus attention and fine motor control of cortical activity. Five skilled right-handed golfers were recruited to be subjected of studying the criteria on how to predict golf putt success. Each of their putts was calculated in power spectral analysis by comparing to the pre-movement period. Successful and unsuccessful putt were classified by focusing on the frontal-midline(Fz), parietal-midline(Pz), central midline(Cz), left central(C3) and right central(C4) which supported by few consistency studies that they are related to a primary sensory motor area, focus attention and working memory processing. Results were shown that high alpha power on C4, theta power on Fz, theta power and high alpha power on Pz can be calculated to use as index of predicting golf putt success. Real-time monitoring system with friendly GUI was proposed in this study as promising preliminary study. Expected goal in the future is to apply this real-time golf putting prediction system into a biofeedback system to increase the golf putting's accuracy. However, it still needs more subjects to increase credibility and accuracy of the prediction.

  15. Multipoint measurements of substorm timing and activations

    NASA Astrophysics Data System (ADS)

    Zuyin, Pu; Cao, X.; Zhang, H.; Ma, Z. W.; Mishin, M. V.; Kubyshkina, M. V.; Pulkkinen, T.; Reeves, G. D.; Escoubet, C. Philippe

    Substorm timing and activations are studied based on Double Star TC1, Cluster, Polar, IM- AGE, LANL satellites and ground-based Pi2 measurements. Substorm expansion onset is found to begin in the near-Earth tail around X= -(8-9) Re, then progresses both earthward and tailward. About 8-10 minutes before aurora breakup, Cluster measured an earthward flow associated with plasma sheet thinning. A couple of minutes after the breakup, TC1 first detects plasma sheet expansion and then LANL satellites near the midnight measure energetic electron injections, or vise versus. About 20 minutes (or more) later, Cluster and Polar observe plasma sheet expansion successively. Of interest are also the following findings. Auroral bulge is found to quickly broaden and expand poleward when the open magnetic flux of the polar cap is rapidly dissipated, indicating the role of tail lobe reconnection of open field lines in the development of the expansion phase. In addition, poleward expansion of auroral bulges and tailward progression of substorm expansion are shown to be closely related. An initial dipolarization in the near-Earth eventually evolve to enable disruption of the cross-tail current in a wide range of the magnetotail, until the open magnetic flux of the polar cap reaches its minimum. Acknowledgements This work is supported by the NSFC Grants 40390152 and 40536030 and Chinese Key Research Project Grant 2006CB806300. The authors acknowledge all PIs of instruments onboard Double Star and Cluster spacecraft. We also appreciate the useful discussions with R. L. McPherron and A. T. Y. Lui.

  16. Neural Underpinnings of Impaired Predictive Motor Timing in Children with Developmental Coordination Disorder

    ERIC Educational Resources Information Center

    Debrabant, Julie; Gheysen, Freja; Caeyenberghs, Karen; Van Waelvelde, Hilde; Vingerhoets, Guy

    2013-01-01

    A dysfunction in predictive motor timing is put forward to underlie DCD-related motor problems. Predictive timing allows for the pre-selection of motor programmes (except "program" in computers) in order to decrease processing load and facilitate reactions. Using functional magnetic resonance imaging (fMRI), this study investigated the neural…

  17. Spontaneous brain activity predicts learning ability of foreign sounds.

    PubMed

    Ventura-Campos, Noelia; Sanjuán, Ana; González, Julio; Palomar-García, María-Ángeles; Rodríguez-Pujadas, Aina; Sebastián-Gallés, Núria; Deco, Gustavo; Ávila, César

    2013-05-29

    Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.

  18. Predicting Single-Neuron Activity in Locally Connected Networks

    PubMed Central

    Azhar, Feraz; Anderson, William S.

    2014-01-01

    The characterization of coordinated activity in neuronal populations has received renewed interest in the light of advancing experimental techniques that allow recordings from multiple units simultaneously. Across both in vitro and in vivo preparations, nearby neurons show coordinated responses when spontaneously active and when subject to external stimuli. Recent work (Truccolo, Hochberg, & Donoghue, 2010) has connected these coordinated responses to behavior, showing that small ensembles of neurons in arm-related areas of sensorimotor cortex can reliably predict single-neuron spikes in behaving monkeys and humans. We investigate this phenomenon using an analogous point process model, showing that in the case of a computational model of cortex responding to random background inputs, one is similarly able to predict the future state of a single neuron by considering its own spiking history, together with the spiking histories of randomly sampled ensembles of nearby neurons. This model exhibits realistic cortical architecture and displays bursting episodes in the two distinct connectivity schemes studied. We conjecture that the baseline predictability we find in these instances is characteristic of locally connected networks more broadly considered. PMID:22845824

  19. Cortical activity predicts good variation in human motor output.

    PubMed

    Babikian, Sarine; Kanso, Eva; Kutch, Jason J

    2017-02-04

    Human movement patterns have been shown to be particularly variable if many combinations of activity in different muscles all achieve the same task goal (i.e., are goal-equivalent). The nervous system appears to automatically vary its output among goal-equivalent combinations of muscle activity to minimize muscle fatigue or distribute tissue loading, but the neural mechanism of this "good" variation is unknown. Here we use a bimanual finger task, electroencephalography (EEG), and machine learning to determine if cortical signals can predict goal-equivalent variation in finger force output. 18 healthy participants applied left and right index finger forces to repeatedly perform a task that involved matching a total (sum of right and left) finger force. As in previous studies, we observed significantly more variability in goal-equivalent muscle activity across task repetitions compared to variability in muscle activity that would not achieve the goal: participants achieved the task in some repetitions with more right finger force and less left finger force (right > left) and in other repetitions with less right finger force and more left finger force (left > right). We found that EEG signals from the 500 milliseconds (ms) prior to each task repetition could make a significant prediction of which repetitions would have right > left and which would have left > right. We also found that cortical maps of sites contributing to the prediction contain both motor and pre-motor representation in the appropriate hemisphere. Thus, goal-equivalent variation in motor output may be implemented at a cortical level.

  20. A divide-and-conquer method for space-time series prediction

    NASA Astrophysics Data System (ADS)

    Deng, Min; Yang, Wentao; Liu, Qiliang; Zhang, Yunfei

    2017-01-01

    Space-time series can be partitioned into space-time smooth and space-time rough, which represent different scale characteristics. However, most existing methods for space-time series prediction directly address space-time series as a whole and do not consider the interaction between space-time smooth and space-time rough in the process of prediction. This will possibly affect the accuracy of space-time series prediction, because the interaction between these two components (i.e., space-time smooth and space-time rough) may cause one of them as dominant component, thus weakening the behavior of the other. Therefore, a divide-and-conquer method for space-time prediction is proposed in this paper. First, the observational fine-grained data are decomposed into two components: coarse-grained data and the residual terms of fine-grained data. These two components are then modeled, respectively. Finally, the predicted values of the fine-grained data are obtained by integrating the predicted values of the coarse-grained data with the residual terms. The experimental results of two groups of different space-time series demonstrated the effectiveness of the divide-and-conquer method.

  1. Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils.

    PubMed

    Daynac, Mathieu; Cortes-Cabrera, Alvaro; Prieto, Jose M

    2015-01-01

    Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity. Methods. The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium perfringens as measured by standardised disk diffusion assays. Results. ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens. Conclusions. ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM.

  2. Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils

    PubMed Central

    Daynac, Mathieu; Cortes-Cabrera, Alvaro; Prieto, Jose M.

    2015-01-01

    Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity. Methods. The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium perfringens as measured by standardised disk diffusion assays. Results. ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens. Conclusions. ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM. PMID:26457111

  3. Spontaneous Fluctuations in Sensory Processing Predict Within-Subject Reaction Time Variability

    PubMed Central

    Ribeiro, Maria J.; Paiva, Joana S.; Castelo-Branco, Miguel

    2016-01-01

    When engaged in a repetitive task our performance fluctuates from trial-to-trial. In particular, inter-trial reaction time variability has been the subject of considerable research. It has been claimed to be a strong biomarker of attention deficits, increases with frontal dysfunction, and predicts age-related cognitive decline. Thus, rather than being just a consequence of noise in the system, it appears to be under the control of a mechanism that breaks down under certain pathological conditions. Although the underlying mechanism is still an open question, consensual hypotheses are emerging regarding the neural correlates of reaction time inter-trial intra-individual variability. Sensory processing, in particular, has been shown to covary with reaction time, yet the spatio-temporal profile of the moment-to-moment variability in sensory processing is still poorly characterized. The goal of this study was to characterize the intra-individual variability in the time course of single-trial visual evoked potentials and its relationship with inter-trial reaction time variability. For this, we chose to take advantage of the high temporal resolution of the electroencephalogram (EEG) acquired while participants were engaged in a 2-choice reaction time task. We studied the link between single trial event-related potentials (ERPs) and reaction time using two different analyses: (1) time point by time point correlation analyses thereby identifying time windows of interest; and (2) correlation analyses between single trial measures of peak latency and amplitude and reaction time. To improve extraction of single trial ERP measures related with activation of the visual cortex, we used an independent component analysis (ICA) procedure. Our ERP analysis revealed a relationship between the N1 visual evoked potential and reaction time. The earliest time point presenting a significant correlation of its respective amplitude with reaction time occurred 175 ms after stimulus onset

  4. Spontaneous Fluctuations in Sensory Processing Predict Within-Subject Reaction Time Variability.

    PubMed

    Ribeiro, Maria J; Paiva, Joana S; Castelo-Branco, Miguel

    2016-01-01

    When engaged in a repetitive task our performance fluctuates from trial-to-trial. In particular, inter-trial reaction time variability has been the subject of considerable research. It has been claimed to be a strong biomarker of attention deficits, increases with frontal dysfunction, and predicts age-related cognitive decline. Thus, rather than being just a consequence of noise in the system, it appears to be under the control of a mechanism that breaks down under certain pathological conditions. Although the underlying mechanism is still an open question, consensual hypotheses are emerging regarding the neural correlates of reaction time inter-trial intra-individual variability. Sensory processing, in particular, has been shown to covary with reaction time, yet the spatio-temporal profile of the moment-to-moment variability in sensory processing is still poorly characterized. The goal of this study was to characterize the intra-individual variability in the time course of single-trial visual evoked potentials and its relationship with inter-trial reaction time variability. For this, we chose to take advantage of the high temporal resolution of the electroencephalogram (EEG) acquired while participants were engaged in a 2-choice reaction time task. We studied the link between single trial event-related potentials (ERPs) and reaction time using two different analyses: (1) time point by time point correlation analyses thereby identifying time windows of interest; and (2) correlation analyses between single trial measures of peak latency and amplitude and reaction time. To improve extraction of single trial ERP measures related with activation of the visual cortex, we used an independent component analysis (ICA) procedure. Our ERP analysis revealed a relationship between the N1 visual evoked potential and reaction time. The earliest time point presenting a significant correlation of its respective amplitude with reaction time occurred 175 ms after stimulus onset

  5. Motor cortex activity predicts response alternation during sensorimotor decisions

    PubMed Central

    Pape, Anna-Antonia; Siegel, Markus

    2016-01-01

    Our actions are constantly guided by decisions based on sensory information. The motor cortex is traditionally viewed as the final output stage in this process, merely executing motor responses based on these decisions. However, it is not clear if, beyond this role, the motor cortex itself impacts response selection. Here, we report activity fluctuations over motor cortex measured using MEG, which are unrelated to choice content and predict responses to a visuomotor task seconds before decisions are made. These fluctuations are strongly influenced by the previous trial's response and predict a tendency to switch between response alternatives for consecutive decisions. This alternation behaviour depends on the size of neural signals still present from the previous response. Our results uncover a response-alternation bias in sensorimotor decision making. Furthermore, they suggest that motor cortex is more than an output stage and instead shapes response selection during sensorimotor decision making. PMID:27713396

  6. Neural activity predicts attitude change in cognitive dissonance.

    PubMed

    van Veen, Vincent; Krug, Marie K; Schooler, Jonathan W; Carter, Cameron S

    2009-11-01

    When our actions conflict with our prior attitudes, we often change our attitudes to be more consistent with our actions. This phenomenon, known as cognitive dissonance, is considered to be one of the most influential theories in psychology. However, the neural basis of this phenomenon is unknown. Using a Solomon four-group design, we scanned participants with functional MRI while they argued that the uncomfortable scanner environment was nevertheless a pleasant experience. We found that cognitive dissonance engaged the dorsal anterior cingulate cortex and anterior insula; furthermore, we found that the activation of these regions tightly predicted participants' subsequent attitude change. These effects were not observed in a control group. Our findings elucidate the neural representation of cognitive dissonance, and support the role of the anterior cingulate cortex in detecting cognitive conflict and the neural prediction of attitude change.

  7. Operational Precipitation prediction in Support of Real-Time Flash Flood Prediction and Reservoir Management

    NASA Astrophysics Data System (ADS)

    Georgakakos, K. P.

    2006-05-01

    The presentation will outline the implementation and performance evaluation of a number of national and international projects pertaining to operational precipitation estimation and prediction in the context of hydrologic warning systems and reservoir management support. In all cases, uncertainty measures of the estimates and predictions are an integral part of the precipitation models. Outstanding research issues whose resolution is likely to lead to improvements in the operational environment are presented. The presentation draws from the experience of the Hydrologic Research Center (http://www.hrc-lab.org) prototype implementation projects at the Panama Canal, Central America, Northern California, and South-Central US. References: Carpenter, T.M, and K.P. Georgakakos, "Discretization Scale Dependencies of the Ensemble Flow Range versus Catchment Area Relationship in Distributed Hydrologic Modeling," Journal of Hydrology, 2006, in press. Carpenter, T.M., and K.P. Georgakakos, "Impacts of Parametric and Radar Rainfall Uncertainty on the Ensemble Streamflow Simulations of a Distributed Hydrologic Model," Journal of Hydrology, 298, 202-221, 2004. Georgakakos, K.P., Graham, N.E., Carpenter, T.M., Georgakakos, A.P., and H. Yao, "Integrating Climate- Hydrology Forecasts and Multi-Objective Reservoir Management in Northern California," EOS, 86(12), 122,127, 2005. Georgakakos, K.P., and J.A. Sperfslage, "Operational Rainfall and Flow Forecasting for the Panama Canal Watershed," in The Rio Chagres: A Multidisciplinary Profile of a Tropical Watershed, R.S. Harmon, ed., Kluwer Academic Publishers, The Netherlands, Chapter 16, 323-334, 2005. Georgakakos, K. P., "Analytical results for operational flash flood guidance," Journal of Hydrology, doi:10.1016/j.jhydrol.2005.05.009, 2005.

  8. Predicting the activation states of the muscles governing upper esophageal sphincter relaxation and opening

    PubMed Central

    Jones, Corinne A.; Hammer, Michael J.; Cock, Charles; Dinning, Philip; Wiklendt, Lukasz; Costa, Marcello; McCulloch, Timothy M.

    2016-01-01

    The swallowing muscles that influence upper esophageal sphincter (UES) opening are centrally controlled and modulated by sensory information. Activation and deactivation of neural inputs to these muscles, including the intrinsic cricopharyngeus (CP) and extrinsic submental (SM) muscles, results in their mechanical activation or deactivation, which changes the diameter of the lumen, alters the intraluminal pressure, and ultimately reduces or promotes flow of content. By measuring the changes in diameter, using intraluminal impedance, and the concurrent changes in intraluminal pressure, it is possible to determine when the muscles are passively or actively relaxing or contracting. From these “mechanical states” of the muscle, the neural inputs driving the specific motor behaviors of the UES can be inferred. In this study we compared predictions of UES mechanical states directly with the activity measured by electromyography (EMG). In eight subjects, pharyngeal pressure and impedance were recorded in parallel with CP- and SM-EMG activity. UES pressure and impedance swallow profiles correlated with the CP-EMG and SM-EMG recordings, respectively. Eight UES muscle states were determined by using the gradient of pressure and impedance with respect to time. Guided by the level and gradient change of EMG activity, mechanical states successfully predicted the activity of the CP muscle and SM muscle independently. Mechanical state predictions revealed patterns consistent with the known neural inputs activating the different muscles during swallowing. Derivation of “activation state” maps may allow better physiological and pathophysiological interpretations of UES function. PMID:26767985

  9. Baseline Brain Activity Predicts Response to Neuromodulatory Pain Treatment

    PubMed Central

    Jensen, Mark P.; Sherlin, Leslie H.; Fregni, Felipe; Gianas, Ann; Howe, Jon D.; Hakimian, Shahin

    2015-01-01

    Objectives The objective of this study was to examine the associations between baseline electroencephalogram (EEG)-assessed brain oscillations and subsequent response to four neuromodulatory treatments. Based on available research, we hypothesized that baseline theta oscillations would prospectively predict response to hypnotic analgesia. Analyses involving other oscillations and the other treatments (meditation, neurofeedback, and both active and sham transcranial direct current stimulation) were viewed as exploratory, given the lack of previous research examining brain oscillations as predictors of response to these other treatments. Design Randomized controlled study of single sessions of four neuromodulatory pain treatments and a control procedure. Methods Thirty individuals with spinal cord injury and chronic pain had their EEG recorded before each session of four active treatments (hypnosis, meditation, EEG biofeedback, transcranial direct current stimulation) and a control procedure (sham transcranial direct stimulation). Results As hypothesized, more presession theta power was associated with greater response to hypnotic analgesia. In exploratory analyses, we found that less baseline alpha power predicted pain reduction with meditation. Conclusions The findings support the idea that different patients respond to different pain treatments and that between-person treatment response differences are related to brain states as measured by EEG. The results have implications for the possibility of enhancing pain treatment response by either 1) better patient/treatment matching or 2) influencing brain activity before treatment is initiated in order to prepare patients to respond. Research is needed to replicate and confirm the findings in additional samples of individuals with chronic pain. PMID:25287554

  10. Music-induced emotions can be predicted from a combination of brain activity and acoustic features.

    PubMed

    Daly, Ian; Williams, Duncan; Hallowell, James; Hwang, Faustina; Kirke, Alexis; Malik, Asad; Weaver, James; Miranda, Eduardo; Nasuto, Slawomir J

    2015-12-01

    It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,p<0.001. This regression fit suggests that over 20% of the variance of the participant's music induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01).

  11. Food reward without a timing component does not alter the timing of activity under positive energy balance.

    PubMed

    van der Vinne, V; Akkerman, J; Lanting, G D; Riede, S J; Hut, R A

    2015-09-24

    Circadian clocks drive daily rhythms in physiology and behavior which allow organisms to anticipate predictable daily changes in the environment. In most mammals, circadian rhythms result in nocturnal activity patterns although plasticity of the circadian system allows activity patterns to shift to different times of day. Such plasticity is seen when food access is restricted to a few hours during the resting (light) phase resulting in food anticipatory activity (FAA) in the hours preceding food availability. The mechanisms underlying FAA are unknown but data suggest the involvement of the reward system and homeostatic regulation of metabolism. We previously demonstrated the isolated effect of metabolism by inducing diurnality in response to energetic challenges. Here the importance of reward timing in inducing daytime activity is assessed. The daily activity distribution of mice earning palatable chocolate at their preferred time by working in a running wheel was compared with that of mice receiving a timed palatable meal at noon. Mice working for chocolate (WFC) without being energetically challenged increased their total daily activity but this did not result in a shift to diurnality. Providing a chocolate meal at noon each day increased daytime activity, identifying food timing as a factor capable of altering the daily distribution of activity and rest. These results show that timing of food reward and energetic challenges are both independently sufficient to induce diurnality in nocturnal mammals. FAA observed following timed food restriction is likely the result of an additive effect of distinct regulatory pathways activated by energetic challenges and food reward.

  12. Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

    EPA Science Inventory

    Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

  13. Solar Flare Predictions Using Time Series of SDO/HMI Observations and Machine Learning Methods

    NASA Astrophysics Data System (ADS)

    Ilonidis, Stathis; Bobra, Monica; Couvidat, Sebastien

    2015-08-01

    Solar active regions are dynamic systems that can rapidly evolve in time and produce flare eruptions. The temporal evolution of an active region can provide important information about its potential to produce major flares. In this study, we build a flare forecasting model using supervised machine learning methods and time series of SDO/HMI data for all the flaring regions with magnitude M1.0 or higher that have been observed with HMI and several thousand non-flaring regions. We define and compute hundreds of features that characterize the temporal evolution of physical properties related to the size, non-potentiality, and complexity of the active region, as well as its flaring history, for several days before the flare eruption. Using these features, we implement and test the performance of several machine learning algorithms, including support vector machines, neural networks, decision trees, discriminant analysis, and others. We also apply feature selection algorithms that aim to discard features with low predictive power and improve the performance of the machine learning methods. Our results show that support vector machines provide the best forecasts for the next 24 hours, achieving a True Skill Statistic of 0.923, an accuracy of 0.985, and a Heidke skill score of 0.861, which improve the scores obtained by Bobra and Couvidat (2015). The results of this study contribute to the development of a more reliable and fully automated data-driven flare forecasting system.

  14. LSD-induced entropic brain activity predicts subsequent personality change.

    PubMed

    Lebedev, A V; Kaelen, M; Lövdén, M; Nilsson, J; Feilding, A; Nutt, D J; Carhart-Harris, R L

    2016-09-01

    Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality. Hum Brain Mapp 37:3203-3213, 2016. © 2016 Wiley Periodicals, Inc.

  15. A prediction of accelerator-produced activation products.

    PubMed

    Culp, Todd

    2007-02-01

    The operational radiation protection issues associated with the Z-Machine accelerator located at Sandia National Laboratories are large: a variety of materials can be placed into the machine; these materials can be subjected to a variety of nuclear reactions, producing a variety of activation products. Without full understanding of the most likely contaminants, a realistic identification of the radiological hazards and appropriate controls is not possible. This paper presents a process developed to provide a realistic prediction of the accelerator-produced radionuclides of interest.

  16. Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

    NASA Astrophysics Data System (ADS)

    Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

    2008-11-01

    The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

  17. Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

    DOE PAGES

    Petters, M. D.; Kreidenweis, S. M.; Ziemann, P. J.

    2016-01-19

    A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Köhler theory to predict themore » effective hygroscopicity parameter, kappa. However, in our approach we also account for liquid–liquid phase boundaries to simulate phase-limited activation behavior. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of 2. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. The model can be incorporated into scale-bridging test beds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger-scale models.« less

  18. Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

    NASA Astrophysics Data System (ADS)

    Petters, M. D.; Kreidenweis, S. M.; Ziemann, P. J.

    2016-01-01

    A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Köhler theory to predict the effective hygroscopicity parameter, kappa. However, in our approach we also account for liquid-liquid phase boundaries to simulate phase-limited activation behavior. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of 2. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. The model can be incorporated into scale-bridging test beds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger-scale models.

  19. Real-time Seismic Amplitude Measurement (RSAM): a volcano monitoring and prediction tool

    USGS Publications Warehouse

    Endo, E.T.; Murray, T.

    1991-01-01

    Seismicity is one of the most commonly monitored phenomena used to determine the state of a volcano and for the prediction of volcanic eruptions. Although several real-time earthquake-detection and data acquisition systems exist, few continuously measure seismic amplitude in circumstances where individual events are difficult to recognize or where volcanic tremor is prevalent. Analog seismic records provide a quick visual overview of activity; however, continuous rapid quantitative analysis to define the intensity of seismic activity for the purpose of predicing volcanic eruptions is not always possible because of clipping that results from the limited dynamic range of analog recorders. At the Cascades Volcano Observatory, an inexpensive 8-bit analog-to-digital system controlled by a laptop computer is used to provide 1-min average-amplitude information from eight telemetered seismic stations. The absolute voltage level for each station is digitized, averaged, and appended in near real-time to a data file on a multiuser computer system. Raw realtime seismic amplitude measurement (RSAM) data or transformed RSAM data are then plotted on a common time base with other available volcano-monitoring information such as tilt. Changes in earthquake activity associated with dome-building episodes, weather, and instrumental difficulties are recognized as distinct patterns in the RSAM data set. RSAM data for domebuilding episodes gradually develop into exponential increases that terminate just before the time of magma extrusion. Mount St. Helens crater earthquakes show up as isolated spikes on amplitude plots for crater seismic stations but seldom for more distant stations. Weather-related noise shows up as low-level, long-term disturbances on all seismic stations, regardless of distance from the volcano. Implemented in mid-1985, the RSAM system has proved valuable in providing up-to-date information on seismic activity for three Mount St. Helens eruptive episodes from 1985 to

  20. Anticipatory eye movements evoked after active following versus passive observation of a predictable motion stimulus.

    PubMed

    Burke, M R; Barnes, G R

    2008-12-15

    We used passive and active following of a predictable smooth pursuit stimulus in order to establish if predictive eye movement responses are equivalent under both passive and active conditions. The smooth pursuit stimulus was presented in pairs that were either 'predictable' in which both presentations were matched in timing and velocity, or 'randomized' in which each presentation in the pair was varied in both timing and velocity. A visual cue signaled the type of response required from the subject; a green cue indicated the subject should follow both the target presentations (Go-Go), a pink cue indicated that the subject should passively observe the 1st target and follow the 2nd target (NoGo-Go), and finally a green cue with a black cross revealed a randomized (Rnd) trial in which the subject should follow both presentations. The results revealed better prediction in the Go-Go trials than in the NoGo-Go trials, as indicated by higher anticipatory velocity and earlier eye movement onset (latency). We conclude that velocity and timing information stored from passive observation of a moving target is diminished when compared to active following of the target. This study has significant consequences for understanding how visuomotor memory is generated, stored and subsequently released from short-term memory.

  1. Studying the time scale dependence of environmental variables predictability using fractal analysis.

    PubMed

    Yuval; Broday, David M

    2010-06-15

    Prediction of meteorological and air quality variables motivates a lot of research in the atmospheric sciences and exposure assessment communities. An interesting related issue regards the relative predictive power that can be expected at different time scales, and whether it vanishes altogether at certain ranges. An improved understanding of our predictive powers enables better environmental management and more efficient decision making processes. Fractal analysis is commonly used to characterize the self-affinity of time series. This work introduces the Continuous Wavelet Transform (CWT) fractal analysis method as a tool for assessing environmental time series predictability. The high temporal scale resolution of the CWT enables detailed information about the Hurst parameter, a common temporal fractality measure, and thus about time scale variations in predictability. We analyzed a few years records of half-hourly air pollution and meteorological time series from which the trivial seasonal and daily cycles were removed. We encountered a general trend of decreasing Hurst values from about 1.4 (good autocorrelation and predictability), in the sub-daily time scale to 0.5 (which implies complete randomness) in the monthly to seasonal scales. The air pollutants predictability follows that of the meteorological variables in the short time scales but is better at longer scales.

  2. Long-Range Solar Activity Predictions: A Reprieve from Cycle #24's Activity

    NASA Technical Reports Server (NTRS)

    Richon, K.; Schatten, K.

    2003-01-01

    We discuss the field of long-range solar activity predictions and provide an outlook into future solar activity. Orbital predictions for satellites in Low Earth Orbit (LEO) depend strongly on exospheric densities. Solar activity forecasting is important in this regard, as the solar ultra-violet (UV) and extreme ultraviolet (EUV) radiations inflate the upper atmospheric layers of the Earth, forming the exosphere in which satellites orbit. Rather than concentrate on statistical, or numerical methods, we utilize a class of techniques (precursor methods) which is founded in physical theory. The geomagnetic precursor method was originally developed by the Russian geophysicist, Ohl, using geomagnetic observations to predict future solar activity. It was later extended to solar observations, and placed within the context of physical theory, namely the workings of the Sun s Babcock dynamo. We later expanded the prediction methods with a SOlar Dynamo Amplitude (SODA) index. The SODA index is a measure of the buried solar magnetic flux, using toroidal and poloidal field components. It allows one to predict future solar activity during any phase of the solar cycle, whereas previously, one was restricted to making predictions only at solar minimum. We are encouraged that solar cycle #23's behavior fell closely along our predicted curve, peaking near 192, comparable to the Schatten, Myers and Sofia (1996) forecast of 182+/-30. Cycle #23 extends from 1996 through approximately 2006 or 2007, with cycle #24 starting thereafter. We discuss the current forecast of solar cycle #24, (2006-2016), with a predicted smoothed F10.7 radio flux of 142+/-28 (1-sigma errors). This, we believe, represents a reprieve, in terms of reduced fuel costs, etc., for new satellites to be launched or old satellites (requiring reboosting) which have been placed in LEO. By monitoring the Sun s most deeply rooted magnetic fields; long-range solar activity can be predicted. Although a degree of uncertainty

  3. Time and Frequency Transfer Activities at NIST

    DTIC Science & Technology

    2008-12-01

    Metrologia (SIM) Time Network The Sistema Interamericano de Metrologia (SIM) consists of national metrology institutes (NMIs) located in the 34...2003, “Time Transfer to TAI Using Geodetic Receivers,” Metrologia , 40, 184-188. [5] K. M. Larson, J. Levine, L. M. Nelson, and T. E. Parker, 2000... Metrologia , 40, 270-288. [7] G. Petit and Z. Jiang, 2008, “Precise point positioning for TAI computation,” International Journal of Navigation

  4. Working Memory-Related Neural Activity Predicts Future Smoking Relapse

    PubMed Central

    Loughead, James; Wileyto, E Paul; Ruparel, Kosha; Falcone, Mary; Hopson, Ryan; Gur, Ruben; Lerman, Caryn

    2015-01-01

    Brief abstinence from smoking impairs cognition, particularly executive function, and this has a role in relapse to smoking. This study examined whether working memory-related brain activity predicts subsequent smoking relapse above and beyond standard clinical and behavioral measures. Eighty treatment-seeking smokers completed two functional magnetic resonance imaging sessions (smoking satiety vs 24 h abstinence challenge) during performance of a visual N-back task. Brief counseling and a short-term quit attempt followed. Relapse during the first 7 days was biochemically confirmed by the presence of the nicotine metabolite cotinine. Mean percent blood oxygen level-dependent (BOLD) signal change was extracted from a priori regions of interest: bilateral dorsolateral prefrontal cortex (DLPFC), medial frontal/cingulate gyrus, posterior cingulate cortex (PCC), and ventromedial prefrontal cortex. Signal from these brain regions and additional clinical measures were used to model outcome status, which was then validated with resampling techniques. Relapse to smoking was predicted by increased withdrawal symptoms, decreased left DLPFC and increased PCC BOLD percent signal change (abstinence vs smoking satiety). Receiver operating characteristic analysis demonstrated 81% area under the curve using these predictors, a significant improvement over the model with clinical variables only. The combination of abstinence-induced decreases in left DLPFC activation and reduced suppression of PCC may be a prognostic marker for poor outcome, specifically early smoking relapse. PMID:25469682

  5. Predictive Analysis of Landslide Activity Using Remote Sensing Data

    NASA Astrophysics Data System (ADS)

    Markuzon, N.; Regan, J.; Slesnick, C.

    2012-12-01

    Landslides are historically one of the most damaging geohazard phenomena in terms of death tolls and socio-economic losses. Therefore, understanding the underlying causes of landslides and how environmental phenomena affect their frequency and severity is of critical importance. Of specific importance for mitigating future damage is increasing our understanding of how climate change will affect landslide severity, occurrence rates, and damage. We are developing data driven models aimed at predicting landslide activity. The models learn multi-dimensional weather and geophysical patterns associated with historical landslides and estimate location-dependent probabilities for landslides under current or future weather and geophysical conditions. Our approach uses machine learning algorithms capable of determining non-linear associations between dependent variables and landslide occurrence without requiring detailed knowledge of geomorphology. Our primary goal in year one of the project is to evaluate the predictive capabilities of data mining models in application to landslide activity, and to analyze if the approach will discover previously unknown variables and/or relationships important to landslide occurrence, frequency or severity. The models include remote sensing and ground-based data, including weather, landcover, slope, elevation and drainage information as well as urbanization data. The historical landslide dataset we used to build our preliminary models was compiled from City of Seattle landslide files, United States Geological Survey reports, newspaper articles, and a verified subset of the Seattle Landslide Database that consists of all reported landslides within Seattle, WA, between 1948 and 1999. Most of the landslides analyzed to-date are shallow. Using statistical analysis and unsupervised clustering methods we have thus far identified subsets of weather conditions that lead to a significantly higher landslide probability, and have developed

  6. Physical terms and leisure time activities

    NASA Astrophysics Data System (ADS)

    Valovičová, Ľubomíra; Siptáková, Mária; ŠtubÅa, Martin

    2017-01-01

    People have to educate not only in school but also outside it. One approach to acquire new knowledge are leisure activities such as hobby groups or camps. Leisure activities, more and more seem to be the appropriate form for informal learning of physics concepts. Within leisure activities pupils have the possibility to acquire new concepts in unusual and interesting way. It is possible to inspire their intrinsic motivation on the matter or the phenomenon which is the aim of all teachers. This article deals with the description of and insights on acquisition of the concept of uniform and non-uniform rectilinear movement during a physics camp where pupils had the opportunity to use modern technologies which are despite of modernization of education still unconventional teaching methods in our schools.

  7. Predicting Atlantic seasonal hurricane activity using outgoing longwave radiation over Africa

    NASA Astrophysics Data System (ADS)

    Karnauskas, Kristopher B.; Li, Laifang

    2016-07-01

    Seasonal hurricane activity is a function of the amount of initial disturbances (e.g., easterly waves) and the background environment in which they develop into tropical storms (i.e., the main development region). Focusing on the former, a set of indices based solely upon the meridional structure of satellite-derived outgoing longwave radiation (OLR) over the African continent are shown to be capable of predicting Atlantic seasonal hurricane activity with very high rates of success. Predictions of named storms based on the July OLR field and trained only on the time period prior to the year being predicted yield a success rate of 87%, compared to the success rate of NOAA's August outlooks of 53% over the same period and with the same average uncertainty range (±2). The resulting OLR indices are statistically robust, highly detectable, physically linked to the predictand, and may account for longer-term observed trends.

  8. Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

    PubMed Central

    Mestyán, Márton; Yasseri, Taha; Kertész, János

    2013-01-01

    Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia. PMID:23990938

  9. Comparison of time series models for predicting campylobacteriosis risk in New Zealand.

    PubMed

    Al-Sakkaf, A; Jones, G

    2014-05-01

    Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic.

  10. Time for prediction? The effect of presentation rate on predictive sentence comprehension during word-by-word reading

    PubMed Central

    Wlotko, Edward W.; Federmeier, Kara D.

    2015-01-01

    Predictive processing is a core component of normal language comprehension, but the brain may not engage in prediction to the same extent in all circumstances. This study investigates the effects of timing on anticipatory comprehension mechanisms. Event-related brain potentials (ERPs) were recorded while participants read two-sentence mini-scenarios previously shown to elicit prediction-related effects for implausible items that are categorically related to expected items (‘They wanted to make the hotel look more like a tropical resort. So along the driveway they planted rows of PALMS/PINES/TULIPS.’). The first sentence of every pair was presented in its entirety and was self-paced. The second sentence was presented word-by-word with a fixed stimulus onset asynchrony (SOA) of either 500 ms or 250 ms that was manipulated in a within-subjects blocked design. Amplitudes of the N400 ERP component are taken as a neural index of demands on semantic processing. At 500 ms SOA, implausible words related to predictable words elicited reduced N400 amplitudes compared to unrelated words (PINES vs. TULIPS), replicating past studies. At 250 ms SOA this prediction-related semantic facilitation was diminished. Thus, timing is a factor in determining the extent to which anticipatory mechanisms are engaged. However, we found evidence that prediction can sometimes be engaged even under speeded presentation rates. Participants who first read sentences in the 250 ms SOA block showed no effect of semantic similarity for this SOA, although these same participants showed the effect in the second block with 500 ms SOA. However, participants who first read sentences in the 500 ms SOA block continued to show the N400 semantic similarity effect in the 250 ms SOA block. These findings add to results showing that the brain flexibly allocates resources to most effectively achieve comprehension goals given the current processing environment. PMID:25987437

  11. Haida Story Telling Time with Activity Folder.

    ERIC Educational Resources Information Center

    Cogo, Robert

    One in a series of curriculum materials on Southeast Alaska Natives, this booklet contains seven myths and legends from the Haida oral tradition, each accompanied by discussion questions and suggested learning activities. Intended for use in the intermediate grades, the stories are two to four pages long with many Haida words included in the text…

  12. Functional Embedding Predicts the Variability of Neural Activity

    PubMed Central

    Mišić, Bratislav; Vakorin, Vasily A.; Paus, Tomáš; McIntosh, Anthony R.

    2011-01-01

    Neural activity is irregular and unpredictable, yet little is known about why this is the case and how this property relates to the functional architecture of the brain. Here we show that the variability of a region’s activity systematically varies according to its topological role in functional networks. We recorded the resting-state electroencephalogram (EEG) and constructed undirected graphs of functional networks. We measured the centrality of each node in terms of the number of connections it makes (degree), the ease with which the node can be reached from other nodes in the network (efficiency) and the tendency of the node to occupy a position on the shortest paths between other pairs of nodes in the network (betweenness). As a proxy for variability, we estimated the information content of neural activity using multiscale entropy analysis. We found that the rate at which information was generated was largely predicted by centrality. Namely, nodes with greater degree, betweenness, and efficiency were more likely to have high information content, while peripheral nodes had relatively low information content. These results suggest that the variability of regional activity reflects functional embedding. PMID:22164135

  13. "Personal best times in an olympic distance triathlon and a marathon predict an ironman race time for recreational female triathletes".

    PubMed

    Rüst, Christoph Alexander; Knechtle, Beat; Wirth, Andrea; Knechtle, Patrizia; Ellenrieder, Birte; Rosemann, Thomas; Lepers, Romuald

    2012-06-30

    "The aim of this study was to investigate whether the characteristics of anthropometry, training or previous performance were related to an Ironman race time in recreational female Ironman triathletes. These characteristics were correlated to an Ironman race time for 53 recreational female triathletes in order to determine the predictor variables, and so be able to predict an Ironman race time for future novice triathletes. In the bi-variate analysis, no anthropometric characteristic was related to race time. The weekly cycling kilometers (r = -0.35) and hours (r = -0.32), as well as the personal best time in an Olympic distance triathlon (r = 0.49) and in a marathon (r = 0.74) were related to an Ironman race time (< 0.05). Stepwise multiple regressions showed that both the personal best time in an Olympic distance triathlon ( P = 0.0453) and in a marathon (P = 0.0030) were the best predictors for the Ironman race time (n = 28, r² = 0.53). The race time in an Ironman triathlon might be partially predicted by the following equation (r² = 0.53, n = 28): Race time (min) = 186.3 + 1.595 × (personal best time in an Olympic distance triathlon, min) + 1.318 × (personal best time in a marathon, min) for recreational female Ironman triathletes."

  14. Essays on the predictability of oil shocks and yield curves for real-time output growth

    NASA Astrophysics Data System (ADS)

    Carlton, Amelie B.

    This dissertation is a collection of three essays that revisits the long-standing puzzle of the apparently disproportionate effect of oil prices in the economy by examining output growth predictability with real-time data. Each study of the predictive content of oil shocks is from a different perspective by using newly developed real-time datasets, which allows for replicating the economic environment faced by policymakers in real time. The first study extends the conventional set of models of output growth determination by investigating predictability of models that incorporate various functional forms of oil prices and real-time data. The results are supportive of the relationship of GDP and oil in the context of Granger causality with real-time data. In the second essay, I use oil shocks to predict the economy is changing direction earlier than would be predicted by solely using initial GDP releases. The model provides compelling evidence of negative GDP growth predictability in response to oil price shocks, which could shorten the "recognition lag" for successful implementation of discretionary counter-cyclical policies. In the third essay, I evaluate short-horizon output growth predictability using real-time data for different sample periods. I find strong evidence of predictability at the one-quarter and four-quarter horizon for the United States. The major result of the paper is that we reject the null hypothesis of no predictability against an alternative hypothesis of predictability with oil shocks that include yield curves in the forecasting regression. This relationship suggests the combination of monetary policy and oil shocks are important for subsequent GDP growth.

  15. Differences in Motor Imagery Time when Predicting Task Duration in Alpine Skiers and Equestrian Riders

    ERIC Educational Resources Information Center

    Louis, Magali; Collet, Christian; Champely, Stephane; Guillot, Aymeric

    2012-01-01

    Athletes' ability to use motor imagery (MI) to predict the speed at which they could perform a motor sequence has received little attention. In this study, 21 alpine skiers and 16 equestrian riders performed MI based on a prediction of actual performance time (a) after the course inspection, (b) before the start, and (c) after the actual…

  16. Capturing Cognitive Processing Time for Active Authentication

    DTIC Science & Technology

    2014-02-01

    biometrics, extracted from keystroke dynamics , as “something a user is” for active authentication. This scheme performs continual verification in the...fingerprint for continuous authentication. Its effectiveness has been verified through a large-scale dataset. 2.0 INTRODUCTION Keystroke dynamics —the...measure the similarity. A recent survey on biometric authentication using keystroke dynamics classified research papers on the basis of their

  17. Prediction of altimetric sea level anomalies using time series models based on spatial correlation

    NASA Astrophysics Data System (ADS)

    Miziński, Bartłomiej; Niedzielski, Tomasz

    2014-05-01

    Sea level anomaly (SLA) times series, which are time-varying gridded data, can be modelled and predicted using time series methods. This approach has been shown to provide accurate forecasts within the Prognocean system, the novel infrastructure for anticipating sea level change designed and built at the University of Wrocław (Poland) which utilizes the real-time SLA data from Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO). The system runs a few models concurrently, and our ocean prediction experiment includes both uni- and multivariate time series methods. The univariate ones are: extrapolation of polynomial-harmonic model (PH), extrapolation of polynomial-harmonic model and autoregressive prediction (PH+AR), extrapolation of polynomial-harmonic model and self-exciting threshold autoregressive prediction (PH+SETAR). The following multivariate methods are used: extrapolation of polynomial-harmonic model and vector autoregressive prediction (PH+VAR), extrapolation of polynomial-harmonic model and generalized space-time autoregressive prediction (PH+GSTAR). As the aforementioned models and the corresponding forecasts are computed in real time, hence independently and in the same computational setting, we are allowed to compare the accuracies offered by the models. The objective of this work is to verify the hypothesis that the multivariate prediction techniques, which make use of cross-correlation and spatial correlation, perform better than the univariate ones. The analysis is based on the daily-fitted and updated time series models predicting the SLA data (lead time of two weeks) over several months when El Niño/Southern Oscillation (ENSO) was in its neutral state.

  18. Basophil activation tests: time for a reconsideration.

    PubMed

    Uyttebroek, Astrid P; Sabato, Vito; Faber, Margaretha A; Cop, Nathalie; Bridts, Chris H; Lapeere, Hilde; De Clerck, Luc S; Ebo, Didier G

    2014-10-01

    Challenges in in vitro allergy diagnostics lie in the development of accessible and reliable assays allowing identification of all offending allergens and cross-reactive structures. Flow-assisted analysis and quantification of in vitro activated basophils serves as a diagnostic instrument with increasing applications developed over the years. From the earliest days it was clear that the test could constitute a diagnostic asset in basophil-mediated hypersensitivity. However, utility of the basophil activation test should be reassessed regarding difficulties with preparation, characterization and validation of allergen extracts; availability and the potential of more accessible diagnostics. Today, the added value mainly lies in diagnosis of immediate drug hypersensitivity. Other potential indications are monitoring venom-immunotherapy and follow-up of natural history of food allergies. However, results in these nondiagnostic applications are preliminary. We review the most relevant clinical applications of the basophil activation test. Some personal comments and views about perspectives and challenges about flow-assisted allergy diagnosis are made.

  19. Predicting Homework Time Management at the Secondary School Level: A Multilevel Analysis

    ERIC Educational Resources Information Center

    Xu, Jianzhong

    2010-01-01

    The purpose of this study is to test empirical models of variables posited to predict homework time management at the secondary school level. Student- and class-level predictors of homework time management were analyzed in a survey of 1895 students from 111 classes. Most of the variance in homework time management occurred at the student level,…

  20. Can the theory of planned behaviour predict the physical activity behaviour of individuals?

    PubMed

    Hobbs, Nicola; Dixon, Diane; Johnston, Marie; Howie, Kate

    2013-01-01

    The theory of planned behaviour (TPB) can identify cognitions that predict differences in behaviour between individuals. However, it is not clear whether the TPB can predict the behaviour of an individual person. This study employs a series of n-of-1 studies and time series analyses to examine the ability of the TPB to predict physical activity (PA) behaviours of six individuals. Six n-of-1 studies were conducted, in which TPB cognitions and up to three PA behaviours (walking, gym workout and a personally defined PA) were measured twice daily for six weeks. Walking was measured by pedometer step count, gym attendance by self-report with objective validation of gym entry and the personally defined PA behaviour by self-report. Intra-individual variability in TPB cognitions and PA behaviour was observed in all participants. The TPB showed variable predictive utility within individuals and across behaviours. The TPB predicted at least one PA behaviour for five participants but had no predictive utility for one participant. Thus, n-of-1 designs and time series analyses can be used to test theory in an individual.

  1. Predicting Decisions in Human Social Interactions Using Real-Time fMRI and Pattern Classification

    PubMed Central

    Baecke, Sebastian; Lützkendorf, Ralf; Müller, Charles; Adolf, Daniela; Bernarding, Johannes

    2011-01-01

    Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives. PMID:22003388

  2. Evaluation of the predictability of real-time crash risk models.

    PubMed

    Xu, Chengcheng; Liu, Pan; Wang, Wei

    2016-09-01

    The primary objective of the present study was to investigate the predictability of crash risk models that were developed using high-resolution real-time traffic data. More specifically the present study sought answers to the following questions: (a) how to evaluate the predictability of a real-time crash risk model; and (b) how to improve the predictability of a real-time crash risk model. The predictability is defined as the crash probability given the crash precursor identified by the crash risk model. An equation was derived based on the Bayes' theorem for estimating approximately the predictability of crash risk models. The estimated predictability was then used to quantitatively evaluate the effects of the threshold of crash precursors, the matched and unmatched case-control design, and the control-to-case ratio on the predictability of crash risk models. It was found that: (a) the predictability of a crash risk model can be measured as the product of prior crash probability and the ratio between sensitivity and false alarm rate; (b) there is a trade-off between the predictability and sensitivity of a real-time crash risk model; (c) for a given level of sensitivity, the predictability of the crash risk model that is developed using the unmatched case-controlled sample is always better than that of the model developed using the matched case-controlled sample; and (d) when the control-to-case ratio is beyond 4:1, the increase in control-to-case ratio does not lead to clear improvements in predictability.

  3. Estimation and prediction of noise power based on variational Bayesian and adaptive ARMA time series

    NASA Astrophysics Data System (ADS)

    Zhang, Jingyi; Li, Yonggui; Zhu, Yonggang; Li, Binwu

    2014-04-01

    Estimation and prediction of noise power are very important for communication anti-jamming and efficient allocation of spectrum resources in adaptive wireless communication and cognitive radio. In order to estimate and predict the time-varying noise power caused by natural factors and jamming in the high frequency channel, Variational Bayesian algorithm and adaptive ARMA time series are proposed. Through establishing the time-varying noise power model, which controlled by the noise variance rate, the noise power can be estimated with Variational Bayesian algorithm, and the results show that the estimation error is related to observation interval. What's more, through the analysis of the correlation characteristics of the estimation power, noise power can be predicted based on adaptive ARMA time series, and the results show that it will be available to predict the noise power in next 5 intervals with the proportional error less than 0.2.

  4. Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario

    PubMed Central

    Shu, Hua; Song, Ci; Pei, Tao; Xu, Lianming; Ou, Yang; Zhang, Libin; Li, Tao

    2016-01-01

    Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals’ average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day’s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas. PMID:27879663

  5. Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario.

    PubMed

    Shu, Hua; Song, Ci; Pei, Tao; Xu, Lianming; Ou, Yang; Zhang, Libin; Li, Tao

    2016-11-22

    Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals' average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day's WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.

  6. Predicting dose-time profiles of solar energetic particle events using Bayesian forecasting methods.

    PubMed

    Neal, J S; Townsend, L W

    2001-12-01

    Bayesian inference techniques, coupled with Markov chain Monte Carlo sampling methods, are used to predict dose-time profiles for energetic solar particle events. Inputs into the predictive methodology are dose and dose-rate measurements obtained early in the event. Surrogate dose values are grouped in hierarchical models to express relationships among similar solar particle events. Models assume nonlinear, sigmoidal growth for dose throughout an event. Markov chain Monte Carlo methods are used to sample from Bayesian posterior predictive distributions for dose and dose rate. Example predictions are provided for the November 8, 2000, and August 12, 1989, solar particle events.

  7. Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories

    NASA Astrophysics Data System (ADS)

    Xu, Tao; Li, Xiang; Claramunt, Christophe

    2017-03-01

    Accurate travel time prediction is undoubtedly of importance to both traffic managers and travelers. In highly-urbanized areas, trip-oriented travel time prediction (TOTTP) is valuable to travelers rather than traffic managers as the former usually expect to know the travel time of a trip which may cross over multiple road sections. There are two obstacles to the development of TOTTP, including traffic complexity and traffic data coverage.With large scale historical vehicle trajectory data and meteorology data, this research develops a BPNN-based approach through integrating multiple factors affecting trip travel time into a BPNN model to predict trip-oriented travel time for OD pairs in urban network. Results of experiments demonstrate that it helps discover the dominate trends of travel time changes daily and weekly, and the impact of weather conditions is non-trivial.

  8. Predicting Time-to-Relapse in Breast Cancer Using Neural Networks

    DTIC Science & Technology

    1997-12-01

    AD GRANT NUMBER DÄMD17-94-J-4137 TITLE: Predicting Time-to-Relapse in Breast Cancer Using Neural Networks PRINCIPAL INVESTIGATOR: Jonathan D...Nov 97) 4. TITLE AND SUBTITLE Predicting Time-to-Relapse in Breast Cancer Using Neural Networks 6. AUTHOR(S) Buckley, Jonathan D., Ph.D. 5...Biomedical Laboratories. Date in TABLE OF CONTENTS STANDARD FORM 298 ii FOREWORD iii TABLE OF CONTENTS iv INTRODUCTION 1 Neural networks - a

  9. Time perspective and physical activity among central Appalachian adolescents.

    PubMed

    Gulley, Tauna

    2013-04-01

    Time perspective is a cultural behavioral concept that reflects individuals' orientations or attitudes toward the past, present, or future. Individuals' time perspectives influence their choices regarding daily activities. Time perspective is an important consideration when teaching adolescents about the importance of being physically active. However, little is known about the relationship between time perspective and physical activity among adolescents. The purpose of this study was to determine the time perspective of central Appalachian adolescents and explore the relationship between time perspective and physical activity. This study was guided by The theory of planned behavior (TPB). One hundred and ninety-three students completed surveys to examine time perspective and physical activity behaviors. Data were collected in one school. Results of this study can inform school nurses and high school guidance counselors about the importance of promoting a future-oriented time perspective to improve physical activity and educational outcomes.

  10. Decreased dopamine activity predicts relapse in methamphetamine abusers

    SciTech Connect

    Wang G. J.; Wang, G.-J.; Smith, L.; Volkow, N.D.; Telang, F.; Logan, J.; Tomasi, D.; Wong, C.T.; Hoffman, W.; Jayne, M.; Alia-Klein, N.; Thanos, P.; Fowler, J.S.

    2011-01-20

    Studies in methamphetamine (METH) abusers showed that the decreases in brain dopamine (DA) function might recover with protracted detoxification. However, the extent to which striatal DA function in METH predicts recovery has not been evaluated. Here we assessed whether striatal DA activity in METH abusers is associated with clinical outcomes. Brain DA D2 receptor (D2R) availability was measured with positron emission tomography and [{sup 11}C]raclopride in 16 METH abusers, both after placebo and after challenge with 60 mg oral methylphenidate (MPH) (to measure DA release) to assess whether it predicted clinical outcomes. For this purpose, METH abusers were tested within 6 months of last METH use and then followed up for 9 months of abstinence. In parallel, 15 healthy controls were tested. METH abusers had lower D2R availability in caudate than in controls. Both METH abusers and controls showed decreased striatal D2R availability after MPH and these decreases were smaller in METH than in controls in left putamen. The six METH abusers who relapsed during the follow-up period had lower D2R availability in dorsal striatum than in controls, and had no D2R changes after MPH challenge. The 10 METH abusers who completed detoxification did not differ from controls neither in striatal D2R availability nor in MPH-induced striatal DA changes. These results provide preliminary evidence that low striatal DA function in METH abusers is associated with a greater likelihood of relapse during treatment. Detection of the extent of DA dysfunction may be helpful in predicting therapeutic outcomes.

  11. Pallidal spiking activity reflects learning dynamics and predicts performance

    PubMed Central

    Noblejas, Maria Imelda; Mizrahi, Aviv D.; Dauber, Omer; Bergman, Hagai

    2016-01-01

    The basal ganglia (BG) network has been divided into interacting actor and critic components, modulating the probabilities of different state–action combinations through learning. Most models of learning and decision making in the BG focus on the roles of the striatum and its dopaminergic inputs, commonly overlooking the complexities and interactions of BG downstream nuclei. In this study, we aimed to reveal the learning-related activity of the external segment of the globus pallidus (GPe), a downstream structure whose computational role has remained relatively unexplored. Recording from monkeys engaged in a deterministic three-choice reversal learning task, we found that changes in GPe discharge rates predicted subsequent behavioral shifts on a trial-by-trial basis. Furthermore, the activity following the shift encoded whether it resulted in reward or not. The frequent changes in stimulus–outcome contingencies (i.e., reversals) allowed us to examine the learning-related neural activity and show that GPe discharge rates closely matched across-trial learning dynamics. Additionally, firing rates exhibited a linear decrease in sequences of correct responses, possibly reflecting a gradual shift from goal-directed execution to automaticity. Thus, modulations in GPe spiking activity are highest for attention-demanding aspects of behavior (i.e., switching choices) and decrease as attentional demands decline (i.e., as performance becomes automatic). These findings are contrasted with results from striatal tonically active neurons, which show none of these task-related modulations. Our results demonstrate that GPe, commonly studied in motor contexts, takes part in cognitive functions, in which movement plays a marginal role. PMID:27671661

  12. Autonomic activity during sleep predicts memory consolidation in humans

    PubMed Central

    Whitehurst, Lauren N.; McDevitt, Elizabeth A.; Duggan, Katherine A.; Mednick, Sara C.

    2016-01-01

    Throughout history, psychologists and philosophers have proposed that good sleep benefits memory, yet current studies focusing on the relationship between traditionally reported sleep features (e.g., minutes in sleep stages) and changes in memory performance show contradictory findings. This discrepancy suggests that there are events occurring during sleep that have not yet been considered. The autonomic nervous system (ANS) shows strong variation across sleep stages. Also, increases in ANS activity during waking, as measured by heart rate variability (HRV), have been correlated with memory improvement. However, the role of ANS in sleep-dependent memory consolidation has never been examined. Here, we examined whether changes in cardiac ANS activity (HRV) during a daytime nap were related to performance on two memory conditions (Primed and Repeated) and a nonmemory control condition on the Remote Associates Test. In line with prior studies, we found sleep-dependent improvement in the Primed condition compared with the Quiet Wake control condition. Using regression analyses, we compared the proportion of variance in performance associated with traditionally reported sleep features (model 1) vs. sleep features and HRV during sleep (model 2). For both the Primed and Repeated conditions, model 2 (sleep + HRV) predicted performance significantly better (73% and 58% of variance explained, respectively) compared with model 1 (sleep only, 46% and 26% of variance explained, respectively). These findings present the first evidence, to our knowledge, that ANS activity may be one potential mechanism driving sleep-dependent plasticity. PMID:27298366

  13. Solubility Prediction of Active Pharmaceutical Compounds with the UNIFAC Model

    NASA Astrophysics Data System (ADS)

    Nouar, Abderrahim; Benmessaoud, Ibtissem; Koutchoukali, Ouahiba; Koutchoukali, Mohamed Salah

    2016-03-01

    The crystallization from solution of an active pharmaceutical ingredient requires the knowledge of the solubility in the entire temperature range investigated during the process. However, during the development of a new active ingredient, these data are missing. Its experimental determination is possible, but tedious. UNIFAC Group contribution method Fredenslund et al. (Vapor-liquid equilibria using UNIFAC: a group contribution method, 1977; AIChE J 21:1086, 1975) can be used to predict this physical property. Several modifications on this model have been proposed since its development in 1977, modified UNIFAC of Dortmund Weidlich et al. (Ind Eng Chem Res 26:1372, 1987), Gmehling et al. (Ind Eng Chem Res 32:178, 1993), Pharma-modified UNIFAC Diedrichs et al. (Evaluation und Erweiterung thermodynamischer Modelle zur Vorhersage von Wirkstofflöslichkeiten, PhD Thesis, 2010), KT-UNIFAC Kang et al. (Ind Eng Chem Res 41:3260, 2002), ldots In this study, we used UNIFAC model by considering the linear temperature dependence of interaction parameters as in Pharma-modified UNIFAC and structural groups as defined by KT-UNIFAC first-order model. More than 100 binary datasets were involved in the estimation of interaction parameters. These new parameters were then used to calculate activity coefficient and solubility of some molecules in various solvents at different temperatures. The model gives better results than those from the original UNIFAC and shows good agreement between the experimental solubility and the calculated one.

  14. Aging of nickel added to soils as predicted by soil pH and time.

    PubMed

    Ma, Yibing; Lombi, Enzo; McLaughlin, Mike J; Oliver, Ian W; Nolan, Annette L; Oorts, Koen; Smolders, Erik

    2013-08-01

    Although aging processes are important in risk assessment for metals in soils, the aging of Ni added to soils has not been studied in detail. In this study, after addition of water soluble Ni to soils, the changes over time in isotopic exchangeability, total concentrations and free Ni(2+) activity in soil pore water, were investigated in 16 European soils incubated outdoors for 18 months. The results showed that after Ni addition, concentrations of Ni in soil pore water and isotopic exchangeability of Ni in soils initially decreased rapidly. This phase was followed by further decreases in the parameters measured but these occurred at slower rates. Increasing soil pH increased the rate and extent of aging reactions. Semi-mechanistic models, based on Ni precipitation/nucleation on soil surfaces and micropore diffusion, were developed and calibrated. The initial fast processes, which were attributed to precipitation/nucleation, occurred over a short time (e.g. 1h), afterwards the slow processes were most likely controlled by micropore diffusion processes. The models were validated by comparing predicted and measured Ni aging in three additional, widely differing soils aged outdoors for periods up to 15 months in different conditions. These models could be used to scale ecotoxicological data generated in short-term studies to longer aging times.

  15. Predicting earthquakes by analyzing accelerating precursory seismic activity

    USGS Publications Warehouse

    Varnes, D.J.

    1989-01-01

    During 11 sequences of earthquakes that in retrospect can be classed as foreshocks, the accelerating rate at which seismic moment is released follows, at least in part, a simple equation. This equation (1) is {Mathematical expression},where {Mathematical expression} is the cumulative sum until time, t, of the square roots of seismic moments of individual foreshocks computed from reported magnitudes;C and n are constants; and tfis a limiting time at which the rate of seismic moment accumulation becomes infinite. The possible time of a major foreshock or main shock, tf,is found by the best fit of equation (1), or its integral, to step-like plots of {Mathematical expression} versus time using successive estimates of tfin linearized regressions until the maximum coefficient of determination, r2,is obtained. Analyzed examples include sequences preceding earthquakes at Cremasta, Greece, 2/5/66; Haicheng, China 2/4/75; Oaxaca, Mexico, 11/29/78; Petatlan, Mexico, 3/14/79; and Central Chile, 3/3/85. In 29 estimates of main-shock time, made as the sequences developed, the errors in 20 were less than one-half and in 9 less than one tenth the time remaining between the time of the last data used and the main shock. Some precursory sequences, or parts of them, yield no solution. Two sequences appear to include in their first parts the aftershocks of a previous event; plots using the integral of equation (1) show that the sequences are easily separable into aftershock and foreshock segments. Synthetic seismic sequences of shocks at equal time intervals were constructed to follow equation (1), using four values of n. In each series the resulting distributions of magnitudes closely follow the linear Gutenberg-Richter relation log N=a-bM, and the product n times b for each series is the same constant. In various forms and for decades, equation (1) has been used successfully to predict failure times of stressed metals and ceramics, landslides in soil and rock slopes, and volcanic

  16. Target of rapamycin activation predicts lifespan in fruit flies

    PubMed Central

    Scialò, Filippo; Sriram, Ashwin; Naudí, Alba; Ayala, Victoria; Jové, Mariona; Pamplona, Reinald; Sanz, Alberto

    2015-01-01

    Aging and age-related diseases are one of the most important health issues that the world will confront during the 21st century. Only by understanding the proximal causes will we be able to find treatments to reduce or delay the onset of degenerative diseases associated with aging. Currently, the prevalent paradigm in the field is the accumulation of damage. However, a new theory that proposes an alternative explanation is gaining momentum. The hyperfunction theory proposes that aging is not a consequence of a wear and tear process, but a result of the continuation of developmental programs during adulthood. Here we use Drosophila melanogaster, where evidence supporting both paradigms has been reported, to identify which parameters that have been previously related with lifespan best predict the rate of aging in wild type flies cultured at different temperatures. We find that mitochondrial function and mitochondrial reactive oxygen species (mtROS) generation correlates with metabolic rate, but not with the rate of aging. Importantly, we find that activation of nutrient sensing pathways (i.e. insulin-PI3K/Target of rapamycin (Tor) pathway) correlates with lifespan, but not with metabolic rate. Our results, dissociate metabolic rate and lifespan in wild type flies and instead link nutrient sensing signaling with longevity as predicted by the hyperfunction theory. PMID:26259964

  17. Impact of meteorological predictions on real-time spring flow forecasting

    NASA Astrophysics Data System (ADS)

    Coulibaly, Paulin

    2003-12-01

    Meteorological predictions, such as precipitation and temperature, are commonly used to improve real-time hydrologic forecasting, despite their inherent uncertainty and their absence in the model calibration stage. In this study, we quantify the effect of meteorological prediction errors on the accuracy of daily spring reservoir inflow forecasts using weather predictions in both the model calibration and testing phases. Different modelling experiments are compared using an operational conceptual model and nonlinear empirical models to assess the effects of using daily numerical weather predictions as opposed to the use of historical observations. It is found that, even with large prediction errors, meteorological forecasts can provide significant improvement of spring flow forecast for up to 7 days lead time, particularly for low flows. Spring flow prediction errors associated with the type of hydrological model used are significantly larger than those related to the meteorological predictions, particularly for 1 to 4 days ahead forecasts. The experimental results also indicate that multiple model-based forecasting using an iterative prediction approach appears to be the most effective method for an adequate use of weather predictions. Copyright

  18. Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

    PubMed Central

    Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

    2015-01-01

    Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes. PMID:26294903

  19. Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

    PubMed

    Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

    2015-01-01

    Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

  20. Predictive fuzzy reasoning method for time series stock market data mining

    NASA Astrophysics Data System (ADS)

    Khokhar, Rashid H.; Md Sap, Mohd Noor

    2005-03-01

    Data mining is able to uncover hidden patterns and predict future trends and behaviors in financial markets. In this research we approach quantitative time series stock selection as a data mining problem. We present another modification of extraction of weighted fuzzy production rules (WFPRs) from fuzzy decision tree by using proposed similarity-based fuzzy reasoning method called predictive reasoning (PR) method. In proposed predictive reasoning method weight parameter can be assigned to each proposition in the antecedent of a fuzzy production rule (FPR) and certainty factor (CF) to each rule. Certainty factors are calculated by using some important variables like effect of other companies, effect of other local stock market, effect of overall world situation, and effect of political situation from stock market. The predictive FDT has been tested using three data sets including KLSE, NYSE and LSE. The experimental results show that WFPRs rules have high learning accuracy and also better predictive accuracy of stock market time series data.

  1. Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates

    PubMed Central

    Kambara, Hiroyuki; Nambu, Atsushi; Isa, Tadashi; Nishimura, Yukio; Koike, Yasuharu

    2012-01-01

    Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work for the realization of ECoG-based BMIs as neuroprosthetics. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements. ECoG signals were band-pass filtered into separate sensorimotor rhythm bands, z-score normalized, and smoothed with a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity. The best average correlation coefficient and the normalized root-mean-square error were 0.92±0.06 and 0.06±0.10, respectively, in the flexor digitorum profundus finger muscle. The δ (1.5∼4Hz) and γ2 (50∼90Hz) bands contributed significantly more strongly than other frequency bands (P<0.001). These results demonstrate the feasibility of predicting muscle activity from ECoG signals in an online fashion. PMID:23110153

  2. Gain scheduled continuous-time model predictive controller with experimental validation on AC machine

    NASA Astrophysics Data System (ADS)

    Wang, Liuping; Gan, Lu

    2013-08-01

    Linear controllers with gain scheduling have been successfully used in the control of nonlinear systems for the past several decades. This paper proposes the design of gain scheduled continuous-time model predictive controller with constraints. Using induction machine as an illustrative example, the paper will show the four steps involved in the design of a gain scheduled predictive controller: (i) linearisation of a nonlinear plant according to operating conditions; (ii) the design of linear predictive controllers for the family of linear models; (iii) gain scheduled predictive control law that will optimise a multiple model objective function with constraints, which will also ensure smooth transitions (i.e. bumpless transfer) between the predictive controllers; (iv) experimental validation of the gain scheduled predictive control system with constraints.

  3. Using Kalman Filtering to Predict Time-Varying Parameters in a Model Predicting Baroreflex Regulation During Head-Up Tilt.

    PubMed

    Matzuka, Brett; Mehlsen, Jesper; Tran, Hien; Olufsen, Mette Sofie

    2015-08-01

    The cardiovascular control system is continuously engaged to maintain homeostasis, but it is known to fail in a large cohort of patients suffering from orthostatic intolerance. Numerous clinical studies have been put forward to understand how the system fails, yet noninvasive clinical data are sparse, typical studies only include measurements of heart rate and blood pressure, as a result it is difficult to determine what mechanisms that are impaired. It is known, that blood pressure regulation is mediated by changes in heart rate, vascular resistance, cardiac contractility, and a number of other factors. Given that numerous factors contribute to changing these quantities, it is difficult to devise a physiological model describing how they change in time. One way is to build a model that allows these controlled quantities to change and to compare dynamics between subject groups. To do so, it requires more knowledge of how these quantities change for healthy subjects. This study compares two methods predicting time-varying changes in cardiac contractility and vascular resistance during head-up tilt. Similar to the study by Williams et al. [51], the first method uses piecewise linear splines, while the second uses the ensemble transform Kalman filter (ETKF) [1], [11], [12], [33]. In addition, we show that the delayed rejection adaptive Metropolis (DRAM) algorithm can be used for predicting parameter uncertainties within the spline methodology, which is compared with the variability obtained with the ETKF. While the spline method is easier to set up, this study shows that the ETKF has a significantly shorter computational time. Moreover, while uncertainty of predictions can be augmented to spline predictions using DRAM, these are readily available with the ETKF.

  4. Activity and loading influence the predicted bone remodeling around cemented hip replacements.

    PubMed

    Dickinson, Alexander S

    2014-04-01

    Periprosthetic bone remodeling is frequently observed after total hip replacement. Reduced bone density increases the implant and bone fracture risk, and a gross loss of bone density challenges fixation in subsequent revision surgery. Computational approaches allow bone remodeling to be predicted in agreement with the general clinical observations of proximal resorption and distal hypertrophy. However, these models do not reproduce other clinically observed bone density trends, including faster stabilizing mid-stem density losses, and loss-recovery trends around the distal stem. These may resemble trends in postoperative joint loading and activity, during recovery and rehabilitation, but the established remodeling prediction approach is often used with identical pre- and postoperative load and activity assumptions. Therefore, this study aimed to evaluate the influence of pre- to postoperative changes in activity and loading upon the predicted progression of remodeling. A strain-adaptive finite element model of a femur implanted with a cemented Charnley stem was generated, to predict 60 months of periprosthetic remodeling. A control set of model input data assumed identical pre- and postoperative loading and activity, and was compared to the results obtained from another set of inputs with three varying activity and load profiles. These represented activity changes during rehabilitation for weak, intermediate and strong recoveries, and pre- to postoperative joint force changes due to hip center translation and the use of walking aids. Predicted temporal bone density change trends were analyzed, and absolute bone density changes and the time to homeostasis were inspected, alongside virtual X-rays. The predicted periprosthetic bone density changes obtained using modified loading inputs demonstrated closer agreement with clinical measurements than the control. The modified inputs also predicted the clinically observed temporal density change trends, but still under

  5. Predicting trace organic compound attenuation with spectroscopic parameters in powdered activated carbon processes.

    PubMed

    Ziska, Austin D; Park, Minkyu; Anumol, Tarun; Snyder, Shane A

    2016-08-01

    The removal of trace organic compounds (TOrCs) is of growing interest in water research and society. Powdered activated carbon (PAC) has been proven to be an effective method of removal for TOrCs in water, with the degree of effectiveness depending on dosage, contact time, and activated carbon type. In this study, the attenuation of TOrCs in three different secondary wastewater effluents using four PAC materials was studied in order to elucidate the effectiveness and efficacy of PAC for TOrC removal. With the notable exception of hydrochlorothiazide, all 14 TOrC indicators tested in this study exhibited a positive correlation of removal rate with their log Dow values, demonstrating that the main adsorption mechanism was hydrophobic interaction. As a predictive model, the modified Chick-Watson model, often used for the prediction of microorganism inactivation by disinfectants, was applied. The applied model exhibited good predictive power for TOrC attenuation by PAC in wastewater. In addition, surrogate models based upon spectroscopic measurements including UV absorbance at 254 nm and total fluorescence were applied to predict TOrC removal by PAC. The surrogate model was found to provide an excellent prediction of TOrC attenuation for all combinations of water quality and PAC type included in this study. The success of spectrometric parameters as surrogates in predicting TOrC attenuation by PAC are particularly useful because of their potential application in real-time on-line sensor monitoring and process control at full-scale water treatment plants, which could lead to significantly reduced operator response times and PAC operational optimization.

  6. Medial Temporal Lobe Activity Predicts Successful Relational Memory Binding

    PubMed Central

    Hannula, Deborah E.; Ranganath, Charan

    2009-01-01

    Previous neuropsychological findings have implicated medial temporal lobe (MTL) structures in retaining object-location relations over the course of short delays, but MTL effects have not always been reported in neuroimaging investigations with similar short-term memory requirements. Here, we used event-related functional magnetic resonance imaging to test the hypothesis that the hippocampus and related MTL structures support accurate retention of relational memory representations, even across short delays. On every trial, four objects were presented, each in one of nine possible locations of a three-dimensional grid. Participants were to mentally rotate the grid and then maintain the rotated representation in anticipation of a test stimulus: a rendering of the grid, rotated 90° from the original viewpoint. The test stimulus was either a “match” display, in which object-location relations were intact, or a “mismatch” display, in which one object occupied a new, previously unfilled location (mismatch position), or two objects had swapped locations (mismatch swap). Encoding phase activation in anterior and posterior regions of the left hippocampus, and in bilateral perirhinal cortex, predicted subsequent accuracy on the short-term memory decision, as did bilateral posterior hippocampal activity after the test stimulus. Notably, activation in these posterior hippocampal regions was also sensitive to the degree to which object-location bindings were preserved in the test stimulus; activation was greatest for match displays, followed by mismatch-position displays, and finally mismatch-swap displays. These results indicate that the hippocampus and related MTL structures contribute to successful encoding and retrieval of relational information in visual short-term memory. PMID:18171929

  7. Healthy Work Revisited: Do Changes in Time Strain Predict Well-Being?

    PubMed Central

    Moen, Phyllis; Kelly, Erin L.; Lam, Jack

    2013-01-01

    Building on Karasek and Theorell (R. Karasek & T. Theorell, 1990, Healthy work: Stress, productivity, and the reconstruction of working life, New York, NY: Basic Books), we theorized and tested the relationship between time strain (work-time demands and control) and seven self-reported health outcomes. We drew on survey data from 550 employees fielded before and 6 months after the implementation of an organizational intervention, the Results Only Work Environment (ROWE) in a white-collar organization. Cross-sectional (Wave 1) models showed psychological time demands and time control measures were related to health outcomes in expected directions. The ROWE intervention did not predict changes in psychological time demands by Wave 2, but did predict increased time control (a sense of time adequacy and schedule control). Statistical models revealed increases in psychological time demands and time adequacy predicted changes in positive (energy, mastery, psychological well-being, self-assessed health) and negative (emotional exhaustion, somatic symptoms, psychological distress) outcomes in expected directions, net of job and home demands and covariates. This study demonstrates the value of including time strain in investigations of the health effects of job conditions. Results encourage longitudinal models of change in psychological time demands as well as time control, along with the development and testing of interventions aimed at reducing time strain in different populations of workers. PMID:23506547

  8. Predicting Regional Drought on Sub-Seasonal to Decadal Time Scales

    NASA Technical Reports Server (NTRS)

    Schubert, Siegfried; Wang, Hailan; Suarez, Max; Koster, Randal

    2011-01-01

    Drought occurs on a wide range of time scales, and within a variety of different types of regional climates. It is driven foremost by an extended period of reduced precipitation, but it is the impacts on such quantities as soil moisture, streamflow and crop yields that are often most important from a users perspective. While recognizing that different users have different needs for drought information, it is nevertheless important to understand that progress in predicting drought and satisfying such user needs, largely hinges on our ability to improve predictions of precipitation. This talk reviews our current understanding of the physical mechanisms that drive precipitation variations on subseasonal to decadal time scales, and the implications for predictability and prediction skill. Examples are given highlighting the phenomena and mechanisms controlling precipitation on monthly (e.g., stationary Rossby waves, soil moisture), seasonal (ENSO) and decadal time scales (PD and AMO).

  9. Time-Preference Tests Fail to Predict Behavior Related to Self-control

    PubMed Central

    Arfer, Kodi B.; Luhmann, Christian C.

    2017-01-01

    According to theory, choices relating to patience and self-control in domains as varied as drug use and retirement saving are driven by generalized preferences about delayed rewards. Past research has shown that measurements of these time preferences are associated with these choices. Research has also attempted to examine how well such measurements can predict choices, but only with inappropriate analytical methods. Moreover, it is not clear which of the many kinds of time-preference tests that have been proposed are most useful for prediction, and a theoretically important aspect of time preferences, nonstationarity, has been neglected in measurement. In Study 1, we examined three approaches to measuring time preferences with 181 users of Mechanical Turk. Retest reliability, for both immediate and 1-month intervals, was decent, as was convergent validity between tests, and association was similar to previous results, but predictive accuracy for 10 criterion variables (e.g., tobacco use) was approximately nil. In Study 2, we examined one other approach to measuring time preferences, and 40 criterion variables, using 7,127 participants in the National Longitudinal Survey of Youth 1979. Time preferences were significantly related to criterion variables, but predictive accuracy was again poor. Our findings imply serious problems for using time-preference tests to predict real-world decisions. The results of Study 1 further suggest there is little value in measuring nonstationarity separately from patience. PMID:28232810

  10. Predictability of nonstationary time series using wavelet and EMD based ARMA models

    NASA Astrophysics Data System (ADS)

    Karthikeyan, L.; Nagesh Kumar, D.

    2013-10-01

    Research has been undertaken to ascertain the predictability of non-stationary time series using wavelet and Empirical Mode Decomposition (EMD) based time series models. Methods have been developed in the past to decompose a time series into components. Forecasting of these components combined with random component could yield predictions. Using this ideology, wavelet and EMD analyses have been incorporated separately which decomposes a time series into independent orthogonal components with both time and frequency localizations. The component series are fit with specific auto-regressive models to obtain forecasts which are later combined to obtain the actual predictions. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability is checked for six and twelve months ahead forecasts across both the methodologies. Based on performance measures, it is observed that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place. Finally, the study concludes that the wavelet based time series algorithm can be used to model events such as droughts with reasonable accuracy. Also, some modifications that can be made in the model have been discussed that could extend the scope of applicability to other areas in the field of hydrology.

  11. Innovation diffusion on time-varying activity driven networks

    NASA Astrophysics Data System (ADS)

    Rizzo, Alessandro; Porfiri, Maurizio

    2016-01-01

    Since its introduction in the 1960s, the theory of innovation diffusion has contributed to the advancement of several research fields, such as marketing management and consumer behavior. The 1969 seminal paper by Bass [F.M. Bass, Manag. Sci. 15, 215 (1969)] introduced a model of product growth for consumer durables, which has been extensively used to predict innovation diffusion across a range of applications. Here, we propose a novel approach to study innovation diffusion, where interactions among individuals are mediated by the dynamics of a time-varying network. Our approach is based on the Bass' model, and overcomes key limitations of previous studies, which assumed timescale separation between the individual dynamics and the evolution of the connectivity patterns. Thus, we do not hypothesize homogeneous mixing among individuals or the existence of a fixed interaction network. We formulate our approach in the framework of activity driven networks to enable the analysis of the concurrent evolution of the interaction and individual dynamics. Numerical simulations offer a systematic analysis of the model behavior and highlight the role of individual activity on market penetration when targeted advertisement campaigns are designed, or a competition between two different products takes place.

  12. Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand

    PubMed Central

    Lauer, Stephen A.; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E.; Salje, Henrik; Cummings, Derek A. T.; Lessler, Justin

    2016-01-01

    Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. PMID:27304062

  13. Off-Time Pubertal Timing Predicts Physiological Reactivity to Postpuberty Interpersonal Stress

    ERIC Educational Resources Information Center

    Smith, Anne Emilie; Powers, Sally I.

    2009-01-01

    We investigated associations between retrospectively assessed timing of pubertal development, interpersonal interactions, and hypothalamic-pituitary-adrenal axis reactivity to an interpersonal stress task in 110 young adult women. Participants provided salivary cortisol samples at points prior and subsequent to a video-taped conflict discussion…

  14. Free Time Motivation and Physical Activity in Middle School Children

    ERIC Educational Resources Information Center

    Kozub, Francis M.; Farmer, James

    2011-01-01

    This study examined free time motivation and physical activity in 68 middle school children from a rural public school system (N = 24) and a private school located in the same area of the Midwest (N = 44). Results indicated that free time motivation did not explain variability in physical activity behavior during free time or while students were…

  15. Time Perspective and Physical Activity among Central Appalachian Adolescents

    ERIC Educational Resources Information Center

    Gulley, Tauna

    2013-01-01

    Time perspective is a cultural behavioral concept that reflects individuals' orientations or attitudes toward the past, present, or future. Individuals' time perspectives influence their choices regarding daily activities. Time perspective is an important consideration when teaching adolescents about the importance of being physically active.…

  16. How Young Children Spend Their Time: Television and Other Activities.

    ERIC Educational Resources Information Center

    Huston, Aletha C.; Wright, John C.; Marquis, Janet; Green, Samuel B.

    1999-01-01

    Examined television viewing over three years among two cohorts of 2- and 4-year olds. Found that viewing declined with age. With age, time in reading and educational activities increased on weekdays but declined on weekends, and sex differences in time-use patterns increased. Increased time in educational activities, social interaction, and video…

  17. Time Score: A New Feature for Link Prediction in Social Networks

    NASA Astrophysics Data System (ADS)

    Munasinghe, Lankeshwara; Ichise, Ryutaro

    Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most previous studies have not sufficiently discussed either the impact of time stamps of the interactions or time stamps of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduce a new time-aware feature, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We also analyze the effectiveness of time score with different parameter settings for different network data sets. The results of the analysis revealed that the time score was sensitive to different networks and different time measures. We applied time score to two social network data sets, namely, Facebook friendship network data set and a coauthorship network data set. The results revealed a significant improvement in predicting future links.

  18. Long-term polar motion prediction using normal time-frequency transform

    NASA Astrophysics Data System (ADS)

    Su, Xiaoqing; Liu, Lintao; Houtse, Hsu; Wang, Guocheng

    2014-02-01

    This paper presents normal time-frequency transform (NTFT) application in harmonic/quasi-harmonic signal prediction. Particularly, we use the normal wavelet transform (a special NTFT) to make long-term polar motion prediction. Instantaneous frequency, phase and amplitude of Chandler wobble, prograde and retrograde annual wobbles of Earth's polar motion are analyzed via the NTFT. Results show that the three main wobbles can be treated as quasi-harmonic processes. Current instantaneous harmonic information of the three wobbles can be acquired by the NTFT that has a kernel function constructed with a normal half-window function. Based on this information, we make the polar motion predictions with lead times of 1 year and 5 years. Results show that our prediction skills are very good with long lead time. An abnormality in the predictions occurs during the second half of 2005 and first half of 2006. Finally, we provide the future (starting from 2013) polar motion predictions with 1- and 5-year leads. These predictions will be used to verify the effectiveness of the method proposed in this paper.

  19. Predicting myelinated axon activation using spatial characteristics of the extracellular field

    PubMed Central

    Peterson, EJ; Izad, O; Tyler, DJ

    2011-01-01

    Computation time required for modeling the nonlinear response of an axon to an applied electric field is a significant limitation to optimizing a large number of neural interface design parameters through use of advanced computer algorithms. This paper introduces two methods of predicting axon activation that incorporate a threshold that includes the magnitude of the extracellular potential to achieve increased accuracy over previous computationally efficient methods. Each method employs the use of a modified driving function that includes the second spatial difference of the applied extracellular voltage to predict the electrical excitation of a nerve. The first method uses the second spatial difference taken at a single node of Ranvier, while the second uses a weighted sum of the second spatial differences taken at all nodes of Ranvier. This study quantifies prediction accuracy for cases with single and multiple point source stimulating electrodes. While both new methods address the major criticism of linearized prediction models, the weighted sum method provides the most robust response across single and multiple point sources. These methods eimprove prediction of axon activation based on properties of the applied field in a computationally efficient manner. PMID:21750371

  20. Role of spontaneous physical activity in prediction of susceptibility to activity based anorexia in male and female rats.

    PubMed

    Perez-Leighton, Claudio E; Grace, Martha; Billington, Charles J; Kotz, Catherine M

    2014-08-01

    Anorexia nervosa (AN) is a chronic eating disorder affecting females and males, defined by body weight loss, higher physical activity levels and restricted food intake. Currently, the commonalities and differences between genders in etiology of AN are not well understood. Animal models of AN, such as activity-based anorexia (ABA), can be helpful in identifying factors determining individual susceptibility to AN. In ABA, rodents are given an access to a running wheel while food restricted, resulting in paradoxical increased physical activity levels and weight loss. Recent studies suggest that different behavioral traits, including voluntary exercise, can predict individual weight loss in ABA. A higher inherent drive for movement may promote development and severity of AN, but this hypothesis remains untested. In rodents and humans, drive for movement is defined as spontaneous physical activity (SPA), which is time spent in low-intensity, non-volitional movements. In this paper, we show that a profile of body weight history and behavioral traits, including SPA, can predict individual weight loss caused by ABA in male and female rats with high accuracy. Analysis of the influence of SPA on ABA susceptibility in males and females rats suggests that either high or low levels of SPA increase the probability of high weight loss in ABA, but with larger effects in males compared to females. These results suggest that the same behavioral profile can identify individuals at-risk of AN for both male and female populations and that SPA has predictive value for susceptibility to AN.

  1. Time and Frequency Activities at the JHU Applied Physics Laboratory

    DTIC Science & Technology

    2009-11-01

    Resolution Offset Generator  2 GPS Time Transfer Receivers Time and Frequency Dissemination  1 MHz, 5 MHz, 10 MHz, 100 MHz  1 PPS  IRIG ...B APL Local Time  IRIG -B UTC  Common View GPS Time Transfer •NIST, USNO, BIPM 41 st Annual Precise Time and Time Interval (PTTI) Meeting...MASER* CESIUM 3 MICROPHASE STEPPER 5 MHZ DISTRIBUTION APL TIMESCALE PROCESSOR PREDICTION ALGORITHM TIMECODE 1PPS & IRIG 8 CHANNEL GPS A

  2. Predicting the activity phase of a follower neuron with A-current in an inhibitory network.

    PubMed

    Zhang, Yu; Bose, Amitabha; Nadim, Farzan

    2008-09-01

    The transient potassium A-current is present in most neurons and plays an important role in determining the timing of action potentials. We examine the role of the A-current in the activity phase of a follower neuron in a rhythmic feed-forward inhibitory network with a reduced three-variable model and conduct experiments to verify the usefulness of our model. Using geometric analysis of dynamical systems, we explore the factors that determine the onset of activity in a follower neuron following release from inhibition. We first analyze the behavior of the follower neuron in a single cycle and find that the phase plane structure of the model can be used to predict the potential behaviors of the follower neuron following release from inhibition. We show that, depending on the relative scales of the inactivation time constant of the A-current and the time constant of the recovery variable, the follower neuron may or may not reach its active state following inhibition. Our simple model is used to derive a recursive set of equations to predict the contribution of the A-current parameters in determining the activity phase of a follower neuron as a function of the duration and frequency of the inhibitory input it receives. These equations can be used to demonstrate the dependence of activity phase on the period and duty cycle of the periodic inhibition, as seen by comparing the predictions of the model with the activity of the pyloric constrictor (PY) neurons in the crustacean pyloric network.

  3. Driver Vision Based Perception-Response Time Prediction and Assistance Model on Mountain Highway Curve.

    PubMed

    Li, Yi; Chen, Yuren

    2016-12-30

    To make driving assistance system more humanized, this study focused on the prediction and assistance of drivers' perception-response time on mountain highway curves. Field tests were conducted to collect real-time driving data and driver vision information. A driver-vision lane model quantified curve elements in drivers' vision. A multinomial log-linear model was established to predict perception-response time with traffic/road environment information, driver-vision lane model, and mechanical status (last second). A corresponding assistance model showed a positive impact on drivers' perception-response times on mountain highway curves. Model results revealed that the driver-vision lane model and visual elements did have important influence on drivers' perception-response time. Compared with roadside passive road safety infrastructure, proper visual geometry design, timely visual guidance, and visual information integrality of a curve are significant factors for drivers' perception-response time.

  4. LPTA: Location Predictive and Time Adaptive Data Gathering Scheme with Mobile Sink for Wireless Sensor Networks

    PubMed Central

    Rodrigues, Joel J. P. C.

    2014-01-01

    This paper exploits sink mobility to prolong the lifetime of sensor networks while maintaining the data transmission delay relatively low. A location predictive and time adaptive data gathering scheme is proposed. In this paper, we introduce a sink location prediction principle based on loose time synchronization and deduce the time-location formulas of the mobile sink. According to local clocks and the time-location formulas of the mobile sink, nodes in the network are able to calculate the current location of the mobile sink accurately and route data packets timely toward the mobile sink by multihop relay. Considering that data packets generating from different areas may be different greatly, an adaptive dwelling time adjustment method is also proposed to balance energy consumption among nodes in the network. Simulation results show that our data gathering scheme enables data routing with less data transmission time delay and balance energy consumption among nodes. PMID:25302327

  5. Driver Vision Based Perception-Response Time Prediction and Assistance Model on Mountain Highway Curve

    PubMed Central

    Li, Yi; Chen, Yuren

    2016-01-01

    To make driving assistance system more humanized, this study focused on the prediction and assistance of drivers’ perception-response time on mountain highway curves. Field tests were conducted to collect real-time driving data and driver vision information. A driver-vision lane model quantified curve elements in drivers’ vision. A multinomial log-linear model was established to predict perception-response time with traffic/road environment information, driver-vision lane model, and mechanical status (last second). A corresponding assistance model showed a positive impact on drivers’ perception-response times on mountain highway curves. Model results revealed that the driver-vision lane model and visual elements did have important influence on drivers’ perception-response time. Compared with roadside passive road safety infrastructure, proper visual geometry design, timely visual guidance, and visual information integrality of a curve are significant factors for drivers’ perception-response time. PMID:28042851

  6. Predicting Monoamine Oxidase Inhibitory Activity through Ligand-Based Models

    PubMed Central

    Vilar, Santiago; Ferino, Giulio; Quezada, Elias; Santana, Lourdes; Friedman, Carol

    2013-01-01

    The evolution of bio- and cheminformatics associated with the development of specialized software and increasing computer power has produced a great interest in theoretical in silico methods applied in drug rational design. These techniques apply the concept that “similar molecules have similar biological properties” that has been exploited in Medicinal Chemistry for years to design new molecules with desirable pharmacological profiles. Ligand-based methods are not dependent on receptor structural data and take into account two and three-dimensional molecular properties to assess similarity of new compounds in regards to the set of molecules with the biological property under study. Depending on the complexity of the calculation, there are different types of ligand-based methods, such as QSAR (Quantitative Structure-Activity Relationship) with 2D and 3D descriptors, CoMFA (Comparative Molecular Field Analysis) or pharmacophoric approaches. This work provides a description of a series of ligand-based models applied in the prediction of the inhibitory activity of monoamine oxidase (MAO) enzymes. The controlled regulation of the enzymes’ function through the use of MAO inhibitors is used as a treatment in many psychiatric and neurological disorders, such as depression, anxiety, Alzheimer’s and Parkinson’s disease. For this reason, multiple scaffolds, such as substituted coumarins, indolylmethylamine or pyridazine derivatives were synthesized and assayed toward MAO-A and MAO-B inhibition. Our intention is to focus on the description of ligand-based models to provide new insights in the relationship between the MAO inhibitory activity and the molecular structure of the different inhibitors, and further study enzyme selectivity and possible mechanisms of action. PMID:23231398

  7. Real-Time Mesoscale Modeling Over Antarctica: The Antarctic Mesoscale Prediction System*.

    NASA Astrophysics Data System (ADS)

    Powers, Jordan G.; Monaghan, Andrew J.; Cayette, Arthur M.; Bromwich, David H.; Kuo, Ying-Hwa; Manning, Kevin W.

    2003-11-01

    *Byrd Polar Research Center Contribution Number 1276In support of the United States Antarctic Program (USAP), the National Center for Atmospheric Research and the Byrd Polar Research Center of The Ohio State University have created the Antarctic Mesoscale Prediction System (AMPS): an experimental, real-time mesoscale modeling system covering Antarctica. AMPS has been designed to serve flight forecasters at McMurdo Station, to support science and operations around the continent, and to be a vehicle for the development of physical parameterizations suitable for polar regions. Since 2000, AMPS has been producing high-resolution forecasts (grids to 3.3 km) with the “Polar MM5,” a version of the fifth-generation Pennsylvania State University NCAR Mesoscale Model tuned for the polar atmosphere. Beyond its basic mission of serving the USAP flight forecasters at McMurdo, AMPS has assisted both in emergency operations to save lives and in programs to explore the extreme polar environment. The former have included a medical evacuation from the South Pole and a marine rescue from the continental margin. The latter have included scientific field campaigns and the daily activities of international Antarctic forecasters and researchers. The AMPS program has been a success in terms of advancing polar mesoscale NWP, serving critical logistical operations of the USAP, and, most visibly, assisting in emergency rescue missions to save lives. The history and performance of AMPS are described and the successes of this unique real-time mesoscale modeling system in crisis support are detailed.

  8. Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

    PubMed Central

    2011-01-01

    Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of

  9. A community computational challenge to predict the activity of pairs of compounds

    PubMed Central

    Bansal, Mukesh; Yang, Jichen; Karan, Charles; Menden, Michael P; Costello, James C; Tang, Hao; Xiao, Guanghua; Li, Yajuan; Allen, Jeffrey; Zhong, Rui; Chen, Beibei; Kim, Minsoo; Wang, Tao; Heiser, Laura M; Realubit, Ronald; Mattioli, Michela; Alvarez, Mariano J; Shen, Yao; Gallahan, Daniel; Singer, Dinah; Saez-Rodriguez, Julio; Xie, Yang; Stolovitzky, Gustavo; Califano, Andrea

    2015-01-01

    Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction. PMID:25419740

  10. 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…

  11. Practical calculator programs. Part 6. Program predicts drilling time, penetration rate

    SciTech Connect

    Chenevert, M.E.; Hollo, R.

    1981-10-05

    Chenevert's program, designed for the TI-59 programmable calculator, can determine the time (hr) needed to drill a new well to a given depth and the expected penetration rate (ft/hr) at that depth. The program bases its calculations on bit records obtained from an offset well. Extrapolation of the results can predict drilling times for deeper wells.

  12. Trust in health care providers: factors predicting trust among homeless veterans over time.

    PubMed

    van den Berk-Clark, Carissa; McGuire, James

    2014-08-01

    We examined whether a combination of predisposing, enabling, need, and primary care experience variables would predict trust in medical health care providers for homeless veterans over 18 months. Linear mixed model analysis indicated that, among these variables, race, social support, service-connected disability status, and satisfaction and continuity with providers predicted trust in provider over time. Trust in providers improved during the initial stages of the relationship between patient and provider and then declined to slightly below baseline levels over time. Further research is needed to determine generalizability and effects of provider trust on patient health care status over longer periods of time.

  13. The Size of the Expressed HIV Reservoir Predicts Timing of Viral Rebound after Treatment Interruption

    PubMed Central

    Li, Jonathan Z.; Etemad, Behzad; Ahmed, Hayat; Aga, Evgenia; Bosch, Ronald J.; Mellors, John W.; Kuritzkes, Daniel R.; Lederman, Michael M.; Para, Michael; Gandhi, Rajesh T.

    2016-01-01

    Objectives Therapies to achieve sustained antiretroviral therapy-free HIV remission will require validation in analytic treatment interruption (ATI) trials. Identifying biomarkers that predict time to viral rebound could accelerate the development of such therapeutics. Design A pooled analysis of participants from 6 AIDS Clinical Trials Group ATI studies to identify predictors of viral rebound. Methods Cell-associated DNA (CA-DNA) and CA-RNA were quantified in pre-ATI PBMC samples, and residual plasma viremia (RV) was measured using the single-copy assay. Results Participants who initiated ART during acute/early HIV infection and those on an NNRTI-containing regimen had significantly delayed viral rebound. Participants who initiated ART during acute/early infection had lower levels of pre-ATI CA-RNA (acute/early vs. chronic-treated: median <92 vs. 156 HIV-1 RNA copies/106 CD4+ cells, P<0.01). Higher pre-ATI CA-RNA levels were significantly associated with shorter time to viral rebound (≤4 wks vs. 5–8 wks vs. >8 wks: median 182 vs. 107 vs. <92 HIV-1 RNA copies/106 CD4+ cells, Kruskal-Wallis P<0.01). The proportion of participants with detectable RV prior to ATI was significantly higher among those with shorter time to viral rebound. Conclusions Higher levels of HIV expression while on ART are associated with shorter time to HIV rebound after treatment interruption. Quantification of the active HIV reservoir may provide a biomarker of efficacy for therapies that aim to achieve ART-free HIV remission. PMID:26588174

  14. Prediction of half-marathon race time in recreational female and male runners.

    PubMed

    Knechtle, Beat; Barandun, Ursula; Knechtle, Patrizia; Zingg, Matthias A; Rosemann, Thomas; Rüst, Christoph A

    2014-01-01

    Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were the best predictor variables for half-marathon race times in both women and men. The aim of the present study was to improve the existing equations to predict half-marathon race time in a larger sample of male and female half-marathoners by using percent body fat and running speed during training sessions as predictor variables. In a sample of 147 men and 83 women, multiple linear regression analysis including percent body fat and running speed during training units as independent variables and race time as dependent variable were performed and an equation was evolved to predict half-marathon race time. For men, half-marathon race time might be predicted by the equation (r(2) = 0.42, adjusted r(2) = 0.41, SE = 13.3) half-marathon race time (min) = 142.7 + 1.158 × percent body fat (%) - 5.223 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.71, p < 0.0001) to the achieved race time. For women, half-marathon race time might be predicted by the equation (r(2) = 0.68, adjusted r(2) = 0.68, SE = 9.8) race time (min) = 168.7 + 1.077 × percent body fat (%) - 7.556 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.89, p < 0.0001) to the achieved race time. The coefficients of determination of the models were slightly higher than for the existing equations. Future studies might include physiological

  15. Using Time Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit

    PubMed Central

    Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

    2015-01-01

    Objectives To build and test cardiac arrest prediction models in a pediatric intensive care unit, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Methods A retrospective cohort study of pediatric intensive care patients over a 30 month study period. All subjects identified by code documentation sheets with matches in hospital physiologic and laboratory data repositories and who underwent chest compressions for two minutes were included as arrest cases. Controls were randomly selected from patients that did not experience arrest and who survived to discharge. Modeling data was based on twelve hours of data preceding the arrest (reference time for controls). Measurements and Main Results 103 cases of cardiac arrest and 109 control cases were used to prepare a baseline data set that consisted of 1025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve (AUROC). The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% AUROC. Conclusions Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical

  16. Time series predictions with neural nets: Application to airborne pollen forecasting

    NASA Astrophysics Data System (ADS)

    Arizmendi, C. M.; Sanchez, J. R.; Ramos, N. E.; Ramos, G. I.

    1993-09-01

    Pollen allergy is a common disease causing rhinoconjunctivitis (hay fever) in 5 10% of the population. Medical studies have indicated that pollen related diseases could be highly reduced if future pollen contents in the air could be predicted. In this work we have developed a new forecasting method that applies the ability of neural nets to predict the future behaviour of chaotic systems in order to make accurate predictions of the airborne pollen concentration. The method requires that the neural net be fed with non-zero values, which restricts the method predictions to the period following the start of pollen flight. The operational method outlined here constitutes a different point of view with respect to the more generally used forecasts of time series analysis, which require input of many meteorological parameters. Excellent forecasts were obtained training a neural net by using only the time series pollen concentration values.

  17. A method for the time-varying nonlinear prediction of complex nonstationary biomedical signals.

    PubMed

    Faes, Luca; Chon, Ki H; Nollo, Giandomenico

    2009-02-01

    A method to perform time-varying (TV) nonlinear prediction of biomedical signals in the presence of nonstationarity is presented in this paper. The method is based on identification of TV autoregressive models through expansion of the TV coefficients onto a set of basis functions and on k-nearest neighbor local linear approximation to perform nonlinear prediction. The approach provides reasonable nonlinear prediction even for TV deterministic chaotic signals, which has been a daunting task to date. Moreover, the method is used in conjunction with a TV surrogate method to provide statistical validation that the presence of nonlinearity is not due to nonstationarity itself. The approach is tested on simulated linear and nonlinear signals reproducing both time-invariant (TIV) and TV dynamics to assess its ability to quantify TIV and TV degrees of predictability and detect nonlinearity. Applicative examples relevant to heart rate variability and EEG analyses are then illustrated.

  18. Prediction of a time-to-event trait using genome wide SNP data

    PubMed Central

    2013-01-01

    Background A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values. Results In this paper, we present a prediction method for time-to-event traits using genome-wide single-nucleotide polymorphisms (SNPs). We also propose a MaxTest associating between a time-to-event trait and a SNP accounting for its possible genetic models. The proposed MaxTest can help screen out nonprognostic SNPs and identify genetic models of prognostic SNPs. The performance of the proposed method is evaluated through simulations. Conclusions In conjunction with the MaxTest, the proposed method provides more parsimonious prediction models but includes more prognostic SNPs than some naive prediction methods. The proposed method is demonstrated with real GWAS data. PMID:23418752

  19. Neighbourhood selection for local modelling and prediction of hydrological time series

    NASA Astrophysics Data System (ADS)

    Jayawardena, A. W.; Li, W. K.; Xu, P.

    2002-02-01

    The prediction of a time series using the dynamical systems approach requires the knowledge of three parameters; the time delay, the embedding dimension and the number of nearest neighbours. In this paper, a new criterion, based on the generalized degrees of freedom, for the selection of the number of nearest neighbours needed for a better local model for time series prediction is presented. The validity of the proposed method is examined using time series, which are known to be chaotic under certain initial conditions (Lorenz map, Henon map and Logistic map), and real hydro meteorological time series (discharge data from Chao Phraya river in Thailand, Mekong river in Thailand and Laos, and sea surface temperature anomaly data). The predicted results are compared with observations, and with similar predictions obtained by using arbitrarily fixed numbers of neighbours. The results indicate superior predictive capability as measured by the mean square errors and coefficients of variation by the proposed approach when compared with the traditional approach of using a fixed number of neighbours.

  20. Accuracy of patient's turnover time prediction using RFID technology in an academic ambulatory surgery center.

    PubMed

    Marchand-Maillet, Florence; Debes, Claire; Garnier, Fanny; Dufeu, Nicolas; Sciard, Didier; Beaussier, Marc

    2015-02-01

    Patients flow in outpatient surgical unit is a major issue with regards to resource utilization, overall case load and patient satisfaction. An electronic Radio Frequency Identification Device (RFID) was used to document the overall time spent by the patients between their admission and discharge from the unit. The objective of this study was to evaluate how a RFID-based data collection system could provide an accurate prediction of the actual time for the patient to be discharged from the ambulatory surgical unit after surgery. This is an observational prospective evaluation carried out in an academic ambulatory surgery center (ASC). Data on length of stay at each step of the patient care, from admission to discharge, were recorded by a RFID device and analyzed according to the type of surgical procedure, the surgeon and the anesthetic technique. Based on these initial data (n = 1520), patients were scheduled in a sequential manner according to the expected duration of the previous case. The primary endpoint was the difference between actual and predicted time of discharge from the unit. A total of 414 consecutive patients were prospectively evaluated. One hundred seventy four patients (42%) were discharged at the predicted time ± 30 min. Only 24% were discharged behind predicted schedule. Using an automatic record of patient's length of stay would allow an accurate prediction of the discharge time according to the type of surgery, the surgeon and the anesthetic procedure.

  1. Adsorption of selected pharmaceuticals and an endocrine disrupting compound by granular activated carbon. 2. Model prediction

    SciTech Connect

    Yu, Z.; Peldszus, S.; Huck, P.M.

    2009-03-01

    The adsorption of two representative pharmaceutically active compounds (PhACs) naproxen and carbamazepine and one endocrine disrupting compound (EDC) nonylphenol was studied in pilot-scale granular activated carbon (GAC) adsorbers using post-sedimentation (PS) water from a full-scale drinking water treatment plant. The GAC adsorbents were coal-based Calgon Filtrasorb 400 and coconut shell-based PICA CTIF TE. Acidic naproxen broke through fastest while nonylphenol was removed best, which was consistent with the degree to which fouling affected compound removals. Model predictions and experimental data were generally in good agreement for all three compounds, which demonstrated the effectiveness and robustness of the pore and surface diffusion model (PSDM) used in combination with the time-variable parameter approach for predicting removals at environmentally relevant concentrations (i.e., ng/L range). Sensitivity analyses suggested that accurate determination of film diffusion coefficients was critical for predicting breakthrough for naproxen and carbamazepine, in particular when high removals are targeted. Model simulations demonstrated that GAC carbon usage rates (CURs) for naproxen were substantially influenced by the empty bed contact time (EBCT) at the investigated conditions. Model-based comparisons between GAC CURs and minimum CURs for powdered activated carbon (PAC) applications suggested that PAC would be most appropriate for achieving 90% removal of naproxen, whereas GAC would be more suitable for nonylphenol. 25 refs., 4 figs., 1 tab.

  2. Intraseasonal Tropical Storm Activity Prediction with the NCEP CFSv2 45-day Forecasts

    NASA Astrophysics Data System (ADS)

    Schemm, J. K. E.; Long, L. N.

    2015-12-01

    Global predictability of intraseasonal tropical storm (TS) activity is assessed using the 1999-2012 CFSv2 45-day hindcast suite and real-time predictions for 2014-2015. Weekly TS activities in the CFSv2 45-day forecasts were determined using the TS detection and tracking method devised by Carmago and Zebiak (2002). The forecast periods are divided into weekly intervals for Week 1 through Week 4. The TS activities in those intervals are compared to the observed activities based on the NHC HURDAT and JTWC Best Track datasets.The CFSv2 45-day hindcast suite is made of forecast runs initialized at 00, 06, 12 and 18Z every day during the 1999 - 2012 period. For predictability evaluation, forecast TS activities are analyzed based on 20-member ensemble forecasts comprised of 45-day runs made during the most recent 5 days prior to the verification period. The forecast TS activities are evaluated in terms of the number of storms, genesis locations and storm tracks during the weekly periods. The CFSv2 forecasts are shown to have a fair level of skill in predicting the anomalous number of storms over most of the seven ocean basins during the 1999-2012 active seasons. The average temporal correlation score for Week 1 forecasts is between 0.50-0.52 in the Eastern and Western North Pacific, the South Indian and South Pacific basins, while correlations drop to around 0.20 for Week 4 forecasts. The forecast track is also examined using density distribution maps and false alarm statistics compiled using the hindcast analyses with Heidke Skill Scores peaking at around 0.35. Real-time weekly TS activity predictions began in December 2013 using the climatological TS forecast statistics to make the model bias corrections in terms of the storm counts, track distribution and removal of false alarm storms. This operational implementation provides an objective tool for the CPC's Global Tropical Hazards Outlooks. Verification and evaluation of the 2014 and 2015 seasons will be discussed.

  3. Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.

    PubMed

    Ak, Ronay; Fink, Olga; Zio, Enrico

    2016-08-01

    The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

  4. Lifespan Mental Activity Predicts Diminished Rate of Hippocampal Atrophy

    PubMed Central

    Valenzuela, Michael J.; Sachdev, Perminder; Wen, Wei; Chen, Xiaohua; Brodaty, Henry

    2008-01-01

    Objective Epidemiological studies suggest that complex mental activity may reduce the risk for dementia, however an underlying mechanism remains unclear. Our objective was to determine whether individual differences in lifespan complex mental activity are linked to altered rates of hippocampal atrophy independent of global measures of neurodegeneration. Methods Thirty seven healthy older individuals had their complex mental activity levels estimated using the Lifetime of Experiences Questionnaire (LEQ) and completed serial MRI investigations at baseline and three years follow-up. Hippocampal volume and semi-automatic quantitation of whole brain volume (WBV) and white matter hyperintensities (WMHs) were compared at both time points. Results Higher LEQ scores were correlated with hippocampal volume independent of covariates at the three year follow-up stage (r = 0.43, p = 0.012). Moreover, those with higher LEQ scores experienced less hippocampal atrophy over the follow-up period (r = 0.41, p = 0.02). High LEQ individuals had less than half the hippocampal volume decline of low LEQ individuals in a multivariate analysis (F = 4.47, p = 0.042). No parallel changes were found in measures of WBV and WMHs. Conclusions High level of complex mental activity across the lifespan was correlated with a reduced rate of hippocampal atrophy. This finding could not be explained by general differences in intracranial volume, larger hippocampi at baseline, presence of hypertensive disease, gender or low mood. Our results suggest that neuroprotection in medial temporal lobe may be one mechanism underlying the link between mental activity and lower rates of dementia observed in population-based studies. Additional studies are required to further explore this novel finding. PMID:18612379

  5. Predicting Water Activity for Complex Wastes with Solvation Cluster Equilibria (SCE) - 12042

    SciTech Connect

    Agnew, S.F.; Reynolds, J.G.; Johnston, C.T.

    2012-07-01

    Predicting an electrolyte mixture's water activity, i.e. the ratio of water vapor pressure over a solution with that of pure water, in principle reveals both boiling point and solubilities for that mixture. Better predictions of these properties helps support the ongoing missions to concentrate complex nuclear waste mixtures in order to conserve tank space and improved predictions of water activity will help. A new approach for predicting water activity, the solvation cluster equilibria (SCE) model, uses pure electrolyte water activities to predict water activity for a complex mixture of those electrolytes. An SCE function based on electrolyte hydration free energy and a standard Debye- Hueckel (DH) charge compression fits each pure electrolyte's water activity with three parameters. Given these pure electrolyte water activities, the SCE predicts any mixture water activity over a large range of concentration with an additional parameter for each mixture vector, the multinarity. In contrast to ionic strength, which scales with concentration, multinarity is related to the relative proportion of electrolytes in a mixture and can either increase or decrease the water activity prediction over a broad range of concentration for that mixture. The SCE model predicts water activity for complex electrolyte mixtures based on the water activities of pure electrolytes. Three parameter SCE functions fit the water activities of pure electrolytes and along with a single multinarity parameter for each mixture vector then predict the mixture water activity. Predictions of water activity can in principle predict solution electrolyte activity and this relationship will be explored in the future. Predicting electrolyte activities for complex mixtures provides a means of determining solubilities for each electrolyte. Although there are a number of reports [9, 10, 11] of water activity models for pure and binary mixtures of electrolytes, none of them compare measured versus calculated

  6. PSO-MISMO modeling strategy for multistep-ahead time series prediction.

    PubMed

    Bao, Yukun; Xiong, Tao; Hu, Zhongyi

    2014-05-01

    Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

  7. Multivariable time series prediction for the icing process on overhead power transmission line.

    PubMed

    Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

    2014-01-01

    The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters.

  8. Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

    PubMed Central

    Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

    2014-01-01

    The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

  9. Predicting the timing and potential of the spring emergence of overwintered populations of Heliothis spp

    NASA Technical Reports Server (NTRS)

    Hartstack, A. W.; Witz, J. A.; Lopez, J. D. (Principal Investigator)

    1981-01-01

    The current state of knowledge dealing with the prediction of the overwintering population and spring emergence of Heliothis spp., a serious pest of numerous crops is surveyed. Current literature is reviewed in detail. Temperature and day length are the primary factors which program H. spp. larva for possible diapause. Although studies on the interaction of temperature and day length are reported, the complete diapause induction process is not identified sufficiently to allow accurate prediction of diapause timing. Mortality during diapause is reported as highly variable. The factors causing mortality are identified, but only a few are quantified. The spring emergence of overwintering H. spp. adults and mathematical models which predict the timing of emergence are reviewed. Timing predictions compare favorably to observed field data; however, prediction of actual numbers of emerging moths is not possible. The potential for use of spring emergence predictions in pest management applications, as an early warning of potential crop damage, are excellent. Research requirements to develop such an early warning system are discussed.

  10. Long-term prediction of the Arctic ionospheric TEC based on time-varying periodograms.

    PubMed

    Liu, Jingbin; Chen, Ruizhi; Wang, Zemin; An, Jiachun; Hyyppä, Juha

    2014-01-01

    Knowledge of the polar ionospheric total electron content (TEC) and its future variations is of scientific and engineering relevance. In this study, a new method is developed to predict Arctic mean TEC on the scale of a solar cycle using previous data covering 14 years. The Arctic TEC is derived from global positioning system measurements using the spherical cap harmonic analysis mapping method. The study indicates that the variability of the Arctic TEC results in highly time-varying periodograms, which are utilized for prediction in the proposed method. The TEC time series is divided into two components of periodic oscillations and the average TEC. The newly developed method of TEC prediction is based on an extrapolation method that requires no input of physical observations of the time interval of prediction, and it is performed in both temporally backward and forward directions by summing the extrapolation of the two components. The backward prediction indicates that the Arctic TEC variability includes a 9 years period for the study duration, in addition to the well-established periods. The long-term prediction has an uncertainty of 4.8-5.6 TECU for different period sets.

  11. Investigation of Average Prediction Time for Different Meteorological Variables By Using Chaotic Approach

    NASA Astrophysics Data System (ADS)

    Özgür, Evren; Koçak, Kasım

    2016-04-01

    According to nonlinear dynamical system approach, it is possible that the time evolution of a system can be represented by its trajectories in phase space. This phase space is spanned by the state variables which are necessary to determine the time evolution of the system. Atmospheric processes can not be represented by linear approaches because of their dependency on numerous independent variables. Since a small changes in initial conditions lead to significant differences in prediction, long term prediction of meteorological variables is not possible. This situation can be explained by the term "sensitive dependence on initial conditions". In the study, it was tried to determine the average prediction time for different atmospheric variables by applying nonlinear approach. In order to apply the method, the first step is to reconstruct the phase space. Phase space has two parameters which are time delay and embedding dimension. Mutual Information Function (MIF) can be used to determine optimum time delay. MIF considers both linear and nonlinear inner-dependencies in a given time series. To define phase space, embedding dimension must be identified correctly. Embedding dimesion is the number of necessary state variables which describe the dynamics of a system. The algorithm to define embedding dimension is False Nearest Neighbors (FNN). After constructing the phase space by using time delay and embedding dimension, the maximum Lyapunov exponent was introduced. Lyapunov exponent is related to the exponential divergence or convergence of nearby orbits in the phase space. A dynamical system which has positive Lyapunov exponent is defined as chaotic system. Because meteorological variables can be controlled with large numbers of independent variables, time series of meteorological variables might be produced by a chaotic dynamical system. By using phase space and maximum Lyapunov exponent value, average prediction times of different parameters were calculated

  12. Predicting Activation of Experiments Inside the Annular Core Research Reactor

    SciTech Connect

    Greenberg, Joseph Isaac

    2015-11-01

    The objective of this thesis is to create a program to quickly estimate the radioactivity and decay of experiments conducted inside of the Annular Core Research Reactor at Sandia National Laboratories and eliminate the need for users to write code. This is achieved by model the neutron fluxes in the reactor’s central cavity where experiments are conducted for 4 different neutron spectra using MCNP. The desired neutron spectrum, experiment material composition, and reactor power level are then input into CINDER2008 burnup code to obtain activation and decay information for every isotope generated. DREAD creates all of the files required for CINDER2008 through user selected inputs in a graphical user interface and executes the program for the user and displays the resulting estimation for dose rate at various distances. The DREAD program was validated by weighing and measuring various experiments in the different spectra and then collecting dose rate information after they were irradiated and comparing it to the dose rates that DREAD predicted. The program provides results with an average of 17% higher estimates than the actual values and takes seconds to execute.

  13. Electrophysiological correlates of competitor activation predict retrieval-induced forgetting.

    PubMed

    Hellerstedt, Robin; Johansson, Mikael

    2014-06-01

    The very act of retrieval modifies the accessibility of memory for knowledge and past events and can also cause forgetting. A prominent theory of such retrieval-induced forgetting (RIF) holds that retrieval recruits inhibition to overcome interference from competing memories, rendering these memories inaccessible. The present study tested a fundamental tenet of the inhibitory-control account: The competition-dependence assumption. Event-related potentials (ERPs) were recorded while participants engaged in a competitive retrieval task. Competition levels were manipulated within the retrieval task by varying the cue-item associative strength of competing items. In order to temporally separate ERP correlates of competitor activation and target retrieval, memory was probed with the sequential presentation of 2 cues: A category cue, to reactivate competitors, and a target cue. As predicted by the inhibitory-control account, competitors with strong compared with weak cue-competitor association were more susceptible to forgetting. Furthermore, competition-sensitive ERP modulations, elicited by the category cue, were observed over anterior regions and reflected individual differences in ensuing forgetting. The present study demonstrates ERP correlates of the reactivation of tightly bound associated memories (the competitors) and provides support for the inhibitory-control account of RIF.

  14. Extremely Randomized Machine Learning Methods for Compound Activity Prediction.

    PubMed

    Czarnecki, Wojciech M; Podlewska, Sabina; Bojarski, Andrzej J

    2015-11-09

    Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called 'extremely randomized methods'-Extreme Entropy Machine and Extremely Randomized Trees-for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their 'non-extreme' competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

  15. Models to Predict Flowering Time in the Main Saffron Production Regions of Khorasan Province

    NASA Astrophysics Data System (ADS)

    Behdani, M. A.; Koocheki, A.; Nassiri, M.; Rezvani, P.

    The objective of this study was to develop a thermal model that can be used for prediction of saffron flowering time. For this purpose, existing data on saffron flower emergence time were collected in a wide range of temperature regimes over the saffron production regions of Khorasan province, Iran. Linear second-order polynomial and 5-parameter beta models were used and statistically compared for their ability in predicting saffron flowering time as a function of temperature. The results showed a significant delay in flowering date across the temperature gradient. While beta model had a better statistical performance but the simple linear model also showed a good predicting ability and therefore, can be used as a reliable model.

  16. On the feasibility of real-time prediction of aircraft carrier motion at sea

    NASA Technical Reports Server (NTRS)

    Sidar, M. M.; Doolin, B. F.

    1983-01-01

    Landing aircraft on board carriers is a most delicate phase of flight operations at sea. The ability to predict the aircraft carrier's motion over an interval of several seconds within reasonable error bounds may allow an improvement in touchdown dispersion and reduce the value of the ramp clearance due to a smoother aircraft trajectory. Also, improved information to the landing signal officer should decrease the number of waveoffs substantially. This paper indicates and shows quantitatively that, based upon the power density spectrum data for pitch and heave measured for various ships and sea conditions, the motion can be predicted well, for up to 15 s. Moreover, the zero crossover times for both pitch and heave motions can be predicted with impressive accuracy. The predictor was designed on the basis of Kalman's optimum filtering theory (the discrete time case), being compatible with real-time digital computer operation.

  17. On the feasibility of real-time prediction of aircraft carrier motion at sea

    NASA Technical Reports Server (NTRS)

    Sidar, M.; Doolin, B. F.

    1975-01-01

    The ability to predict the aircraft carrier's motion over an interval of several seconds within reasonable error bounds may allow an improvement in touchdown dispersion and a more certain value for ramp clearance due to a smoother aircraft trajectory. Also, improved information to the landing signal officer should decrease the number of waveoffs substantially. It is quantitatively shown that, based on the power density spectrum data for pitch and heave measured for various ships and sea conditions, the motion can be predicted well for up to 15 seconds. The zero crossover times for both pitch and heave motions can be predicted with impressive accuracy. The predictor was designed on the basis of Kalman's optimum filtering theory for the discrete time case, adapted for real-time digital computer operation.

  18. Computational fluid dynamic prediction of the residence time of a vortex separator applied to disinfection.

    PubMed

    Egarr, D; Faram, M G; O'Doherty, T; Phipps, D; Syred, N

    2005-01-01

    A Hydrodynamic Vortex Separator (HDVS) has been modelled using Computational Fluid Dynamics (CFD) in order to predict the residence time of the fluid at the overflow and underflow outlets. A technique which was developed for use in Heating, Ventilation and Air Conditioning (HVAC) was used to determine the residence time and the results have been compared with those determined experimentally. It is shown that in using CFD, it is possible to predict the mean residence time of the fluid and to study the response to a pulse injection of tracer. It is also shown that it is possible to apply these techniques to predict the mean survival rate of bacteria in a combined separation and disinfection process.

  19. Objectively Quantified Physical Activity and Sedentary Behavior in Predicting Visceral Adiposity and Liver Fat

    PubMed Central

    Pavey, Toby G.; Caterson, Ian D.; George, Jacob

    2016-01-01

    Objective. Epidemiologic studies suggest an inverse relationship between nonalcoholic fatty liver disease (NAFLD), visceral adipose tissue (VAT), and self-reported physical activity levels. However, subjective measurements can be inaccurate and prone to reporter bias. We investigated whether objectively quantified physical activity levels predicted liver fat and VAT in overweight/obese adults. Methods. Habitual physical activity was measured by triaxial accelerometry for four days (n = 82). Time spent in sedentary behavior (MET < 1.6) and light (MET 1.6 < 3), moderate (MET 3 < 6), and vigorous (MET 6 < 9) physical activity was quantified. Magnetic resonance imaging and spectroscopy were used to quantify visceral and liver fat. Bivariate correlations and hierarchical multiple regression analyses were performed. Results. There were no associations between physical activity or sedentary behavior and liver lipid. Sedentary behavior and moderate and vigorous physical activity accounted for just 3% of variance for VAT (p = 0.14) and 0.003% for liver fat (p = 0.96). Higher levels of VAT were associated with time spent in moderate activity (r = 0.294, p = 0.007), but there was no association with sedentary behavior. Known risk factors for obesity-related NAFLD accounted for 62% and 40% of variance in VAT and liver fat, respectively (p < 0.01). Conclusion. Objectively measured levels of habitual physical activity and sedentary behavior did not influence VAT or liver fat. PMID:27777796

  20. Trial-to-trial Adaptation: Parsing out the Roles of Cerebellum and BG in Predictive Motor Timing.

    PubMed

    Lungu, Ovidiu V; Bares, Martin; Liu, Tao; Gomez, Christopher M; Cechova, Ivica; Ashe, James

    2016-07-01

    We previously demonstrated that predictive motor timing (i.e., timing requiring visuomotor coordination in anticipation of a future event, such as catching or batting a ball) is impaired in patients with spinocerebellar ataxia (SCA) types 6 and 8 relative to healthy controls. Specifically, SCA patients had difficulties postponing their motor response while estimating the target kinematics. This behavioral difference relied on the activation of both cerebellum and striatum in healthy controls, but not in cerebellar patients, despite both groups activating certain parts of cerebellum during the task. However, the role of these two key structures in the dynamic adaptation of the motor timing to target kinematic properties remained unexplored. In the current paper, we analyzed these data with the aim of characterizing the trial-by-trial changes in brain activation. We found that in healthy controls alone, and in comparison with SCA patients, the activation in bilateral striatum was exclusively associated with past successes and that in the left putamen, with maintaining a successful performance across successive trials. In healthy controls, relative to SCA patients, a larger network was involved in maintaining a successful trial-by-trial strategy; this included cerebellum and fronto-parieto-temporo-occipital regions that are typically part of attentional network and action monitoring. Cerebellum was also part of a network of regions activated when healthy participants postponed their motor response from one trial to the next; SCA patients showed reduced activation relative to healthy controls in both cerebellum and striatum in the same contrast. These findings support the idea that cerebellum and striatum play complementary roles in the trial-by-trial adaptation in predictive motor timing. In addition to expanding our knowledge of brain structures involved in time processing, our results have implications for the understanding of BG disorders, such as Parkinson disease

  1. Predictive role of physical education motivation: the developmental trajectories of physical activity during grades 7-9.

    PubMed

    Yli-Piipari, Sami; Leskinen, Esko; Jaakkola, Timo; Liukkonen, Jarmo

    2012-12-01

    In this study we examined adolescents' physical activity (PA) stability as well as individual differences in PA across grades 7-9. In addition, we tested the predictive role of adolescents' self-determination and goal orientations in physical education at grade 6 in relation to their PA development. Adolescents' (N = 812) self-reported PA was measured five times and physical education motivation once. Results revealed a decreasing trajectory of PA, with individual differences diminishing over time. Although boys' showed a consistently higher PA level, gender did not moderate the change in PA. Finally, self-determination and task orientation (boys only) predicted PA levels but did not predict change in PA.

  2. Time-dependent damage in predictions of fatigue behaviour of normal and healing ligaments

    NASA Astrophysics Data System (ADS)

    Thornton, Gail M.; Bailey, Soraya J.; Schwab, Timothy D.

    2015-08-01

    Ligaments are dense fibrous tissues that connect bones across a joint and are exposed daily to creep and fatigue loading. Ligaments are tensile load-bearing tissues; therefore, fatigue loading will have a component of time-dependent damage from the non-zero mean stress and cycle-dependent damage from the oscillating stress. If time-dependent damage is not sufficient to completely predict the fatigue response, then cycle-dependent damage could be an important contributor. Using data from normal ligaments (current study and Thornton et al., Clin. Biomech. 22:932-940, 2007a) and healing ligaments (Thornton and Bailey, J. Biomech. Eng. 135:091004-1-091004-6, 2013), creep data was used to predict the fatigue response considering time-dependent damage. Relationships between creep lifetime and test stress or initial strain were modelled using exponential or power-law regression. In order to predict fatigue lifetimes, constant rates of damage were assumed and time-varying stresses were introduced into the expressions for time-dependent damage from creep. Then, the predictions of fatigue lifetime were compared with curvefits to the fatigue data where exponential or power-law regressions were used to determine the relationship between fatigue lifetime and test stress or initial strain. The fatigue prediction based on time-dependent damage alone greatly overestimated fatigue lifetime suggesting that time-dependent damage alone cannot account for all of the damage accumulated during fatigue and that cycle-dependent damage has an important role. At lower stress and strain, time-dependent damage was a greater relative contributor for normal ligaments than healing ligaments; however, cycle-dependent damage was a greater relative contributor with incremental increases in stress or strain for normal ligaments than healing ligaments.

  3. Utilizing Selected Di- and Trinucleotides of siRNA to Predict RNAi Activity

    PubMed Central

    Han, Ye; Liu, Yuanning; Zhang, Hao; He, Fei; Shu, Chonghe

    2017-01-01

    Small interfering RNAs (siRNAs) induce posttranscriptional gene silencing in various organisms. siRNAs targeted to different positions of the same gene show different effectiveness; hence, predicting siRNA activity is a crucial step. In this paper, we developed and evaluated a powerful tool named “siRNApred” with a new mixed feature set to predict siRNA activity. To improve the prediction accuracy, we proposed 2-3NTs as our new features. A Random Forest siRNA activity prediction model was constructed using the feature set selected by our proposed Binary Search Feature Selection (BSFS) algorithm. Experimental data demonstrated that the binding site of the Argonaute protein correlates with siRNA activity. “siRNApred” is effective for selecting active siRNAs, and the prediction results demonstrate that our method can outperform other current siRNA activity prediction methods in terms of prediction accuracy. PMID:28243313

  4. Optimal Futility Interim Design: A Predictive Probability of Success Approach with Time-to-Event Endpoint.

    PubMed

    Tang, Zhongwen

    2015-01-01

    An analytical way to compute predictive probability of success (PPOS) together with credible interval at interim analysis (IA) is developed for big clinical trials with time-to-event endpoints. The method takes account of the fixed data up to IA, the amount of uncertainty in future data, and uncertainty about parameters. Predictive power is a special type of PPOS. The result is confirmed by simulation. An optimal design is proposed by finding optimal combination of analysis time and futility cutoff based on some PPOS criteria.

  5. Predicting retention time shifts associated with variation of the gradient slope in peptide RP-HPLC.

    PubMed

    Spicer, Vic; Grigoryan, Marine; Gotfrid, Alexander; Standing, Kenneth G; Krokhin, Oleg V

    2010-12-01

    We have developed a sequence-specific model for predicting slopes (S) in the fundamental equation of linear solvent strength theory for the reversed-phase HPLC separation of tryptic peptides detected in a typical bottom-up-proteomics experiment. These slopes control the variation in the separation selectivity observed when the physical parameters of chromatographic separation, such as gradient slope, flow rate, and column size are altered. For example, with the use of an arbitrarily chosen set of tryptic peptides with a 4-times difference in the gradient slope between two experiments, the R(2)-value of correlation between the observed retention times of identical species decreases to ~0.993-0.996 (compared to a theoretical value of ~1.00). The observed retention time shifts associated with variations of the gradient slope can be predicted a priori using the approach described here. The proposed model is based on our findings for a set of synthetic species (Vu, H.; Spicer, V.; Gotfrid, A.; Krokhin, O. V. J. Chromatogr., A, 2010, 1217, 489-497), which postulate that slopes S can be predicted taking into account simultaneously peptide length, charge, and hydrophobicity. Here we extend this approach using an extensive set of real tryptic peptides. We developed the procedure to accurately measure S-values in nano-RP HPLC MS experiments and introduced sequence-specific corrections for a more accurate prediction of the slopes S. A correlation of ~0.95 R(2)-value between the predicted and experimental S-values was demonstrated. Predicting S-values and calculating the expected retention time shifts when the physical parameters of separation like gradient slope are altered will facilitate a more accurate application of peptide retention prediction protocols, aid in the transfer of scheduled MRM (SRM) procedures between LC systems, and increase the efficiency of interlaboratory data collection and comparison.

  6. Personal best times in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male triathletes

    PubMed Central

    Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Rosemann, Thomas; Lepers, Romuald

    2011-01-01

    Background The purpose of this study was to define predictor variables for recreational male Ironman triathletes, using age and basic measurements of anthropometry, training, and previous performance to establish an equation for the prediction of an Ironman race time for future recreational male Ironman triathletes. Methods Age and anthropometry, training, and previous experience variables were related to Ironman race time using bivariate and multivariate analysis. Results A total of 184 recreational male triathletes, of mean age 40.9 ± 8.4 years, height 1.80 ± 0.06 m, and weight 76.3 ± 8.4 kg completed the Ironman within 691 ± 83 minutes. They spent 13.9 ± 5.0 hours per week in training, covering 6.3 ± 3.1 km of swimming, 194.4 ± 76.6 km of cycling, and 45.0 ± 15.9 km of running. In total, 149 triathletes had completed at least one marathon, and 150 athletes had finished at least one Olympic distance triathlon. They had a personal best time of 130.4 ± 44.2 minutes in an Olympic distance triathlon and of 193.9 ± 31.9 minutes in marathon running. In total, 126 finishers had completed both an Olympic distance triathlon and a marathon. After multivariate analysis, both a personal best time in a marathon (P < 0.0001) and in an Olympic distance triathlon (P < 0.0001) were the best variables related to Ironman race time. Ironman race time (minutes) might be partially predicted by the following equation: (r2 = 0.65, standard error of estimate = 56.8) = 152.1 + 1.332 × (personal best time in a marathon, minutes) + 1.964 × (personal best time in an Olympic distance triathlon, minutes). Conclusion These results suggest that, in contrast with anthropometric and training characteristics, both the personal best time in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male Ironman triathletes. PMID:24198578

  7. MO-G-18C-05: Real-Time Prediction in Free-Breathing Perfusion MRI

    SciTech Connect

    Song, H; Liu, W; Ruan, D; Jung, S; Gach, M

    2014-06-15

    Purpose: The aim is to minimize frame-wise difference errors caused by respiratory motion and eliminate the need for breath-holds in magnetic resonance imaging (MRI) sequences with long acquisitions and repeat times (TRs). The technique is being applied to perfusion MRI using arterial spin labeling (ASL). Methods: Respiratory motion prediction (RMP) using navigator echoes was implemented in ASL. A least-square method was used to extract the respiratory motion information from the 1D navigator. A generalized artificial neutral network (ANN) with three layers was developed to simultaneously predict 10 time points forward in time and correct for respiratory motion during MRI acquisition. During the training phase, the parameters of the ANN were optimized to minimize the aggregated prediction error based on acquired navigator data. During realtime prediction, the trained ANN was applied to the most recent estimated displacement trajectory to determine in real-time the amount of spatial Results: The respiratory motion information extracted from the least-square method can accurately represent the navigator profiles, with a normalized chi-square value of 0.037±0.015 across the training phase. During the 60-second training phase, the ANN successfully learned the respiratory motion pattern from the navigator training data. During real-time prediction, the ANN received displacement estimates and predicted the motion in the continuum of a 1.0 s prediction window. The ANN prediction was able to provide corrections for different respiratory states (i.e., inhalation/exhalation) during real-time scanning with a mean absolute error of < 1.8 mm. Conclusion: A new technique enabling free-breathing acquisition during MRI is being developed. A generalized ANN development has demonstrated its efficacy in predicting a continuum of motion profile for volumetric imaging based on navigator inputs. Future work will enhance the robustness of ANN and verify its effectiveness with human

  8. Predicting brain activation patterns associated with individual lexical concepts based on five sensory-motor attributes

    PubMed Central

    Fernandino, Leonardo; Humphries, Colin J.; Seidenberg, Mark S.; Gross, William L.; Conant, Lisa L.; Binder, Jeffrey R.

    2015-01-01

    While major advances have been made in uncovering the neural processes underlying perceptual representations, our grasp of how the brain gives rise to conceptual knowledge remains relatively poor. Recent work has provided strong evidence that concepts rely, at least in part, on the same sensory and motor neural systems through which they were acquired, but it is still unclear whether the neural code for concept representation uses information about sensory-motor features to discriminate between concepts. In the present study, we investigate this question by asking whether an encoding model based on five semantic attributes directly related to sensory-motor experience – sound, color, visual motion, shape, and manipulation – can successfully predict patterns of brain activation elicited by individual lexical concepts. We collected ratings on the relevance of these five attributes to the meaning of 820 words, and used these ratings as predictors in a multiple regression model of the fMRI signal associated with the words in a separate group of participants. The five resulting activation maps were then combined by linear summation to predict the distributed activation pattern elicited by a novel set of 80 test words. The encoding model predicted the activation patterns elicited by the test words significantly better than chance. As expected, prediction was successful for concrete but not for abstract concepts. Comparisons between encoding models based on different combinations of attributes indicate that all five attributes contribute to the representation of concrete concepts. Consistent with embodied theories of semantics, these results show, for the first time, that the distributed activation pattern associated with a concept combines information about different sensory-motor attributes according to their respective relevance. Future research should investigate how additional features of phenomenal experience contribute to the neural representation of conceptual

  9. STRUCTURE-ACTIVITY RELATIONSHIP STUIDES AND THEIR ROLE IN PREDICTING AND INVESTIGATING CHEMICAL TOXICITY

    EPA Science Inventory

    Structure-Activity Relationship Studies and their Role in Predicting and Investigating Chemical Toxicity

    Structure-activity relationships (SAR) represent attempts to generalize chemical information relative to biological activity for the twin purposes of generating insigh...

  10. Window-Based Channel Impulse Response Prediction for Time-Varying Ultra-Wideband Channels

    PubMed Central

    Al-Samman, A. M.; Azmi, M. H.; Rahman, T. A.; Khan, I.; Hindia, M. N.; Fattouh, A.

    2016-01-01

    This work proposes channel impulse response (CIR) prediction for time-varying ultra-wideband (UWB) channels by exploiting the fast movement of channel taps within delay bins. Considering the sparsity of UWB channels, we introduce a window-based CIR (WB-CIR) to approximate the high temporal resolutions of UWB channels. A recursive least square (RLS) algorithm is adopted to predict the time evolution of the WB-CIR. For predicting the future WB-CIR tap of window wk, three RLS filter coefficients are computed from the observed WB-CIRs of the left wk−1, the current wk and the right wk+1 windows. The filter coefficient with the lowest RLS error is used to predict the future WB-CIR tap. To evaluate our proposed prediction method, UWB CIRs are collected through measurement campaigns in outdoor environments considering line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Under similar computational complexity, our proposed method provides an improvement in prediction errors of approximately 80% for LOS and 63% for NLOS scenarios compared with a conventional method. PMID:27992445

  11. Jump neural network for real-time prediction of glucose concentration.

    PubMed

    Zecchin, Chiara; Facchinetti, Andrea; Sparacino, Giovanni; Cobelli, Claudio

    2015-01-01

    Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming from different domains. In this chapter we illustrate the design of a jump NN glucose prediction algorithm that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic alerts and the improvement of artificial pancreas performance.

  12. Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score

    NASA Astrophysics Data System (ADS)

    Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G.

    2014-09-01

    The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

  13. Combinatorial modeling of chromatin features quantitatively predicts DNA replication timing in Drosophila.

    PubMed

    Comoglio, Federico; Paro, Renato

    2014-01-01

    In metazoans, each cell type follows a characteristic, spatio-temporally regulated DNA replication program. Histone modifications (HMs) and chromatin binding proteins (CBPs) are fundamental for a faithful progression and completion of this process. However, no individual HM is strictly indispensable for origin function, suggesting that HMs may act combinatorially in analogy to the histone code hypothesis for transcriptional regulation. In contrast to gene expression however, the relationship between combinations of chromatin features and DNA replication timing has not yet been demonstrated. Here, by exploiting a comprehensive data collection consisting of 95 CBPs and HMs we investigated their combinatorial potential for the prediction of DNA replication timing in Drosophila using quantitative statistical models. We found that while combinations of CBPs exhibit moderate predictive power for replication timing, pairwise interactions between HMs lead to accurate predictions genome-wide that can be locally further improved by CBPs. Independent feature importance and model analyses led us to derive a simplified, biologically interpretable model of the relationship between chromatin landscape and replication timing reaching 80% of the full model accuracy using six model terms. Finally, we show that pairwise combinations of HMs are able to predict differential DNA replication timing across different cell types. All in all, our work provides support to the existence of combinatorial HM patterns for DNA replication and reveal cell-type independent key elements thereof, whose experimental investigation might contribute to elucidate the regulatory mode of this fundamental cellular process.

  14. Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction

    PubMed Central

    Frimayanti, Neni; Yam, Mun Li; Lee, Hong Boon; Othman, Rozana; Zain, Sharifuddin M.; Rahman, Noorsaadah Abd.

    2011-01-01

    Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r2 value, r2 (CV) value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set. PMID:22272096

  15. The short-term prediction of universal time and length of day using atmospheric angular momentum

    NASA Technical Reports Server (NTRS)

    Freedman, A. P.; Steppe, J. A.; Dickey, J. O.; Eubanks, T. M.; Sung, L.-Y.

    1994-01-01

    The ability to predict short-term variations in the Earth's rotation has gained importance in recent years owing to more precise spacecraft tracking requirements. Universal time (UT1), that component of the Earth's orientation corresponding to the rotation angle, can be measured by number of high-precision space geodetic techniques. A Kalman filter developed at the Jet Propulsion Laboratory (JPL) optimally combines these different data sets and generates a smoothed times series and a set of predictions for UT1, as well as for additional Earth orientation components. These UT1 predictions utilize an empirically derived random walk stochastic model for the length of the day (LOD) and require frequent and up-to-date measurements of either UT1 or LOD to keep errors from quickly accumulating. Recent studies have shown that LOD variations are correlated with changes in the Earth's axial atmospheric angular momentum (AAM) over timescales of several years down to as little as 8 days. AAM estimates and forecasts out to 10 days are routinely available from meteorological analysis centers; these data can supplement geodetic measurements to improve the short-term prediction of LOD and have therefore been incorporated as independent data types in the JPL Kalman filter. We find that AAM and, to a lesser extent, AAM forecast data are extremely helpful in generating accurate near-real-time estimates of UT1 and LOD and in improving short-term predictions of these quantities out to about 10 days.

  16. A multi-time scale approach to remaining useful life prediction in rolling bearing

    NASA Astrophysics Data System (ADS)

    Qian, Yuning; Yan, Ruqiang; Gao, Robert X.

    2017-01-01

    This paper presents a novel multi-time scale approach to bearing defect tracking and remaining useful life (RUL) prediction, which integrates enhanced phase space warping (PSW) with a modified Paris crack growth model. As a data-driven method, PSW describes the dynamical behavior of the bearing being tested on a fast-time scale, whereas the Paris crack growth model, as a physics-based model, characterizes the bearing's defect propagation on a slow-time scale. Theoretically, PSW constructs a tracking metric by evaluating the phase space trajectory warping of the bearing vibration data, and establishes a correlation between measurement on a fast-time scale and defect growth variables on a slow-time scale. Furthermore, PSW is enhanced by a multi-dimensional auto-regression (AR) model for improved accuracy in defect tracking. Also, the Paris crack growth model is modified by a time-piecewise algorithm for real-time RUL prediction. Case studies performed on two run-to-failure experiments indicate that the developed technique is effective in tracking the evolution of bearing defects and accurately predict the bearing RUL, thus contributing to the literature of bearing prognosis .

  17. Reliable activation to novel stimuli predicts higher fluid intelligence.

    PubMed

    Euler, Matthew J; Weisend, Michael P; Jung, Rex E; Thoma, Robert J; Yeo, Ronald A

    2015-07-01

    The ability to reliably respond to stimuli could be an important biological determinant of differences in fluid intelligence (Gf). However, most electrophysiological studies of Gf employ event-related potential (ERP) measures that average brain activity over trials, and hence have limited power to quantify neural variability. Time-frequency analyses can capture cross-trial variation in the phase of neural activity, and thus can help address the importance of neural reliability to differences in Gf. This study recruited a community sample of healthy adults and measured inter-trial phase clustering (ITPC), total spectral power, and ERP amplitudes elicited by Repeated and Novel non-target stimuli during two visual oddball tasks. Condition effects, relations among the EEG measures, and relations with Gf were assessed. Early visual responses to Repeated stimuli elicited higher ITPC, yet only ITPC elicited by Novel stimuli was associated with Gf. Analyses of spectral power further highlighted the contribution of phase consistency to the findings. The link between Gf and reliable responding to changing inputs suggests an important role for flexible resource allocation in fluid intellectual skills.

  18. Least squares support vector machine for short-term prediction of meteorological time series

    NASA Astrophysics Data System (ADS)

    Mellit, A.; Pavan, A. Massi; Benghanem, M.

    2013-01-01

    The prediction of meteorological time series plays very important role in several fields. In this paper, an application of least squares support vector machine (LS-SVM) for short-term prediction of meteorological time series (e.g. solar irradiation, air temperature, relative humidity, wind speed, wind direction and pressure) is presented. In order to check the generalization capability of the LS-SVM approach, a K-fold cross-validation and Kolmogorov-Smirnov test have been carried out. A comparison between LS-SVM and different artificial neural network (ANN) architectures (recurrent neural network, multi-layered perceptron, radial basis function and probabilistic neural network) is presented and discussed. The comparison showed that the LS-SVM produced significantly better results than ANN architectures. It also indicates that LS-SVM provides promising results for short-term prediction of meteorological data.

  19. Ecological prediction with nonlinear multivariate time-frequency functional data models

    USGS Publications Warehouse

    Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

    2013-01-01

    Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

  20. Time Perspectives Predict Mood States and Satisfaction with Life over and above Personality.

    PubMed

    Stolarski, Maciej; Matthews, Gerald

    2016-01-01

    The present study aimed to test the incremental validity of Time Perspective (TP) scales in predicting satisfaction with life and mood, over and above the Big Five personality traits. It also investigated whether the new TP construct of Future Negative perspective contributed to prediction of these outcomes. Participants (N = 265) completed four measures: Satisfaction With Life Scale (SWLS), UWIST Mood Adjective Checklist (UMACL), a modified Zimbardo Time Perspective Inventory (ZTPI), and NEO-Five Factor Inventory (NEO-FFI). Results confirmed the incremental validity of TP, although Big Five dimensions were independently predictive of life satisfaction and certain mood scales. Past Negative TP was the strongest single predictor of life satisfaction. However, Future Negative TP was be the strongest mood predictor from the TP universe, after controlling for the Big Five and remaining TP dimensions. Findings suggest that TP is an important aspect of personality for understanding individual differences in well-being.

  1. Time history prediction of direct-drive implosions on the Omega facility

    SciTech Connect

    Laffite, S.; Bourgade, J. L.; Caillaud, T.; Delettrez, J A; Frenje, J. A.; Girard, F.; Glebov, V. Yu.; Joshi, Tirtha Raj; Landoas, O.; Legay, G.; Lemaire, S.; Mancini, R. C.; Marshall, F. J.; Masse, L.; Masson-Laborde, P. E.; Michel, D. T.; Philippe, F.; Reverdin, C.; Seka, W.; Tassin, V.

    2016-01-14

    We present in this article direct-drive experiments that were carried out on the Omega facility [T. R. Boehly et al., Opt. Commun. 133, 495 (1997)]. Two different pulse shapes were tested in order to vary the implosion stability of the same target whose parameters, dimensions and composition, remained the same. The direct-drive configuration on the Omega facility allows the accurate time-resolvedmeasurement of the scattered light. We show that, provided the laser coupling is well controlled, the implosion time history, assessed by the “bang-time” and the shell trajectory measurements, can be predicted. This conclusion is independent on the pulse shape. In contrast, we show that the pulse shape affects the implosion stability, assessed by comparing the target performances between prediction and measurement. For the 1-ns square pulse, the measuredneutron number is about 80% of the prediction. Lastly, for the 2-step 2-ns pulse, we test here that this ratio falls to about 20%.

  2. American Time Use Survey: Sleep Time and Its Relationship to Waking Activities

    PubMed Central

    Basner, Mathias; Fomberstein, Kenneth M.; Razavi, Farid M.; Banks, Siobhan; William, Jeffrey H.; Rosa, Roger R.; Dinges, David F.

    2007-01-01

    Study Objectives: To gain some insight into how various behavioral (lifestyle) factors influence sleep duration, by investigation of the relationship of sleep time to waking activities using the American Time Use Survey (ATUS). Design: Cross-sectional data from ATUS, an annual telephone survey of a population sample of US citizens who are interviewed regarding how they spent their time during a 24-hour period between 04:00 on the previous day and 04:00 on the interview day. Participants: Data were pooled from the 2003, 2004, and 2005 ATUS databases involving N=47,731 respondents older than 14 years of age. Interventions: N/A Results: Adjusted multiple linear regression models showed that the largest reciprocal relationship to sleep was found for work time, followed by travel time, which included commute time. Only shorter than average sleepers (<7.5 h) spent more time socializing, relaxing, and engaging in leisure activities, while both short (<5.5 h) and long sleepers (≥8.5 h) watched more TV than the average sleeper. The extent to which sleep time was exchanged for waking activities was also shown to depend on age and gender. Sleep time was minimal while work time was maximal in the age group 45–54 yr, and sleep time increased both with lower and higher age. Conclusions: Work time, travel time, and time for socializing, relaxing, and leisure are the primary activities reciprocally related to sleep time among Americans. These activities may be confounding the frequently observed association between short and long sleep on one hand and morbidity and mortality on the other hand and should be controlled for in future studies. Citation: Basner M; Fomberstein KM; Razavi FM; Banks S; William JH; Rosa RR; Dinges DF. American time use survey: sleep time and its relationship to waking activities. SLEEP 2007;30(9):1085-1095. PMID:17910380

  3. AL and Dst Predictions with the Real-Time WINDMI Model

    NASA Astrophysics Data System (ADS)

    Mays, L.; Horton, W.; Spencer, E.; Weigel, R.; Vassiliadis, D.; Kozyra, J.

    2006-12-01

    First results are presented of the space weather forecasting capability of the real-time WINDMI model that has been operating since February 2006 as a physics based AL and Dst prediction tool. The well documented WINDMI model is a network of eight coupled ordinary differential equations which describe the transfer of power from the solar wind through the geomagnetic tail, the ionosphere, and ring current in the solar WIND driven Magnetosphere-Ionosphere system. WINDMI includes ring current energization physics from substrom injections and outputs a predicted westward auroral electojet index (AL) and equatorial geomagnetic disturbance storm time index (Dst). At the time of abstract submission (August 2006) real-time WINDMI has captured two storms with the first alarm being sent by email for a moderate -150 nT storm on 14-15 April 2006 and a second -100 nT storm on 19-20 August 2006. During the August 2006 storm period the WINDMI model was a more consistent Dst predictor than the Kyoto WDC Quicklook Dst data which has an incorrect offset of ~-100 nT. Real-time WINDMI uses real-time solar wind data from received from ACE every ten minutes to derive in less than one minute of computational time a predicted AL and Dst and magnetopause standoff distance. Real-time WINDMI predicts the AL index one hour earlier than the data is available from the Kyoto WDC Quicklook website and the Dst index two hours earlier. Every ten minutes real-time AL and Dst data and WINDMI predictions are shown on this website: http://orion.ph.utexas.edu/~windmi/realtime/. The 18 physical parameters of WINDMI are approximated analytically from planetary parameters and optimized within physically allowable ranges using the genetic algorithm. Real-time WINDMI parameters are optimized every hour based on 8 hours of past model/data comparison. In addition to the geomagnetic indices the model predicts the major energy components and power transfers in the solar wind-magnetosphere-ionosphere system. The

  4. Running speed during training and percent body fat predict race time in recreational male marathoners

    PubMed Central

    Barandun, Ursula; Knechtle, Beat; Knechtle, Patrizia; Klipstein, Andreas; Rüst, Christoph Alexander; Rosemann, Thomas; Lepers, Romuald

    2012-01-01

    Background Recent studies have shown that personal best marathon time is a strong predictor of race time in male ultramarathoners. We aimed to determine variables predictive of marathon race time in recreational male marathoners by using the same characteristics of anthropometry and training as used for ultramarathoners. Methods Anthropometric and training characteristics of 126 recreational male marathoners were bivariately and multivariately related to marathon race times. Results After multivariate regression, running speed of the training units (β = −0.52, P < 0.0001) and percent body fat (β = 0.27, P < 0.0001) were the two variables most strongly correlated with marathon race times. Marathon race time for recreational male runners may be estimated to some extent by using the following equation (r2 = 0.44): race time ( minutes) = 326.3 + 2.394 × (percent body fat, %) − 12.06 × (speed in training, km/hours). Running speed during training sessions correlated with prerace percent body fat (r = 0.33, P = 0.0002). The model including anthropometric and training variables explained 44% of the variance of marathon race times, whereas running speed during training sessions alone explained 40%. Thus, training speed was more predictive of marathon performance times than anthropometric characteristics. Conclusion The present results suggest that low body fat and running speed during training close to race pace (about 11 km/hour) are two key factors for a fast marathon race time in recreational male marathoner runners. PMID:24198587

  5. Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks.

    PubMed

    Zhou, Renjie; Yang, Chen; Wan, Jian; Zhang, Wei; Guan, Bo; Xiong, Naixue

    2017-04-06

    Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques.

  6. Time-Perception Network and Default Mode Network Are Associated with Temporal Prediction in a Periodic Motion Task

    PubMed Central

    Carvalho, Fabiana M.; Chaim, Khallil T.; Sanchez, Tiago A.; de Araujo, Draulio B.

    2016-01-01

    The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and bilateral posterior cingulate cortex/precuneus (PCC/PC). It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may

  7. Development and implementation of a real-time 30-day readmission predictive model.

    PubMed

    Cronin, Patrick R; Greenwald, Jeffrey L; Crevensten, Gwen C; Chueh, Henry C; Zai, Adrian H

    2014-01-01

    Hospitals are under great pressure to reduce readmissions of patients. Being able to reliably predict patients at increased risk for rehospitalization would allow for tailored interventions to be offered to them. This requires the creation of a functional predictive model specifically designed to support real-time clinical operations. A predictive model for readmissions within 30 days of discharge was developed using retrospective data from 45,924 MGH admissions between 2/1/2012 and 1/31/2013 only including factors that would be available by the day after admission. It was then validated prospectively in a real-time implementation for 3,074 MGH admissions between 10/1/2013 and 10/31/2013. The model developed retrospectively had an AUC of 0.705 with good calibration. The real-time implementation had an AUC of 0.671 although the model was overestimating readmission risk. A moderately discriminative real-time 30-day readmission predictive model can be developed and implemented in a large academic hospital.

  8. Observations and predictions of eclipse times by astronomers in the pre-telescopic period.

    NASA Astrophysics Data System (ADS)

    Steele, J. M.

    Eclipses of the Sun and Moon are among the most impressive of celestial events. It is therefore unsurprising that they have played an important role in the astronomy and astrology of most early cultures. Many hundreds of references to eclipses are found in the writings of the chroniclers and astronomers of the pre-telescopic world. In particular, the astronomers of Babylon, Ancient Greece, the Islamic Near East. Later Medieval and Renaissance Europe, China, and Japan, recorded a large number of observations and predictions of the time of an eclipse. The present study contains an extensive compilation of all known timed reports of eclipse observations and predictions made by astronomers in the pre-telescopic period. By performing a basic analysis of the recorded times, it has been possible to trace the gradual development of the techniques used by the astronomers to observe and predict eclipses. In order to conduct this analysis, it has been necessary to investigate a number of other problems including the dating of damaged observational accounts, the units of time used by the early astronomers, and the methods by which the Babylonians predicted eclipses. Many of these questions have not previously been answered. Therefore, the results of this study provide important information regarding the astronomies of these early cultures.

  9. Temporal Characteristics of the Predictive Synchronous Firing Modeled by Spike-Timing-Dependent Plasticity

    ERIC Educational Resources Information Center

    Kitano, Katsunori; Fukai, Tomoki

    2004-01-01

    When a sensory cue was repeatedly followed by a behavioral event with fixed delays, pairs of premotor and primary motor neurons showed significant increases of coincident spikes at times a monkey was expecting the event. These results provided evidence that neuronal firing synchrony has predictive power. To elucidate the underlying mechanism, here…

  10. Predicting the Risk of Attrition for Undergraduate Students with Time Based Modelling

    ERIC Educational Resources Information Center

    Chai, Kevin E. K.; Gibson, David

    2015-01-01

    Improving student retention is an important and challenging problem for universities. This paper reports on the development of a student attrition model for predicting which first year students are most at-risk of leaving at various points in time during their first semester of study. The objective of developing such a model is to assist…

  11. Prediction problem for target events based on the inter-event waiting time

    NASA Astrophysics Data System (ADS)

    Shapoval, A.

    2010-11-01

    In this paper we address the problem of forecasting the target events of a time series given the distribution ξ of time gaps between target events. Strong earthquakes and stock market crashes are the two types of such events that we are focusing on. In the series of earthquakes, as McCann et al. show [W.R. Mc Cann, S.P. Nishenko, L.R. Sykes, J. Krause, Seismic gaps and plate tectonics: seismic potential for major boundaries, Pure and Applied Geophysics 117 (1979) 1082-1147], there are well-defined gaps (called seismic gaps) between strong earthquakes. On the other hand, usually there are no regular gaps in the series of stock market crashes [M. Raberto, E. Scalas, F. Mainardi, Waiting-times and returns in high-frequency financial data: an empirical study, Physica A 314 (2002) 749-755]. For the case of seismic gaps, we analytically derive an upper bound of prediction efficiency given the coefficient of variation of the distribution ξ. For the case of stock market crashes, we develop an algorithm that predicts the next crash within a certain time interval after the previous one. We show that this algorithm outperforms random prediction. The efficiency of our algorithm sets up a lower bound of efficiency for effective prediction of stock market crashes.

  12. Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration.

    PubMed

    Zecchin, Chiara; Facchinetti, Andrea; Sparacino, Giovanni; De Nicolao, Giuseppe; Cobelli, Claudio

    2012-06-01

    Diabetes mellitus is one of the most common chronic diseases, and a clinically important task in its management is the prevention of hypo/hyperglycemic events. This can be achieved by exploiting continuous glucose monitoring (CGM) devices and suitable short-term prediction algorithms able to infer future glycemia in real time. In the literature, several methods for short-time glucose prediction have been proposed, most of which do not exploit information on meals, and use past CGM readings only. In this paper, we propose an algorithm for short-time glucose prediction using past CGM sensor readings and information on carbohydrate intake. The predictor combines a neural network (NN) model and a first-order polynomial extrapolation algorithm, used in parallel to describe, respectively, the nonlinear and the linear components of glucose dynamics. Information on the glucose rate of appearance after a meal is described by a previously published physiological model. The method is assessed on 20 simulated datasets and on 9 real Abbott FreeStyle Navigator datasets, and its performance is successfully compared with that of a recently proposed NN glucose predictor. Results suggest that exploiting meal information improves the accuracy of short-time glucose prediction.

  13. Time Critical Targeting: Predictive Vs Reactionary Methods An Analysis For The Future

    DTIC Science & Technology

    2002-06-01

    Chapter 5 Results & Conclusions Having investigated the different methods and techniques that can be used for time critical targeting in the......Targeting: Predictive Vs Reactionary Methods An Analysis For The Future 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S

  14. Development of VIS/NIR spectroscopic system for real-time prediction of fresh pork quality

    NASA Astrophysics Data System (ADS)

    Zhang, Haiyun; Peng, Yankun; Zhao, Songwei; Sasao, Akira

    2013-05-01

    Quality attributes of fresh meat will influence nutritional value and consumers' purchasing power. The aim of the research was to develop a prototype for real-time detection of quality in meat. It consisted of hardware system and software system. A VIS/NIR spectrograph in the range of 350 to 1100 nm was used to collect the spectral data. In order to acquire more potential information of the sample, optical fiber multiplexer was used. A conveyable and cylindrical device was designed and fabricated to hold optical fibers from multiplexer. High power halogen tungsten lamp was collected as the light source. The spectral data were obtained with the exposure time of 2.17ms from the surface of the sample by press down the trigger switch on the self-developed system. The system could automatically acquire, process, display and save the data. Moreover the quality could be predicted on-line. A total of 55 fresh pork samples were used to develop prediction model for real time detection. The spectral data were pretreated with standard normalized variant (SNV) and partial least squares regression (PLSR) was used to develop prediction model. The correlation coefficient and root mean square error of the validation set for water content and pH were 0.810, 0.653, and 0.803, 0.098 respectively. The research shows that the real-time non-destructive detection system based on VIS/NIR spectroscopy can be efficient to predict the quality of fresh meat.

  15. Compensation for the distortion in satellite laser range predictions due to varying pulse travel times

    NASA Technical Reports Server (NTRS)

    Paunonen, Matti

    1993-01-01

    A method for compensating for the effect of the varying travel time of a transmitted laser pulse to a satellite is described. The 'observed minus predicted' range differences then appear to be linear, which makes data screening or use in range gating more effective.

  16. Analysis of locality-sensitive hashing for fast critical event prediction on physiological time series.

    PubMed

    Kim, Yongwook Bryce; O'Reilly, Una-May

    2016-08-01

    We apply the sublinear time, scalable locality-sensitive hashing (LSH) and majority discrimination to the problem of predicting critical events based on physiological waveform time series. Compared to using the linear exhaustive k-nearest neighbor search, our proposed method vastly speeds up prediction time up to 25 times while sacrificing only 1% of accuracy when demonstrated on an arterial blood pressure dataset extracted from the MIMIC2 database. We compare two widely used variants of LSH, the bit sampling based (L1LSH) and the random projection based (E2LSH) methods to measure their direct impact on retrieval and prediction accuracy. We experimentally show that the more sophisticated E2LSH performs worse than L1LSH in terms of accuracy, correlation, and the ability to detect false negatives. We attribute this to E2LSH's simultaneous integration of all dimensions when hashing the data, which actually makes it more impotent against common noise sources such as data misalignment. We also demonstrate that the deterioration of accuracy due to approximation at the retrieval step of LSH has a diminishing impact on the prediction accuracy as the speed up gain accelerates.

  17. Predicting High Risk Adolescents' Substance Use over Time: The Role of Parental Monitoring

    ERIC Educational Resources Information Center

    Clark, Heddy Kovach; Shamblen, Stephen R.; Ringwalt, Chris L.; Hanley, Sean

    2012-01-01

    We examined whether parental monitoring at baseline predicted subsequent substance use in a high-risk youth population. Students in 14 alternative high schools in Washington State completed self-report surveys at three time points over the course of 2 years. Primary analyses included 1,423 students aged 14-20 who lived with at least one parent or…

  18. Factors that Predict Full-Time Community College Faculty Engagement in Online Instruction

    ERIC Educational Resources Information Center

    Akroyd, Duane; Patton, Bess; Bracken, Susan

    2013-01-01

    This study is a secondary quantitative analysis of the 2004 National Study of Postsecondary Faculty (NSOPF) data. It examines the ability of human capital, intrinsic rewards, extrinsic rewards, and gender/race demographics to predict full-time community college faculty teaching on-line courses. Findings indicate that those faculty with higher…

  19. Predicting Time to Recovery Among Depressed Adolescents Treated in Two Psychosocial Group Interventions

    ERIC Educational Resources Information Center

    Rohde, Paul; Seeley, John R.; Kaufman, Noah K.; Clarke, Gregory N.; Stice, Eric

    2006-01-01

    Aims were to identify the demographic, psychopathology, and psychosocial factors predicting time to major depressive disorder (MDD) recovery and moderators of treatment among 114 depressed adolescents recruited from a juvenile justice center and randomized to a cognitive behavioral treatment (CBT) condition or a life skills-tutoring control…

  20. Microwave emission as a proxy of CME speed in ICME arrival time predictions

    NASA Astrophysics Data System (ADS)

    Salas Matamoros, Carolina; Klein, Karl-Ludwig; Trottet, Gerard

    2016-04-01

    The propagation of a coronal mass ejection (CME) to the Earth takes between about 13 hours and several days. Observations of early radiative signatures of CMEs therefore provide a possible means to predict the arrival time of the CME near Earth. The fundamental tool to measure CME speeds in the corona is coronography, but the Earth-directed speed of a CME cannot be measured by a coronagraph located on the Sun-Earth line. Various proxies have been devised, based on the coronographic measurement. As an alternative, we explore radiative proxies. In the present contribution we investigate if microwave observations can be employed as a proxy for CME propagation speed. Caroubalos (1964) had shown that the higher the fluence of a solar radio burst near 3 GHz, the shorter is the time lapse between the solar event and the sudden commencement of a geomagnetic storm. We reconsider the relationship between CME speed and microwave fluence for limb CMEs in cycle 23 and early cycle 24. Then we use the microwave fluence as a proxy of CME speed of Earth-directed CMEs, together with the empirical interplanetary acceleration model devised by Gopalswamy et al. (2001), to predict the CME arrival time at Earth. These predictions are compared with observed arrival times and with the predictions based on other proxies, including soft X-rays and coronographic measurements.

  1. What Time Is Sunrise? Revisiting the Refraction Component of Sunrise/set Prediction Models

    NASA Astrophysics Data System (ADS)

    Wilson, Teresa; Bartlett, Jennifer L.; Hilton, James Lindsay

    2017-01-01

    Algorithms that predict sunrise and sunset times currently have an error of one to four minutes at mid-latitudes (0° - 55° N/S) due to limitations in the atmospheric models they incorporate. At higher latitudes, slight changes in refraction can cause significant discrepancies, even including difficulties determining when the Sun appears to rise or set. While different components of refraction are known, how they affect predictions of sunrise/set has not yet been quantified. A better understanding of the contributions from temperature profile, pressure, humidity, and aerosols could significantly improve the standard prediction. We present a sunrise/set calculator that interchanges the refraction component by varying the refraction model. We then compare these predictions with data sets of observed rise/set times to create a better model. Sunrise/set times and meteorological data from multiple locations will be necessary for a thorough investigation of the problem. While there are a few data sets available, we will also begin collecting this data using smartphones as part of a citizen science project. The mobile application for this project will be available in the Google Play store. Data analysis will lead to more complete models that will provide more accurate rise/set times for the benefit of astronomers, navigators, and outdoorsmen everywhere.

  2. Semiparametric models of time-dependent predictive values of prognostic biomarkers.

    PubMed

    Zheng, Yingye; Cai, Tianxi; Stanford, Janet L; Feng, Ziding

    2010-03-01

    Rigorous statistical evaluation of the predictive values of novel biomarkers is critical prior to applying novel biomarkers into routine standard care. It is important to identify factors that influence the performance of a biomarker in order to determine the optimal conditions for test performance. We propose a covariate-specific time-dependent positive predictive values curve to quantify the predictive accuracy of a prognostic marker measured on a continuous scale and with censored failure time outcome. The covariate effect is accommodated with a semiparametric regression model framework. In particular, we adopt a smoothed survival time regression technique (Dabrowska, 1997, The Annals of Statistics 25, 1510-1540) to account for the situation where risk for the disease occurrence and progression is likely to change over time. In addition, we provide asymptotic distribution theory and resampling-based procedures for making statistical inference on the covariate-specific positive predictive values. We illustrate our approach with numerical studies and a dataset from a prostate cancer study.

  3. Physics-based enzyme design: predicting binding affinity and catalytic activity.

    PubMed

    Sirin, Sarah; Pearlman, David A; Sherman, Woody

    2014-12-01

    Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics-based implicit solvent MM-GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM-GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α-Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real-world enzyme design applications.

  4. Nonlinear techniques for forecasting solar activity directly from its time series

    NASA Technical Reports Server (NTRS)

    Ashrafi, S.; Roszman, L.; Cooley, J.

    1992-01-01

    Numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series are presented. This approach makes it possible to extract dynamical invariants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), given a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

  5. Nonlinear techniques for forecasting solar activity directly from its time series

    NASA Technical Reports Server (NTRS)

    Ashrafi, S.; Roszman, L.; Cooley, J.

    1993-01-01

    This paper presents numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series. This approach makes it possible to extract dynamical in variants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), give a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

  6. Predicting Marathon Time Using Exhaustive Graded Exercise Test in Marathon Runners.

    PubMed

    Till, Eloise S; Armstrong, Stuart A; Harris, Greg; Maloney, Stephen

    2016-02-01

    The study aimed to investigate the correlation between time on a treadmill test and exhaustion 2 weeks before a road marathon and the subsequent road marathon performance time (MPT). The study recruited 59 runners entered in the Melbourne 2012 marathon, Canberra 2013 marathon, and Gold Coast 2013 marathon. Forty runners completed both the graded exercise treadmill test to exhaustion and the 42.2 km marathon. Nineteen participants dropped out of the study due to illness, injury, or did not begin the treadmill test. A statistically significant correlation was found between treadmill time and MPT (adjusted R(2) = 0.447). Sex, weekly running duration (t = -1.58, p = 0.12), years of running (t = 1.10, p = 0.28), and age (t = 0.94, p = 0.36) did not statistically correlate with MPT. The relationship between the graded exercise test and MPT can be used to predict MPT using y = -3.85x + 351.57, where y is MPT and x is treadmill time. This is a simple, accessible, and cost-effective method to aid athletes in predicting their race time over 42.2 km. Prediction of marathon time in a simple and accessible manner was believed to be useful to the growing population of marathon runners around the world.

  7. Accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients

    PubMed Central

    Martinez, Bruno Prata; Gomes, Isabela Barboza; de Oliveira, Carolina Santana; Ramos, Isis Resende; Rocha, Mônica Diniz Marques; Júnior, Luiz Alberto Forgiarini; Camelier, Fernanda Warken Rosa; Camelier, Aquiles Assunção

    2015-01-01

    OBJECTIVES: The ability of the Timed Up and Go test to predict sarcopenia has not been evaluated previously. The objective of this study was to evaluate the accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients. METHODS: This cross-sectional study analyzed 68 elderly patients (≥60 years of age) in a private hospital in the city of Salvador-BA, Brazil, between the 1st and 5th day of hospitalization. The predictive variable was the Timed Up and Go test score, and the outcome of interest was the presence of sarcopenia (reduced muscle mass associated with a reduction in handgrip strength and/or weak physical performance in a 6-m gait-speed test). After the descriptive data analyses, the sensitivity, specificity and accuracy of a test using the predictive variable to predict the presence of sarcopenia were calculated. RESULTS: In total, 68 elderly individuals, with a mean age 70.4±7.7 years, were evaluated. The subjects had a Charlson Comorbidity Index score of 5.35±1.97. Most (64.7%) of the subjects had a clinical admission profile; the main reasons for hospitalization were cardiovascular disorders (22.1%), pneumonia (19.1%) and abdominal disorders (10.2%). The frequency of sarcopenia in the sample was 22.1%, and the mean length of time spent performing the Timed Up and Go test was 10.02±5.38 s. A time longer than or equal to a cutoff of 10.85 s on the Timed Up and Go test predicted sarcopenia with a sensitivity of 67% and a specificity of 88.7%. The accuracy of this cutoff for the Timed Up and Go test was good (0.80; IC=0.66-0.94; p=0.002). CONCLUSION: The Timed Up and Go test was shown to be a predictor of sarcopenia in elderly hospitalized patients. PMID:26039955

  8. A rest-activity biomarker to predict response to SSRIs in major depressive disorder.

    PubMed

    McCall, W Vaughn

    2015-05-01

    Most adults with Major Depressive Disorder (MDD) will not experience a remission with the first antidepressant trial. No practical biomarkers presently exist to predict responsiveness to antidepressants. Herein we report pilot data for a rest-activity biomarker of antidepressant response. Fifty-eight medication-free adults with MDD underwent a week-long collection of actigraphic data before beginning a 9 week open label trial of fluoxetine, coupled with blinded randomized assignment to eszopiclone/placebo. Depression severity was repeatedly measured with the Hamilton Rating Scale for Depression (HRSD). Baseline actigraphic data was analyzed with functional data analysis to create smoothed 24-h curves of activity. The time of the lowest point of activity (the bathyphase) was calculated for each patient, as well the mean difference between bedtime and the bathyphase (BBD). At the end of treatment, patients were characterized as treatment responders (50% reduction in HRSD) or non-responders, and receiver operating curves were calculated to find the optimal cut point of the BBD for prediction of treatment response. The best cut point for BBD was at 260.2 min, resulting in an effect size of 1.45, and with a positive predictive value of 0.75 and a negative predictive value of 0.88. We conclude that actigraphically-determined measures of rest-activity patterns show promise as potential biomarker predictors of antidepressant response. However, this conclusion is based upon a small number of patients who received only one choice of antidepressant, for a single trial. Replication with a larger sample is needed.

  9. Activated partial thromboplastin time of owl monkey (Aotus trivirgatus) plasma.

    PubMed

    Mrema, J E; Johnson, G S; Kelley, S T; Green, T J

    1984-06-01

    Owl monkey plasma samples produced short, reproducible activated partial thromboplastin times, similar to those obtained with samples from many other mammalian species. This was an apparent contradiction to an earlier report of long irreproducible activated partial thromboplastin times from owl monkey samples. The discrepant data could not be explained by differences in anticoagulants (citrate or oxalate), assay reagents (partial thromboplastin with either diatomaceous earth or ellagic acid), or activation incubation times (2, 5, or 10 minutes); nor could they be explained by differences in the monkeys' sex, age or previous experimental exposure to Plasmodium falciparum malaria.

  10. Associations between Screen Time and Physical Activity among Spanish Adolescents

    PubMed Central

    Serrano-Sanchez, Jose A.; Martí-Trujillo, Sara; Lera-Navarro, Angela; Dorado-García, Cecilia; González-Henríquez, Juan J.; Sanchís-Moysi, Joaquín

    2011-01-01

    Background Excessive time in front of a single or several screens could explain a displacement of physical activity. The present study aimed at determining whether screen-time is associated with a reduced level of moderate to vigorous physical activity (MVPA) in Spanish adolescents living in favorable environmental conditions. Methodology/Principal Findings A multi-stage stratified random sampling method was used to select 3503 adolescents (12–18 years old) from the school population of Gran Canaria, Spain. MVPA, screen-time in front of television, computer, video game console and portable console was assessed in the classroom by fulfilling a standardized questionnaire. Bivariate and multivariate logistic regression analyses adjusted by a set of social-environmental variables were carried out. Forty-six percent of girls (95% CI±2.3%) and 26% of boys (95% CI±2.1%) did not meet the MVPA recommendations for adolescents. Major gender differences were observed in the time devoted to vigorous PA, video games and the total time spent on screen-based activities. Boys who reported 4 hours•week−1 or more to total screen-time showed a 64% (OR = 0.61, 95% CI, 0.44–0.86) increased risk of failing to achieve the recommended adolescent MVPA level. Participation in organized physical activities and sports competitions were more strongly associated with MVPA than screen-related behaviors. Conclusions/Significance No single screen-related behavior explained the reduction of MVPA in adolescents. However, the total time accumulated through several screen-related behaviors was negatively associated with MVPA level in boys. This association could be due to lower availability of time for exercise as the time devoted to sedentary screen-time activities increases. Participation in organized physical activities seems to counteract the negative impact of excessive time in front of screens on physical activity. PMID:21909435

  11. Machine Learning Methods for Predicting HLA–Peptide Binding Activity

    PubMed Central

    Luo, Heng; Ye, Hao; Ng, Hui Wen; Shi, Leming; Tong, Weida; Mendrick, Donna L.; Hong, Huixiao

    2015-01-01

    As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA–peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA–peptide binding prediction. We also summarize the descriptors based on which the HLA–peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA–peptide binding prediction method based on network analysis. PMID:26512199

  12. Predictive motor control of sensory dynamics in auditory active sensing.

    PubMed

    Morillon, Benjamin; Hackett, Troy A; Kajikawa, Yoshinao; Schroeder, Charles E

    2015-04-01

    Neuronal oscillations present potential physiological substrates for brain operations that require temporal prediction. We review this idea in the context of auditory perception. Using speech as an exemplar, we illustrate how hierarchically organized oscillations can be used to parse and encode complex input streams. We then consider the motor system as a major source of rhythms (temporal priors) in auditory processing, that act in concert with attention to sharpen sensory representations and link them across areas. We discuss the circuits that could mediate this audio-motor interaction, notably the potential role of the somatosensory system. Finally, we reposition temporal predictions in the context of internal models, discussing how they interact with feature-based or spatial predictions. We argue that complementary predictions interact synergistically according to the organizational principles of each sensory system, forming multidimensional filters crucial to perception.

  13. Cognitive domains that predict time to diagnosis in prodromal Huntington disease

    PubMed Central

    Harrington, Deborah L.; Smith, Megan M.; Zhang, Ying; Carlozzi, Noelle E.; Paulsen, Jane S.

    2013-01-01

    Background Prodromal Huntington disease (prHD) is associated with a myriad of cognitive changes, but the domains that best predict time to clinical diagnosis have not been studied. This is a notable gap because some domains may be more sensitive to cognitive decline, which would inform clinical trials. Objectives The present study sought to characterize cognitive domains underlying a large test battery and for the first time, evaluate their ability to predict to time to diagnosis. Methods Participants included gene-negative and gene-positive prHD participants who were enrolled in the PREDICT-HD study. The CAG/Age Product (CAP) score was the measure of an individual’s genetic signature. A factor analysis of 18 tests was performed to identify sets of measures or latent factors that elucidated core constructs of tests. Factor scores were then fit to a survival model to evaluate their ability to predict time to diagnosis. Results Six factors were identified: 1) speed/inhibition, 2) verbal working memory, 3) motor planning/speed, 4) attention-information integration, 5) sensory-perceptual processing, and 6) verbal learning/memory. Factor scores were sensitive to a worsening of cognitive functioning in prHD, typically more so than performances on individual tests comprising the factors. Only the motor planning/speed and sensory-perceptual processing factors predicted time to diagnosis, after controlling for CAP scores and motor symptoms. Conclusions The results suggest that motor planning/speed and sensory-perceptual processing are important markers of disease prognosis. The findings also have implications for using composite indices of cognition in preventive HD trials where they may be more sensitive than individual tests. PMID:22451099

  14. Real-Time Safety Monitoring and Prediction for the National Airspace System

    NASA Technical Reports Server (NTRS)

    Roychoudhury, Indranil

    2016-01-01

    As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have both an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecasts, predicted health of assets in the airspace, and so on. To this end, we have developed a Real-Time Safety Monitoring (RTSM) that first, estimates the state of the NAS using the dynamic models. Then, given the state estimate and a probability distribution of future inputs to the NAS, the framework predicts the evolution of the NAS, i.e., the future state, and analyzes these future states to predict the occurrence of unsafe events. The entire probability distribution of airspace safety metrics is computed, not just point estimates, without significant assumptions regarding the distribution type and or parameters. We demonstrate our overall approach by predicting the occurrence of some unsafe events and show how these predictions evolve in time as flight operations progress.

  15. Predictive motor timing performance dissociates between early diseases of the cerebellum and Parkinson's disease.

    PubMed

    Bares, Martin; Lungu, Ovidiu V; Husárová, Ivica; Gescheidt, Tomás

    2010-03-01

    There is evidence that both the basal ganglia and the cerebellum play a role in the neural representation of time in a variety of behaviours, but whether one of them is more important is not yet clear. To address this question in the context of predictive motor timing, we tested patients with various movement disorders implicating these two structures in a motor-timing task. Specifically, we investigated four different groups: (1) patients with early Parkinson's disease (PD); (2) patients with sporadic spinocerebellar ataxia (SCA); (3) patients with familial essential tremor (ET); and (4) matched healthy controls. We used a predictive motor-timing task that involved mediated interception of a moving target, and we assessed the effect of movement type (acceleration, deceleration and constant), speed (slow, medium and fast) and angle (0 degrees , 15 degrees and 30 degrees) on performance (hit, early error and late error). The main results showed that PD group and arm ET subgroup did not significantly differ from the control group. SCA and head ET subjects (severe and mild cerebellar damage, respectively) were significantly worse at interception than the other two groups. Our findings support the idea that the basal ganglia play a less significant role in predictive motor timing than the cerebellum. The fact that SCA and ET subjects seemed to have a fundamental problem with predictive motor timing suggests that the cerebellum plays an essential role in integrating incoming visual information with the motor output in a timely manner, and that ET is a heterogeneous entity that deserves increased attention from clinicians.

  16. Comparison of Prothrombin Time and Aspartate Aminotransferase in Predicting Hepatotoxicity After Acetaminophen Overdose.

    PubMed

    Levine, Michael; O'Connor, Ayrn D; Padilla-Jones, Angela; Gerkin, Richard D

    2016-03-01

    Despite decades of experience with acetaminophen (APAP) overdoses, it remains unclear whether elevated hepatic transaminases or coagulopathy develop first. Furthermore, comparison of the predictive value of these two variables in determining hepatic toxicity following APAP overdoses has been poorly elucidated. The primary objective of this study is to determine the test characteristics of the aspartate aminotransferase (AST) and the prothrombin time (PT) in patients with APAP toxicity. A retrospective chart review of APAP overdoses treated with IV N-acetylcysteine at a tertiary care referral center was performed. Of the 304 subjects included in the study, 246 with an initial AST less than 1000 were analyzed to determine predictors of hepatic injury, defined as an AST exceeding 1000 IU/L. The initial AST >50 was 79.5 % sensitive and 82.6 % specific for predicting hepatic injury. The corresponding negative and positive predictive values were 95.5 and 46.3 %, respectively. In contrast, an initial abnormal PT had a sensitivity of 82.1 % and a specificity of 63.6 %. The negative and positive predictive values for initial PT were 94.9 and 30.2 %, respectively. Although the two tests performed similarly for predicting a composite endpoint of death or liver transplant, neither was a useful predictor. Initial AST performed better than the initial PT for predicting hepatic injury in this series of patients with APAP overdose.

  17. Predicting chronic obstructive pulmonary disease hospitalizations based on concurrent influenza activity.

    PubMed

    Gerke, Alicia K; Tang, Fan; Yang, Ming; Foster, Eric D; Cavanaugh, Joseph E; Polgreen, Philip M

    2013-10-01

    Although influenza has been associated with chronic obstructive pulmonary disease (COPD) exacerbations, it is not clear the extent to which this association affects healthcare use in the United States. The first goal of this project was to determine to what extent the incidence of COPD hospitalizations is associated with seasonal influenza. Second, as a natural experiment, we used influenza activity to help predict COPD admissions during the 2009 H1N1 influenza pandemic. To do this, we identified all hospitalizations between 1998 and 2010 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project (HCUP) during which a primary diagnosis of COPD was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We formulated time series regression models to investigate the association of monthly COPD admissions with influenza incidence. Finally, we applied these models, fit using 1998-2008 data, to forecast monthly COPD admissions during the 2009 pandemic. Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of COPD hospitalizations (p-value < 0.0001). The association is especially strong among older patients requiring mechanical ventilation. Use of influenza data to predict COPD admissions during the 2009 H1N1 pandemic reduced the mean-squared prediction error by 29.9%. We conclude that influenza activity is significantly associated with COPD hospitalizations in the United States and influenza activity can be exploited to more accurately forecast COPD admissions. Our results suggest that improvements in influenza surveillance, prevention, and treatment may decrease hospitalizations of patients diagnosed with COPD.

  18. The Elasticity of Time: Associations between Physical Activity and Use of Time in Adolescents

    ERIC Educational Resources Information Center

    Olds, Tim; Ferrar, Katia E.; Gomersall, Sjaan R.; Maher, Carol; Walters, J. L.

    2012-01-01

    The way an individual uses one's time can greatly affect his or her health. The purpose of this article was to examine the cross-sectional cross-elasticity relationships for use of time domains in a sample of Australian adolescents. This study analyzed 24-hour recall time use data collected using the Multimedia Activity Recall for Children and…

  19. An investigation into active vibration isolation based on predictive control: Part I: Energy source control

    NASA Astrophysics Data System (ADS)

    Fei, H. Z.; Zheng, G. T.; Liu, Z. G.

    2006-09-01

    We report the results of a recent study for the active vibration isolation with whole-spacecraft vibration isolation as an application background into which three parts are divided: (i) energy source control, (ii) nonlinearity and time delay, (iii) implementation and experiment. This paper is the first in this three-part series report, which presents theoretical and experimental investigations into pressure tracking system for energy source control of the isolator. Considering the special environment of the rocket and expected characteristics of actuators, where the isolator will be arranged between the rocket and the spacecraft, pneumatic actuator is proposed to realize the active isolation control. In order to improve the dynamic characteristics of the pneumatic isolator, a cascade control algorithm with double loop structure and predictive control algorithm for pressure tracking control of the inner loop are proposed. In the current paper, a pressure tracking control system using model predictive control (MPC) is studied first. A pneumatic model around pressure work point is built firstly by simplifying the flow equation of valve's orifices and pressure differential equation of the chambers. With this model, an MPC algorithm in the state space is developed, and problems including control parameter choice and command horizon generator are discussed in detail. In addition, by adding model error correction loop and velocity compensation feedback, effects of model uncertainty and volume variation of chambers are reduced greatly. Thus with this design, the real-time pressure tracking can be guaranteed, and so that the active control system can work at higher frequency range.

  20. High-resolution summer rainfall prediction in the JHWC real-time WRF system

    NASA Astrophysics Data System (ADS)

    Lee, Dong-Kyou; Eom, Dae-Yong; Kim, Joo-Wan; Lee, Jae-Bok

    2010-08-01

    The WRF-based real-time forecast system (http://jhwc.snu.ac.kr/weather) of the Joint Center for High-impact Weather and Climate Research (JHWC) has been in operation since November 2006; this system has three nested model domains using GFS (Global Forecast System) data for its initial and boundary conditions. In this study, we evaluate the improvement in daily and hourly weather prediction, particularly the prediction of summer rainfall over the Korean Peninsula, in the JHWC WRF (Weather Research and Forecasting) model system by 3DVAR (three-Dimensional Variational) data assimilation using the data obtained from KEOP (Korea Enhanced Observation Program). KEOP was conducted during the period June 15 to July 15, 2007, and the data obtained included GTS (Global Telecommunication System) upper-air sounding, AWS (Automatic Weather System), wind profiler, and radar observation data. Rainfall prediction and its characteristics should be verified by using the precipitation observation and the difference field of each experiment. High-resolution (3 km in domain 3) summer rainfall prediction over the Korean peninsula is substantially influenced by improved synoptic-scale prediction in domains 1 (27 km) and 2 (9 km), in particular by data assimilation using the sounding and wind profiler data. The rainfall prediction in domain 3 was further improved by radar and AWS data assimilation in domain 3. The equitable threat score and bias score of the rainfall predicted in domain 3 indicated improvement for the threshold values of 0.1, 1, and 2.5 mm with data assimilation. For cases of occurrence of heavy rainfall (7 days), the equitable threat score and bias score improved considerably at all threshold values as compared to the entire period of KEOP. Radar and AWS data assimilation improved the temporal and spatial distributions of diurnal rainfall over southern Korea, and AWS data assimilation increased the predicted rainfall amount by approximately 0.3 mm 3hr-1.

  1. Space can substitute for time in predicting climate-change effects on biodiversity.

    PubMed

    Blois, Jessica L; Williams, John W; Fitzpatrick, Matthew C; Jackson, Stephen T; Ferrier, Simon

    2013-06-04

    "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption--that drivers of spatial gradients of species composition also drive temporal changes in diversity--rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  2. Space can substitute for time in predicting climate-change effects on biodiversity

    NASA Astrophysics Data System (ADS)

    Blois, Jessica L.; Williams, John W.; Fitzpatrick, Matthew C.; Jackson, Stephen T.; Ferrier, Simon

    2013-06-01

    "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption-that drivers of spatial gradients of species composition also drive temporal changes in diversity-rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  3. A novel multi-target regression framework for time-series prediction of drug efficacy

    PubMed Central

    Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

    2017-01-01

    Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task. PMID:28098186

  4. A quantitative parameter-free prediction of simulated crystal nucleation times

    SciTech Connect

    Aga, Rachel S; Morris, James R; Hoyt, Jeffrey John; Mendelev, Mikhail I.

    2006-01-01

    We present direct comparisons between simulated crystal-nucleation times and theoretical predictions using a model of aluminum, and demonstrate that a quantitative prediction can be made. All relevant thermodynamic properties of the system are known, making the agreement of our simulation data with nucleation theories free of any adjustable parameters. The role of transient nucleation is included in the classical nucleation theory approach, and shown to be necessary to understand the observed nucleation times. The calculations provide an explanation on why nucleation is difficult to observe in simulations at moderate undercoolings. Even when the simulations are significantly larger than the critical nucleus, and when simulation times are sufficiently long, at moderate undercoolings the small concentration of critical nuclei makes the probability of the nucleation low in molecular dynamics simulations.

  5. Obtaining Reliable Predictions of Terrestrial Energy Coupling From Real-Time Solar Wind Measurements

    NASA Technical Reports Server (NTRS)

    Weimer, Daniel R.

    2002-01-01

    Measurements of the interplanetary magnetic field (IMF) from the ACE (Advanced Composition Explorer), Wind, IMP-8 (Interplanetary Monitoring Platform), and Geotail spacecraft have revealed that the IMF variations are contained in phase planes that are tilted with respect to the propagation direction, resulting in continuously variable changes in propagation times between spacecraft, and therefore, to the Earth. Techniques for using 'minimum variance analysis' have been developed in order to be able to measure the phase front tilt angles, and better predict the actual propagation times from the L1 orbit to the Earth, using only the real-time IMF measurements from one spacecraft. The use of empirical models with the IMF measurements at L1 from ACE (or future satellites) for predicting 'space weather' effects has also been demonstrated.

  6. A Comparison of Center/TRACON Automation System and Airline Time of Arrival Predictions

    NASA Technical Reports Server (NTRS)

    Heere, Karen R.; Zelenka, Richard E.

    2000-01-01

    Benefits from information sharing between an air traffic service provider and a major air carrier are evaluated. Aircraft arrival time schedules generated by the NASA/FAA Center/TRACON Automation System (CTAS) were provided to the American Airlines System Operations Control Center in Fort Worth, Texas, during a field trial of a specialized CTAS display. A statistical analysis indicates that the CTAS schedules, based on aircraft trajectories predicted from real-time radar and weather data, are substantially more accurate than the traditional airline arrival time estimates, constructed from flight plans and en route crew updates. The improvement offered by CTAS is especially advantageous during periods of heavy traffic and substantial terminal area delay, allowing the airline to avoid large predictive errors with serious impact on the efficiency and profitability of flight operations.

  7. Predicting critical transitions in dynamical systems from time series using nonstationary probability density modeling

    NASA Astrophysics Data System (ADS)

    Kwasniok, Frank

    2013-11-01

    A time series analysis method for predicting the probability density of a dynamical system is proposed. A nonstationary parametric model of the probability density is estimated from data within a maximum likelihood framework and then extrapolated to forecast the future probability density and explore the system for critical transitions or tipping points. A full systematic account of parameter uncertainty is taken. The technique is generic, independent of the underlying dynamics of the system. The method is verified on simulated data and then applied to prediction of Arctic sea-ice extent.

  8. Predicting critical transitions in dynamical systems from time series using nonstationary probability density modeling.

    PubMed

    Kwasniok, Frank

    2013-11-01

    A time series analysis method for predicting the probability density of a dynamical system is proposed. A nonstationary parametric model of the probability density is estimated from data within a maximum likelihood framework and then extrapolated to forecast the future probability density and explore the system for critical transitions or tipping points. A full systematic account of parameter uncertainty is taken. The technique is generic, independent of the underlying dynamics of the system. The method is verified on simulated data and then applied to prediction of Arctic sea-ice extent.

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

    PubMed

    Hirata, Yoshito

    2014-05-01

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

  10. Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

    SciTech Connect

    Gregor P. Henze; Moncef Krarti

    2003-12-17

    Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigates the merits of harnessing both storage media concurrently in the context of predictive optimal control. This topical report describes the demonstration of the model-based predictive optimal control for active and passive building thermal storage inventory in a test facility in real-time using time-of-use differentiated electricity prices without demand charges. The laboratory testing findings presented in this topical report cover the second of three project phases. The novel supervisory controller successfully executed a three-step procedure consisting of (1) short-term weather prediction, (2) optimization of control strategy over the next planning horizon using a calibrated building model, and (3) post-processing of the optimal strategy to yield a control command for the current time step that can be executed in the test facility. The primary and secondary building mechanical systems were effectively orchestrated by the model-based predictive optimal controller in real-time while observing comfort and operational constraints. The findings reveal that when the optimal controller is given imperfect weather fore-casts and when the building model used for planning control strategies does not match the actual building perfectly, measured utility costs savings relative to conventional building operation can be substantial. This requires that the facility under control lends itself to passive storage utilization and the building model

  11. Solar activity prediction of sunspot numbers (verification). Predicted solar radio flux; predicted geomagnetic indices Ap and Kp. [space shuttle program: satellite orbital lifetime

    NASA Technical Reports Server (NTRS)

    Newman, S. R.

    1980-01-01

    Efforts to further verify a previously reported technique for predicting monthly sunspot numbers over a period of years (1979 to 1989) involved the application of the technique over the period for the maximum epoch of solar cycle 19. Results obtained are presented. Methods and results for predicting solar flux (F10.7 cm) based on flux/sunspot number models, ascent and descent, and geomagnetic activity indices as a function of sunspot number and solar cycle phase classes are included.

  12. Significance of time awake for predicting pilots' fatigue on short-haul flights: implications for flight duty time regulations.

    PubMed

    Vejvoda, Martin; Elmenhorst, Eva-Maria; Pennig, Sibylle; Plath, Gernot; Maass, Hartmut; Tritschler, Kristjof; Basner, Mathias; Aeschbach, Daniel

    2014-10-01

    European regulations restrict the duration of the maximum daily flight duty period for pilots as a function of the duty start time and the number of scheduled flights. However, late duty end times that may include long times awake are not specifically regulated. In this study, fatigue levels in pilots finishing their duty late at night (00:00-01:59 hour) were analysed and compared with pilots starting their duty early (05:00-06:59 hour). Fatigue levels of 40 commercial short-haul pilots were studied during a total of 188 flight duty periods, of which 87 started early and 22 finished late. Pilots used a small handheld computer to maintain a duty and sleep log, and to indicate fatigue levels immediately after each flight. Sleep logs were checked with actigraphy. Pilots on late-finishing flight duty periods were more fatigued at the end of their duty than pilots on early-starting flight duty periods, despite the fact that preceding sleep duration was longer by 1.1 h. Linear mixed-model regression identified time awake as a preeminent factor predicting fatigue. Workload had a minor effect. Pilots on late-finishing flight duty periods were awake longer by an average of 5.5 h (6.6 versus 1.1 h) before commencing their duty than pilots who started early in the morning. Late-finishing flights were associated with long times awake at a time when the circadian system stops promoting alertness, and an increased, previously underestimated fatigue risk. Based on these findings, flight duty limitations should consider not only duty start time, but also the time of the final landing.

  13. Predicting muscle activation patterns from motion and anatomy: modelling the skull of Sphenodon (Diapsida: Rhynchocephalia)

    PubMed Central

    Curtis, Neil; Jones, Marc E. H.; Evans, Susan E.; Shi, JunFen; O'Higgins, Paul; Fagan, Michael J.

    2010-01-01

    The relationship between skull shape and the forces generated during feeding is currently under widespread scrutiny and increasingly involves the use of computer simulations such as finite element analysis. The computer models used to represent skulls are often based on computed tomography data and thus are structurally accurate; however, correctly representing muscular loading during food reduction remains a major problem. Here, we present a novel approach for predicting the forces and activation patterns of muscles and muscle groups based on their known anatomical orientation (line of action). The work was carried out for the lizard-like reptile Sphenodon (Rhynchocephalia) using a sophisticated computer-based model and multi-body dynamics analysis. The model suggests that specific muscle groups control specific motions, and that during certain times in the bite cycle some muscles are highly active whereas others are inactive. The predictions of muscle activity closely correspond to data previously recorded from live Sphenodon using electromyography. Apparent exceptions can be explained by variations in food resistance, food size, food position and lower jaw motions. This approach shows considerable promise in advancing detailed functional models of food acquisition and reduction, and for use in other musculoskeletal systems where no experimental determination of muscle activity is possible, such as in rare, endangered or extinct species. PMID:19474084

  14. Implicit theories about willpower predict the activation of a rest goal following self-control exertion.

    PubMed

    Job, Veronika; Bernecker, Katharina; Miketta, Stefanie; Friese, Malte

    2015-10-01

    Past research indicates that peoples' implicit theories about the nature of willpower moderate the ego-depletion effect. Only people who believe or were led to believe that willpower is a limited resource (limited-resource theory) showed lower self-control performance after an initial demanding task. As of yet, the underlying processes explaining this moderating effect by theories about willpower remain unknown. Here, we propose that the exertion of self-control activates the goal to preserve and replenish mental resources (rest goal) in people with a limited-resource theory. Five studies tested this hypothesis. In Study 1, individual differences in implicit theories about willpower predicted increased accessibility of a rest goal after self-control exertion. Furthermore, measured (Study 2) and manipulated (Study 3) willpower theories predicted an increased preference for rest-conducive objects. Finally, Studies 4 and 5 provide evidence that theories about willpower predict actual resting behavior: In Study 4, participants who held a limited-resource theory took a longer break following self-control exertion than participants with a nonlimited-resource theory. Longer resting time predicted decreased rest goal accessibility afterward. In Study 5, participants with an induced limited-resource theory sat longer on chairs in an ostensible product-testing task when they had engaged in a task requiring self-control beforehand. This research provides consistent support for a motivational shift toward rest after self-control exertion in people holding a limited-resource theory about willpower.

  15. Circadian Activity Rhythms, Time Urgency, and Achievement Concerns.

    ERIC Educational Resources Information Center

    Watts, Barbara L.

    Many physiological and psychological processes fluctuate throughout the day in fairly stable, rhythmic patterns. The relationship between individual differences in circadian activity rhythms and a sense of time urgency were explored as well as a number of achievement-related variables. Undergraduates (N=308), whose circadian activity rhythms were…

  16. Effects of intensity and positional predictability of a visual stimulus on simple reaction time.

    PubMed

    Carreiro, Luiz Renato Rodrigues; Haddad, Hamilton; Baldo, Marcus Vinicius Chrysóstomo

    2011-01-10

    The influence of visual stimuli intensity on manual reaction time (RT) was investigated under two different attentional settings: high (Experiment 1) and low (Experiment 2) stimulus location predictability. These two experiments were also run under both binocular and monocular viewing conditions. We observed that RT decreased as stimulus intensity increased. It also decreased as the viewing condition was changed from monocular to binocular as well as the location predictability shifted from low to high. A significant interaction was found between stimulus intensity and viewing condition, but no interaction was observed between neither of these factors and location predictability. These findings support the idea that the stimulus intensity effect arises from purely sensory, pre-attentive mechanisms rather than deriving from more efficient attentional capture.

  17. Application of thermodynamic-based retention time prediction to ionic liquid stationary phases.

    PubMed

    Weber, Brandon M; Harynuk, James J

    2014-06-01

    First- and second-dimension retention times for a series of alkyl phosphates were predicted for multiple column combinations in GC×GC. This was accomplished through the use of a three-parameter thermodynamic model where the analytes' interactions with the stationary phases in both dimensions are known. Ionic liquid columns were employed to impart unique selectivity for alkyl phosphates, and it was determined that for alkyl phosphate compounds, ionic liquid columns are best used in the primary dimension. Retention coordinates for unknown phosphates are predicted from the thermodynamic parameters of a set standard alkyl phosphates. Additionally, we present changing retention properties of alkyl phosphates on some ionic liquid columns, due to suspected reaction between the analyte and column. This makes it difficult to accurately predict their retention properties, and in general poses a problem for ionic liquid columns with these types of analytes.

  18. Real time prediction of marine vessel motions using Kalman filtering techniques

    NASA Technical Reports Server (NTRS)

    Triantafyllou, M. S.; Bodson, M.

    1982-01-01

    The present investigation is concerned with the prediction of the future behavior of a vessel within some confidence bounds at a specific instant of time, taking into account an interval of a few seconds. The ability to predict accurately the motions of a vessel can reduce significantly the probability of failure of operations in rough seas. The investigation was started as part of an effort to ensure safe landing of aircraft on relatively small vessels. However, the basic principles involved in the study are the same for any offshore operation, such as carbo transfer in the open sea, structure installation, and floating crane operation. The Kalman filter is a powerful tool for achieving the goals of the prediction procedure. Attention is given to a linear optimal predictor, the equations of motion of the vessel, the wave spectrum, rational approximation, the use of Kalman filter and predictor in an application for a ship, and the motions of a semisubmersible.

  19. Predicting lightning activity in Greece with the Weather Research and Forecasting (WRF) model

    NASA Astrophysics Data System (ADS)

    Giannaros, Theodore M.; Kotroni, Vassiliki; Lagouvardos, Konstantinos

    2015-04-01

    In recent years, significant progress has been made in the development and implementation of parameterizations for the prediction of lightning. In the present study, the commonly used Price and Rind lightning parameterization is evaluated. This parameterization has been recently introduced in the state-of-the-art Weather Research and Forecasting (WRF) model, allowing for the online simulation of lightning activity. The evaluation of the parameterization is conducted for ten different single-day events that took place in Greece during the period of years from 2010 to 2013. Results show that the WRF model could be used for real-time lightning prediction applications, given that the lightning parameterization is properly adapted. In particular, the analysis revealed that model-resolved variables related to the microphysics and thermodynamics are necessary for controlling the parameterization of lightning, which otherwise results to significant overprediction. The total ice content, the maximum vertical velocity and the convective available potential energy were found to be the storm parameters that, when combined together, improve the ability of the model to correctly predict lightning, significantly restricting false alarms. This was further highlighted by separately examining two example case studies, for which the numerical simulations successfully reproduced the spatial and temporal characteristics of lightning activity.

  20. Dynamic variation in pleasure in children predicts nonlinear change in lateral frontal brain electrical activity.

    PubMed

    Light, Sharee N; Coan, James A; Frye, Corrina; Goldsmith, H Hill; Davidson, Richard J

    2009-03-01

    Individual variation in the experience and expression of pleasure may relate to differential patterns of lateral frontal activity. Brain electrical measures have been used to study the asymmetric involvement of lateral frontal cortex in positive emotion, but the excellent time resolution of these measures has not been used to capture second-by-second changes in ongoing emotion until now. The relationship between pleasure and second-by-second lateral frontal activity was examined with the use of hierarchical linear modeling in a sample of 128 children ages 6-10 years. Electroencephalographic activity was recorded during "pop-out toy," a standardized task that elicits pleasure. The task consisted of 3 epochs: an anticipation period sandwiched between 2 play periods. The amount of pleasure expressed during the task predicted the pattern of nonlinear change in lateral frontal activity. Children who expressed increasing amounts of pleasure during the task exhibited increasing left lateral frontal activity during the task, whereas children who expressed contentment exhibited increasing right/decreasing left activity. These findings indicate that task-dependent changes in pleasure relate to dynamic, nonlinear changes in lateral frontal activity as the task unfolds.

  1. Incorporating system latency associated with real-time target tracking radiotherapy in the dose prediction step

    NASA Astrophysics Data System (ADS)

    Roland, Teboh; Mavroidis, Panayiotis; Shi, Chengyu; Papanikolaou, Nikos

    2010-05-01

    System latency introduces geometric errors in the course of real-time target tracking radiotherapy. This effect can be minimized, for example by the use of predictive filters, but cannot be completely avoided. In this work, we present a convolution technique that can incorporate the effect as part of the treatment planning process. The method can be applied independently or in conjunction with the predictive filters to compensate for residual latency effects. The implementation was performed on TrackBeam (Initia Ltd, Israel), a prototype real-time target tracking system assembled and evaluated at our Cancer Institute. For the experimental system settings examined, a Gaussian distribution attributable to the TrackBeam latency was derived with σ = 3.7 mm. The TrackBeam latency, expressed as an average response time, was deduced to be 172 ms. Phantom investigations were further performed to verify the convolution technique. In addition, patient studies involving 4DCT volumes of previously treated lung cancer patients were performed to incorporate the latency effect in the dose prediction step. This also enabled us to effectively quantify the dosimetric and radiobiological impact of the TrackBeam and other higher latency effects on the clinical outcome of a real-time target tracking delivery.

  2. Prediction of reverberation time and speech transmission index in long enclosures

    NASA Astrophysics Data System (ADS)

    Li, Kai Ming; Lam, Pou Man

    2005-06-01

    It is known that the sound field in a long space is not diffuse, and that the classic theory of room acoustics is not applicable. A theoretical model is developed for the prediction of reverberation time and speech transmission index in rectangular long enclosures, such as corridors and train stations, where the acoustic quality is important for speech. The model is based on an image-source method, and both acoustically hard and impedance boundaries are investigated. An approximate analytical solution is used to predict the frequency response of the sound field. The reverberation time is determined from the decay curve which is computed by a reverse-time integration of the squared impulse response. The angle-dependence of reflection coefficients of the boundaries and the change of phase upon reflection are incorporated in this model. Due to the relatively long distance of sound propagation, the effect of atmospheric absorption is also considered. Measurements of reverberation time and speech transmission index taken from a real tunnel, a corridor, and a model tunnel are presented. The theoretical predictions are found to agree well with the experimental data. An application of the proposed model has been suggested. .

  3. Prediction of time to exhaustion in competitive cyclists from a perceptually based scale.

    PubMed

    Garcin, Murielle; Coquart, Jérémy B J; Robin, Sophie; Matran, Régis

    2011-05-01

    Prediction of time to exhaustion in competitive cyclists from a perceptually based scale. We have tested the validity of the estimated time limit (ETL) scale to predict an exhaustion time (T(lim)) from values stemming from incremental and randomized constant workloads tests on a cycle ergometer. Twenty-five cyclists performed 1 continuous incremental test, 1 discontinuous test with randomized workloads, and 1 constant power output test at 90% of maximal aerobic power (MAP) to exhaustion. Estimated time limits at 90% MAP during the incremental test and the test with randomized workloads were calculated from exponential relationships between power and ETL using the same 4 workloads. Real measured T(lim) during the constant power output test was converted into ETL values (called measured ETL). The differences between the calculated and measured ETLs were examined. Estimated time limits calculated at 90% MAP during the incremental and randomized tests corresponded to 14 minutes 56 seconds and 10 minutes 14 seconds, whereas measured ETL was equal to 11 minutes 19 seconds ± 3 minutes 40 seconds. The results showed a nonsignificant difference between calculated and measured ETLs. However, the mean differences between the measured ETL values during the constant test performed at the same intensity were -1.3 ± 2.9 and 0.3 ± 3.0 for the incremental and the randomized constant workloads tests, respectively. Consequently, the use of ETL calculated at 90% MAP during the test with randomized constant workloads may be preferable to predict the accurate T(lim). Moreover, it would seem that high-level cyclists, who were more consciously attuned to their bodies and their own effort sense, were more accurate in their prediction than low-level cyclists. It is concluded that the randomized constant workloads test that is both shorter and less strenuous would be more convenient for high-level athletes.

  4. Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

    PubMed

    Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

    2017-04-06

    When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task.

  5. Leisure-time physical activity in relation to occupational physical activity among women

    PubMed Central

    Ekenga, Christine C.; Parks, Christine G.; Wilson, Lauren E.; Sandler, Dale P.

    2017-01-01

    Objective To examine the association between occupational physical activity and leisure-time physical activity among US women in the Sister Study. Methods We conducted a cross-sectional study of 26,334 women who had been employed in their current job for at least 1 year at baseline (2004–2009). Occupational physical activity was self-reported and leisure-time physical activity was estimated in metabolic equivalent hours per week. Log multinomial regression was used to evaluate associations between occupational (sitting, standing, manually active) and leisure-time (insufficient, moderate, high) activity. Models were adjusted for age, race/ethnicity, education, income, geographic region, and body mass index. Results Only 54% of women met or exceeded minimum recommended levels of leisure-time physical activity (moderate 32% and high 22%). Women who reported sitting (PR = 0.82, 95% CI: 0.74–0.92) or standing (PR = 0.84, 95% CI: 0.75–0.94) most of the time at work were less likely to meet the requirements for high leisure-time physical activity than manually active workers. Associations were strongest among women living in the Northeast and the South. Conclusion In this nationwide study, low occupational activity was associated with lower leisure-time physical activity. Women who are not active in the workplace may benefit from strategies to promote leisure-time physical activity. PMID:25773471

  6. Predicting mountain lion activity using radiocollars equipped with mercury tip-sensors

    USGS Publications Warehouse

    Janis, Michael W.; Clark, Joseph D.; Johnson, Craig

    1999-01-01

    Radiotelemetry collars with tip-sensors have long been used to monitor wildlife activity. However, comparatively few researchers have tested the reliability of the technique on the species being studied. To evaluate the efficacy of using tip-sensors to assess mountain lion (Puma concolor) activity, we radiocollared 2 hand-reared mountain lions and simultaneously recorded their behavior and the associated telemetry signal characteristics. We noted both the number of pulse-rate changes and the percentage of time the transmitter emitted a fast pulse rate (i.e., head up) within sampling intervals ranging from 1-5 minutes. Based on 27 hours of observations, we were able to correctly distinguish between active and inactive behaviors >93% of the time using a logistic regression model. We present several models to predict activity of mountain lions; the selection of which to us would depend on study objectives and logistics. Our results indicate that field protocols that use only pulse-rate changes to indicate activity can lead to significant classification errors.

  7. A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

    PubMed Central

    Zhang, Zhongqiu; Sun, Liren; Xu, Cui

    2016-01-01

    The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day's Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days' AQI prediction. PMID:27597861

  8. Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex

    PubMed Central

    Wittmann, Marco K.; Kolling, Nils; Akaishi, Rei; Chau, Bolton K. H.; Brown, Joshua W.; Nelissen, Natalie; Rushworth, Matthew F. S.

    2016-01-01

    In many natural environments the value of a choice gradually gets better or worse as circumstances change. Discerning such trends makes predicting future choice values possible. We show that humans track such trends by comparing estimates of recent and past reward rates, which they are able to hold simultaneously in the dorsal anterior cingulate cortex (dACC). Comparison of recent and past reward rates with positive and negative decision weights is reflected by opposing dACC signals indexing these quantities. The relative strengths of time-linked reward representations in dACC predict whether subjects persist in their current behaviour or switch to an alternative. Computationally, trend-guided choice can be modelled by using a reinforcement-learning mechanism that computes a longer-term estimate (or expectation) of prediction errors. Using such a model, we find a relative predominance of expected prediction errors in dACC, instantaneous prediction errors in the ventral striatum and choice signals in the ventromedial prefrontal cortex. PMID:27477632

  9. Are transit times key process-based tools for regional classification and prediction in ungauged basins?

    NASA Astrophysics Data System (ADS)

    Tetzlaff, D.; Soulsby, C.; Hrachowitz, M.; Speed, M.

    2009-04-01

    In recent years, transit times (TTs) have been increasingly explored as a process-based tools for conceptualising hydrological processes in an integrated manner at a range of scales. Traditionally the identification of the appropriate transit time distribution (TTD) for a hydrological system (e.g. hillslope or catchment), and the derivation of metrics such as the mean transit time (MTT) have required quantitative assessment of input-output relationships for conservative tracers using lumped parameter models. Such work has allowed the main landscape controls on TTs to be identified and facilitated the prediction of MTT in ungauged basins in particular geomorphic provinces. This has shown TT to be a useful diagnostic index of similarity that can be valuable in process-based catchment classification. In this contribution, we used well-constrained MTT estimates (with uncertainty) from 32 experimental catchments (1 to 250km2 in area) with contrasting geologic, topographic, pedologic and climatic characteristics in Scotland. The MTT was highly variable ranging from 30 days to ca. 1200 days for individual catchments. Moreover, MTT was also found to be closely correlated with key hydrometric design statistics such as the Q95, Q5, Mean Annual Flood (MAF) and the slope of the hydrograph recession curve. Analysis of the TT estimates, in conjunction with GIS-based quantitative assessment of key landscape controls, showed that MTT could be predicted to within 25% for ungauged basins from catchment soil cover, drainage density and topographic wetness index. For ungauged basins it was found that the hydrometric design statistics (Q95, Q5, MAF and the recession slope) could be more simply and accurately forecasted from MTT predictions than a single set of catchment characteristics. We demonstrate that TTs - predicted from mapped landscape characteristics - are useful integrating diagnostic metrics for regional classification, prediction and process assessment in ungauged montane

  10. Predicting enhancer activity and variant impact using gkm-SVM.

    PubMed

    Beer, Michael A

    2017-01-25

    We participated in the Critical Assessment of Genome Interpretation eQTL challenge to further test computational models of regulatory variant impact and their association with human disease. Our prediction model is based on a discriminative gapped-kmer SVM (gkm-SVM) trained on genome-wide chromatin accessibility data in the cell type of interest. The comparisons with massively parallel reporter assays (MPRA) in lymphoblasts show that gkm-SVM is among the most accurate prediction models even though all other models used the MPRA data for model training, and gkm-SVM did not. In addition, we compare gkm-SVM with other MPRA datasets and show that gkm-SVM is a reliable predictor of expression and that deltaSVM is a reliable predictor of variant impact in K562 cells and mouse retina. We further show that DHS (DNase-I hypersensitive sites) and ATAC-seq (assay for transposase-accessible chromatin using sequencing) data are equally predictive substrates for training gkm-SVM, and that DHS regions flanked by H3K27Ac and H3K4me1 marks are more predictive than DHS regions alone.

  11. A real-time predictive simulation of abdominal viscera positions during quiet free breathing.

    PubMed

    Hostettler, A; Nicolau, S A; Rémond, Y; Marescaux, J; Soler, L

    2010-12-01

    Prediction of abdominal viscera and tumour positions during free breathing is a major challenge from which several medical applications could benefit. For instance, in radiotherapy it would reduce the healthy tissue irradiation. In this paper, we present a new approach to predict real-time abdominal viscera positions during free breathing. Our method needs an abdo-thoracic 3D preoperative CT or MR image, a second one limited to the diaphragmatic area, and a tracking of the patient's skin position. First, a physical analysis of the breathing motion shows it is possible to predict accurately abdominal viscera positions from the skin position and a modelling of the diaphragm motion. Secondly, a quantitative analysis of the skin and organ motion allows us to define the demands our real-time simulation has to fulfill. Then, we present in detail all the necessary steps of our original method to compute a deformation field from data extracted in both 3D preoperative image and skin surface tracking. Finally, experiments carried out with two human data show that our simulation model predicts abdominal viscera positions, such as liver, kidneys or spleen, at 50 Hz with an accuracy within 2-3 mm.

  12. Methods and on-farm devices to predict calving time in cattle.

    PubMed

    Saint-Dizier, Marie; Chastant-Maillard, Sylvie

    2015-09-01

    In livestock farming, accurate prediction of calving time is a key factor for profitability and animal welfare. The most accurate and sensitive methods to date for prediction of calving within 24 h are the measurement of pelvic ligament relaxation and assays for circulating progesterone and oestradiol-17β. Conversely, the absence of calving within the next 12-24 h can be accurately predicted by the measurement of incremental daily decrease in vaginal temperature and by the combination of pelvic ligament relaxation and teat filling estimates. Continuous monitoring systems can detect behavioural changes occurring on the actual day of calving, some of them being accentuated in the last few hours before delivery; standing/lying transitions, tail raising, feeding time, and dry matter and water intakes differ between cows with dystocia and those with eutocia. Use of these behavioural changes has the potential to improve the management of calving. Currently, four types of devices for calving detection are on the market: inclinometers and accelerometers detecting tail raising and overactivity, abdominal belts monitoring uterine contractions, vaginal probes detecting a decrease in vaginal temperature and expulsion of the allantochorion, and devices placed in the vagina or on the vulvar lips that detect calf expulsion. The performance of these devices under field conditions and their capacity to predict dystocia require further investigation.

  13. Pulse-echo ultrasound transit time spectroscopy: A comparison of experimental measurement and simulation prediction.

    PubMed

    Wille, Marie-Luise; Almualimi, Majdi A; Langton, Christian M

    2016-01-01

    Considering ultrasound propagation through complex composite media as an array of parallel sonic rays, a comparison of computer-simulated prediction with experimental data has previously been reported for transmission mode (where one transducer serves as transmitter, the other as receiver) in a series of 10 acrylic step-wedge samples, immersed in water, exhibiting varying degrees of transit time inhomogeneity. In this study, the same samples were used but in pulse-echo mode, where the same ultrasound transducer served as both transmitter and receiver, detecting both 'primary' (internal sample interface) and 'secondary' (external sample interface) echoes. A transit time spectrum was derived, describing the proportion of sonic rays with a particular transit time. A computer simulation was performed to predict the transit time and amplitude of various echoes created, and compared with experimental data. Applying an amplitude-tolerance analysis, 91.7% ± 3.7% of the simulated data were within ±1 standard deviation of the experimentally measured amplitude-time data. Correlation of predicted and experimental transit time spectra provided coefficients of determination (R(2)%) ranging from 100.0% to 96.8% for the various samples tested. The results acquired from this study provide good evidence for the concept of parallel sonic rays. Furthermore, deconvolution of experimental input and output signals has been shown to provide an effective method to identify echoes otherwise lost due to phase cancellation. Potential applications of pulse-echo ultrasound transit time spectroscopy include improvement of ultrasound image fidelity by improving spatial resolution and reducing phase interference artefacts.

  14. Can a Clinical Test of Reaction Time Predict a Functional Head-Protective Response?

    PubMed Central

    ECKNER, JAMES T.; LIPPS, DAVID B.; KIM, HOGENE; RICHARDSON, JAMES K.; ASHTON-MILLER, JAMES A.

    2015-01-01

    Purpose Reaction time is commonly prolonged after a sport-related concussion. Besides being a marker for injury, a rapid reaction time is necessary for protective maneuvers that can reduce the frequency and severity of additional head impacts. The purpose of this study was to determine whether a clinical test of simple visuomotor reaction time predicted the time taken to raise the hands to protect the head from a rapidly approaching ball. Methods Twenty-six healthy adult participants recruited from campus and community recreation and exercise facilities completed two experimental protocols during a single session: a manual visuomotor simple reaction time test (RTclin) and a sport-related head-protective response (RTsprt). RTclin measured the time required to catch a thin vertically oriented device on its release by the tester and was calculated from the distance the device fell before being arrested. RTsprt measured the time required to raise the hands from waist level to block a foam tennis ball fired toward the subject’s face from an air cannon and was determined using an optoelectronic camera system. A correlation coefficient was calculated between RTclin and RTsprt, with linear regression used to assess for effect modification by other covariates. Results A strong positive correlation was found between RTclin and RTsprt (r = 0.725, P < 0.001) independent of age, gender, height, or weight. Conclusions RTclin is predictive of a functional sport-related head-protective response. To our knowledge, this is the first demonstration of a clinical test predicting the ability to protect the head in a simulated sport environment. This correlation with a functional head-protective response is a relevant consideration for the potential use of RTclin as part of a multifaceted concussion assessment program. PMID:20689458