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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. PMID:24182516

  3. Thrombin time and anti-IIa dabigatran's activity: hypothesis of thrombin time's predictive value.

    PubMed

    Le Guyader, Maïlys; Kaabar, Mohammed; Lemaire, Pierre; Pineau Vincent, Fabienne

    2015-01-01

    Dabigatran etexilate (Pradaxa®) is a new oral anticoagulant, competitive inhibitor, selective, fast, direct and reversible of thrombin. Dabigatran has an impact on a large panel of used coagulation tests. There is no relationship between thrombin time's lengthening and anti-IIa activity. This study defines thrombin time's predictive value, when its time is normal. The result of negative value is 97,6%. 255 patients were studied between January 2013 and July 2014. Thrombin time and anti-IIa activity were dosed for each patient. This study can be an assistant for therapeutic decision for laboratories without specialized test. PMID:26489812

  4. Predicting Child Physical Activity and Screen Time: Parental Support for Physical Activity and General Parenting Styles

    PubMed Central

    Crain, A. Lauren; Senso, Meghan M.; Levy, Rona L.; Sherwood, Nancy E.

    2014-01-01

    Objective: To examine relationships between parenting styles and practices and child moderate-to-vigorous physical activity (MVPA) and screen time. Methods: Participants were children (6.9 ± 1.8 years) with a body mass index in the 70–95th percentile and their parents (421 dyads). Parent-completed questionnaires assessed parental support for child physical activity (PA), parenting styles and child screen time. Children wore accelerometers to assess MVPA. Results: Parenting style did not predict MVPA, but support for PA did (positive association). The association between support and MVPA, moreover, varied as a function of permissive parenting. For parents high in permissiveness, the association was positive (greater support was related to greater MVPA and therefore protective). For parents low in permissiveness, the association was neutral; support did not matter. Authoritarian and permissive parenting styles were both associated with greater screen time. Conclusions: Parenting practices and styles should be considered jointly, offering implications for tailored interventions. PMID:24812256

  5. 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. PMID:25676913

  6. Real-time prediction of magnetospheric activity using the Boyle Index

    NASA Astrophysics Data System (ADS)

    Bala, Ramkumar; Reiff, P. H.; Landivar, J. E.

    2009-04-01

    We present a new algorithm with an improvement in the accuracy and lead time in short-term space weather predictions by coupling the Boyle Index, Φ = 10-4ν2 + 11.7Bsin3(?/2) kV, to artificial neural networks. The algorithm takes inputs from ACE and a handful of ground-based magnetometers to predict the next upcoming Kp in real time. The model yields a correlation coefficient of over 86% when predicting Kp with a lead time of 1 hour and over 85% for a 2 hour ahead prediction, significantly larger than the Kp persistence of 0.80. The Boyle Index, available in near-real time from http://space.rice.edu/ISTP/wind.html, has been in use for over 5 years now to predict geomagnetic activity. The logarithm of both 3-hour and 1-hour averages of the Boyle Index correlates well with the following Kp: Kp = 8.93 log10 < Boyle Index> -12.55. Using the Boyle Index alone, the algorithm yields a correlation coefficient of 85% when predicting Kp with a lead time of 1 hour and over 84% for a 3 hour ahead prediction, nearly as good as when using Kp in the history but without any possibility of "persistence contamination." Although the Boyle Index generally overestimates the polar cap potential for severe events, it does predict that severe activity will occur. Also, 1-hour value less than 100 kV is a good indicator that the magnetosphere will be quiet. However, some storm events with Kp > 6 occur when the Boyle Index is relatively low; the new algorithm is successful in predicting those events by capturing the influence of preconditioning.

  7. Prediction of switching time between movement preparation and execution by neural activity in monkey premotor cortex.

    PubMed

    Li, Hongbao; Liao, Yuxi; Wang, Yiwen; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

    2015-01-01

    Premotor cortex is a higher level cortex than primary motor cortex in movement controlling hierarchy, which contributes to the motor preparation and execution simultaneously during the planned movement. The mediation mechanism from movement preparation to execution has attracted many scientists' attention. Gateway hypothesis is one possible explanation that some neurons act as "gating" to release the movement intention at the "on-go" cue. We propose to utilize a local-learning based feature extraction method to target the neurons in premotor cortex, which functionally contribute mostly to the discrimination between motor preparation and execution without tuning information to either target or movement trajectory. Then the support vector machine is utilized to predict the single trial switching time. With top three functional "gating" neurons, the prediction accuracy rate of the switching time is above 90%, which indicates the potential of asynchronous BMI control using premotor cortical activity. PMID:26736827

  8. 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

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

    PubMed

    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

  10. 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.

  11. Measurement of exercise habits and prediction of leisure-time activity in established exercise.

    PubMed

    Tappe, Karyn A; Glanz, Karen

    2013-01-01

    Habit formation may be important to maintaining repetitive healthy behaviors like exercise. Existing habit questionnaires only measure part of the definition of habit (automaticity; frequency). A novel habit questionnaire was evaluated that measured contextual cueing. We designed a two-stage observational cohort study of regular exercisers. For stage 1, we conducted an in-person interview on a university campus. For stage 2, we conducted an internet-based survey. Participants were 156 adults exercising at least once per week. A novel measure, The Exercise Habit Survey (EHS) assessed contextual cueing through 13 questions on constancy of place, time, people, and exercise behaviors. A subset of the Self-Report Habit Index (SRHI), measuring automaticity, was also collected along with measures of intention and self-efficacy, and the International Physical Activity Questionnaire (IPAQ), leisure-time section. The EHS was evaluated using factor analysis and test-retest reliability. Its correlation to other exercise predictors and exercise behavior was evaluated using Pearson's r and hierarchical regression. Results suggested that the EHS comprised four subscales (People, Place, Time, Exercise Constancy). Only Exercise Constancy correlated significantly with SRHI. Only the People subscale predicted IPAQ exercise metabolic equivalents. The SRHI was a strong predictor. Contextual cueing is an important aspect of habit but measurement methodologies warrant refinement and comparison by different methods. PMID:23384089

  12. 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

  13. 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. PMID:23893208

  14. Predictive spark timing method

    SciTech Connect

    Tang, D.L.; Chang, M.F.; Sultan, M.C.

    1990-01-09

    This patent describes a method of determining spark time in a spark timing system of an internal combustion engine having a plurality of cylinders and a spark period for each cylinder in which a spark occurs. It comprises: generating at least one crankshaft position reference pulse for each spark firing event, the reference pulse nearest the next spark being set to occur within a same cylinder event as the next spark; measuring at least two reference periods between recent reference pulses; calculating the spark timing synchronously with crankshaft position by performing the calculation upon receipt of the reference pulse nearest the next spark; predicting the engine speed for the next spark period from at least two reference periods including the most recent reference period; and based on the predicted speed, calculating a spark time measured from the the reference pulse nearest the next spark.

  15. How Curriculum and Classroom Achievement Predict Teacher Time on Lecture- and Inquiry-Based Mathematics Activities

    ERIC Educational Resources Information Center

    Kaufman, Julia H.; Rita Karam; Pane, John F.; Junker, Brian W.

    2012-01-01

    This study drew on data from a large, randomized trial of Cognitive Tutor Algebra (CTA) in high-poverty settings to investigate how mathematics curricula and classroom achievement related to teacher reports of time spent on inquiry-based and lecture-based mathematics activities. We found that teachers using the CTA curriculum reported more time on…

  16. Long-term prediction of solar and geomagnetic activity daily time series using singular spectrum analysis and fuzzy descriptor models

    NASA Astrophysics Data System (ADS)

    Mirmomeni, M.; Kamaliha, E.; Shafiee, M.; Lucas, C.

    2009-09-01

    Of the various conditions that affect space weather, Sun-driven phenomena are the most dominant. Cyclic solar activity has a significant effect on the Earth, its climate, satellites, and space missions. In recent years, space weather hazards have become a major area of investigation, especially due to the advent of satellite technology. As such, the design of reliable alerting and warning systems is of utmost importance, and international collaboration is needed to develop accurate short-term and long-term prediction methodologies. Several methods have been proposed and implemented for the prediction of solar and geomagnetic activity indices, but problems in predicting the exact time and magnitude of such catastrophic events still remain. There are, however, descriptor systems that describe a wider class of systems, including physical models and non-dynamic constraints. It is well known that the descriptor system is much tighter than the state-space expression for representing real independent parametric perturbations. In addition, the fuzzy descriptor models as a generalization of the locally linear neurofuzzy models are general forms that can be trained by constructive intuitive learning algorithms. Here, we propose a combined model based on fuzzy descriptor models and singular spectrum analysis (SSA) (FD/SSA) to forecast a number of geomagnetic activity indices in a manner that optimizes a fuzzy descriptor model for each of the principal components obtained from singular spectrum analysis and recombines the predicted values so as to transform the geomagnetic activity time series into natural chaotic phenomena. The method has been applied to predict two solar and geomagnetic activity indices: geomagnetic aa and solar wind speed (SWS) of the solar wind index. The results demonstrate the higher power of the proposed method-- compared to other methods -- for predicting solar activity.

  17. Temporal evolution of oscillatory activity predicts performance in a choice-reaction time reaching task.

    PubMed

    Perfetti, Bernardo; Moisello, Clara; Landsness, Eric C; Kvint, Svetlana; Pruski, April; Onofrj, Marco; Tononi, Giulio; Ghilardi, M Felice

    2011-01-01

    In this study, we characterized the patterns and timing of cortical activation of visually guided movements in a task with critical temporal demands. In particular, we investigated the neural correlates of motor planning and on-line adjustments of reaching movements in a choice-reaction time task. High-density electroencephalography (EEG, 256 electrodes) was recorded in 13 subjects performing reaching movements. The topography of the movement-related spectral perturbation was established across five 250-ms temporal windows (from prestimulus to postmovement) and five frequency bands (from theta to beta). Nine regions of interest were then identified on the scalp, and their activity was correlated with specific behavioral outcomes reflecting motor planning and on-line adjustments. Phase coherence analysis was performed between selected sites. We found that motor planning and on-line adjustments share similar topography in a fronto-parietal network, involving mostly low frequency bands. In addition, activities in the high and low frequency ranges have differential function in the modulation of attention with the former reflecting the prestimulus, top-down processes needed to promote timely responses, and the latter the planning and control of sensory-motor processes. PMID:21047934

  18. 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. PMID:26009612

  19. Left anterior cingulate activity predicts intra-individual reaction time variability in healthy adults.

    PubMed

    Johnson, Beth P; Pinar, Ari; Fornito, Alex; Nandam, L Sanjay; Hester, Robert; Bellgrove, Mark A

    2015-06-01

    Within-subject, or intra-individual, variability in reaction time (RT) is increasingly recognised as an important indicator of the efficiency of attentional control, yet there have been few investigations of the neural correlates of trial-to-trial RT variability in healthy adults. We sought to determine the neural correlates of intra-individual RT variability during a go/no-go response inhibition task in 27 healthy, male participants. We found that reduced trial-to-trial RT variability (i.e. greater response stability) was significantly associated with greater activation in the left pregenual anterior cingulate. These results support the role of the left anterior cingulate in the dynamic control of attention and efficient response selection. Greater understanding of intra-individual RT variability and top-down attentional control in healthy adults may help to inform disorders that impact executive/attentional control, such as attention deficit hyperactivity disorder and schizophrenia. PMID:25791710

  20. 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

  1. 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. PMID:26449155

  2. Ongoing Activity in Temporally Coherent Networks Predicts Intra-Subject Fluctuation of Response Time to Sporadic Executive Control Demands

    PubMed Central

    Nozawa, Takayuki; Sugiura, Motoaki; Yokoyama, Ryoichi; Ihara, Mizuki; Kotozaki, Yuka; Miyauchi, Carlos Makoto; Kanno, Akitake; Kawashima, Ryuta

    2014-01-01

    Can ongoing fMRI BOLD signals predict fluctuations in swiftness of a person’s response to sporadic cognitive demands? This is an important issue because it clarifies whether intrinsic brain dynamics, for which spatio-temporal patterns are expressed as temporally coherent networks (TCNs), have effects not only on sensory or motor processes, but also on cognitive processes. Predictivity has been affirmed, although to a limited extent. Expecting a predictive effect on executive performance for a wider range of TCNs constituting the cingulo-opercular, fronto-parietal, and default mode networks, we conducted an fMRI study using a version of the color–word Stroop task that was specifically designed to put a higher load on executive control, with the aim of making its fluctuations more detectable. We explored the relationships between the fluctuations in ongoing pre-trial activity in TCNs and the task response time (RT). The results revealed the existence of TCNs in which fluctuations in activity several seconds before the onset of the trial predicted RT fluctuations for the subsequent trial. These TCNs were distributed in the cingulo-opercular and fronto-parietal networks, as well as in perceptual and motor networks. Our results suggest that intrinsic brain dynamics in these networks constitute “cognitive readiness,” which plays an active role especially in situations where information for anticipatory attention control is unavailable. Fluctuations in these networks lead to fluctuations in executive control performance. PMID:24901995

  3. Predicting intrinsic brain activity.

    PubMed

    Craddock, R Cameron; Milham, Michael P; LaConte, Stephen M

    2013-11-15

    Multivariate supervised learning methods exhibit a remarkable ability to decode externally driven sensory, behavioral, and cognitive states from functional neuroimaging data. Although they are typically applied to task-based analyses, supervised learning methods are equally applicable to intrinsic effective and functional connectivity analyses. The obtained models of connectivity incorporate the multivariate interactions between all brain regions simultaneously, which will result in a more accurate representation of the connectome than the ones available with standard bivariate methods. Additionally the models can be applied to decode or predict the time series of intrinsic brain activity of a region from an independent dataset. The obtained prediction accuracy provides a measure of the integration between a brain region and other regions in its network, as well as a method for evaluating acquisition and preprocessing pipelines for resting state fMRI data. This article describes a method for learning multivariate models of connectivity. The method is applied in the non-parametric prediction accuracy, influence, and reproducibility-resampling (NPAIRS) framework, to study the regional variation of prediction accuracy and reproducibility (Strother et al., 2002). The resulting spatial distribution of these metrics is consistent with the functional hierarchy proposed by Mesulam (1998). Additionally we illustrate the utility of the multivariate regression connectivity modeling method for optimizing experimental parameters and assessing the quality of functional neuroimaging data. PMID:23707580

  4. Perceptual Distortions in Pitch and Time Reveal Active Prediction and Support for an Auditory Pitch-Motion Hypothesis

    PubMed Central

    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. PMID:23936462

  5. The Contribution of Former Work-Related Activity Levels to Predict Physical Activity and Sedentary Time during Early Retirement: Moderating Role of Educational Level and Physical Functioning

    PubMed Central

    Van Dyck, Delfien; Cardon, Greet; Deforche, Benedicte; De Bourdeaudhuij, Ilse

    2015-01-01

    Background The transition to retirement introduces a decline in total physical activity and an increase in TV viewing time. Nonetheless, as more time becomes available, early retirement is an ideal stage to implement health interventions. Therefore, knowledge on specific determinants of physical activity and sedentary time is needed. Former work-related physical activity has been proposed as a potential determinant, but concrete evidence is lacking. The aim of this study was to examine if former work-related sitting, standing, walking or vigorous activities predict physical activity and sedentary time during early retirement. Additionally, moderating effects of educational level and physical functioning were examined. Methods In total, 392 recently retired Belgian adults (>6 months, <5 years) completed the International Physical Activity Questionnaire, the SF-36 Health Survey and a questionnaire on sociodemographics and former work-related activities. Generalized linear regression analyses were conducted in R. Moderating effects were examined by adding cross-products to the models. Results More former work-related sitting was predictive of more screen time during retirement. Lower levels of former work-related vigorous activities and higher levels of former work-related walking were associated with respectively more cycling for transport and more walking for transport during retirement. None of the predictors significantly explained passive transportation, cycling and walking for recreation, and leisure-time moderate-to-vigorous physical activity during retirement. Several moderating effects were found, but the direction of the interactions was not univocal. Conclusions Former-work related behaviors are of limited importance to explain physical activity during early retirement, so future studies should focus on other individual, social and environmental determinants. Nonetheless, adults who previously had a sedentary job had higher levels of screen time during

  6. The Value of a Panel of Autoantibodies for Predicting the Activity of Lupus Nephritis at Time of Renal Biopsy

    PubMed Central

    Moroni, Gabriella; Quaglini, Silvana; Radice, Antonella; Trezzi, Barbara; Raffiotta, Francesca; Messa, Piergiorgio; Sinico, Renato Alberto

    2015-01-01

    Few studies have correlated serum biomarkers with renal histology, the gold standard for renal activity, in lupus nephritis (LN). We tested a panel of autoantibodies and complement at the time of kidney biopsy and after treatment. Anti-dsDNA, anti-nucleosome, anti-ribosome P, and anti-C1q antibodies and C3/C4 were measured in 107 patients with LN at the time of renal biopsy and after 6–12 months and were correlated with clinical/histological parameters. At multivariate analysis, high titers of anti-C1q antibodies or of anti-dsDNA antibodies (P = 0.005, OR = 8.67, CI: 2.03–37.3) were the independent predictors that discriminate proliferative from nonproliferative LN. All the immunological parameters, except anti-ribosome, showed a significant correlation with activity index but not with chronicity index. Only anti-C1q showed a significant correlation with the amount of proteinuria (R = 0.2, P = 0.03). None of the immunological parameters were predictive of remission at 6 and 12 months. We found that anti-C1q alone or in combination with anti-dsDNA emerged as the most reliable test in differentiating proliferative and nonproliferative LN. Anti-C1q was the only test correlated with the clinical presentation of LN. After treatment, the titre of the autoantibodies was significantly reduced, but none was predictive of remission. PMID:25815344

  7. The value of a panel of autoantibodies for predicting the activity of lupus nephritis at time of renal biopsy.

    PubMed

    Moroni, Gabriella; Quaglini, Silvana; Radice, Antonella; Trezzi, Barbara; Raffiotta, Francesca; Messa, Piergiorgio; Sinico, Renato Alberto

    2015-01-01

    Few studies have correlated serum biomarkers with renal histology, the gold standard for renal activity, in lupus nephritis (LN). We tested a panel of autoantibodies and complement at the time of kidney biopsy and after treatment. Anti-dsDNA, anti-nucleosome, anti-ribosome P, and anti-C1q antibodies and C3/C4 were measured in 107 patients with LN at the time of renal biopsy and after 6-12 months and were correlated with clinical/histological parameters. At multivariate analysis, high titers of anti-C1q antibodies or of anti-dsDNA antibodies (P = 0.005, OR = 8.67, CI: 2.03-37.3) were the independent predictors that discriminate proliferative from nonproliferative LN. All the immunological parameters, except anti-ribosome, showed a significant correlation with activity index but not with chronicity index. Only anti-C1q showed a significant correlation with the amount of proteinuria (R = 0.2, P = 0.03). None of the immunological parameters were predictive of remission at 6 and 12 months. We found that anti-C1q alone or in combination with anti-dsDNA emerged as the most reliable test in differentiating proliferative and nonproliferative LN. Anti-C1q was the only test correlated with the clinical presentation of LN. After treatment, the titre of the autoantibodies was significantly reduced, but none was predictive of remission. PMID:25815344

  8. Time away from work predicts later cognitive function: Differences by activity during leave

    PubMed Central

    Leist, Anja K.; Glymour, M Maria; Mackenbach, Johan P; van Lenthe, Frank J; Avendano, Mauricio

    2013-01-01

    Purpose To examine how different activities performed during employment gaps are associated with later cognitive function and change. Method Five cognitive measures were used to indicate cognitive impairment of 18,259 respondents to the Survey of Health, Ageing, and Retirement in Europe (age 50-73) in 2004/5 or 2006/7. Using complete employment histories, employment gaps of six months or more between ages 25 and 65 were identified. Results Controlling for early-life socioeconomic status, school performance, and education, higher risk of cognitive impairment was associated with employment gaps described as unemployment (Odds Ratio [OR] = 1.18, 95 % Confidence Interval [CI] 1.04, 1.35) and sickness (OR = 1.78, 95 % CI 1.52, 2.09). In contrast, lower risk of cognitive impairment was associated with employment gaps described as training (OR = 0.73, 95 % CI 0.52, 1.01) or maternity (OR = 0.65, 95 % CI 0.57, 0.79). In longitudinal mixed effects models, training and maternity spells were associated with lower two-year aging-related cognitive decline. Discussion Periods away from work described as unemployment or sickness are associated with lower cognitive function, whereas maternity and training spells are associated with better late-life cognitive function. Both causation and selection mechanisms may explain these findings. PMID:23889855

  9. Prediction of landslide activation at locations in Beskidy Mountains using standard and real-time monitoring methods

    NASA Astrophysics Data System (ADS)

    Bednarczyk, Z.

    2012-04-01

    The paper presents landslide monitoring methods used for prediction of landslide activity at locations in the Carpathian Mountains (SE Poland). Different types of monitoring methods included standard and real-time early warning measurement with use of hourly data transfer to the Internet were used. Project financed from the EU funds was carried out for the purpose of public road reconstruction. Landslides with low displacement rates (varying from few mm to over 5cm/year) had size of 0.4-2.2mln m3. Flysch layers involved in mass movements represented mixture of clayey soils and sandstones of high moisture content and plasticity. Core sampling and GPR scanning were used for recognition of landslide size and depths. Laboratory research included index, IL oedometer, triaxial and direct shear laboratory tests. GPS-RTK mapping was employed for actualization of landslide morphology. Instrumentation consisted of standard inclinometers, piezometers and pore pressure transducers. Measurements were carried 2006-2011, every month. In May 2010 the first in Poland real-time monitoring system was installed at landslide complex over the Szymark-Bystra public road. It included in-place uniaxial sensors and 3D continuous inclinometers installed to the depths of 12-16m with tilt sensors every 0.5m. Vibrating wire pore pressure and groundwater level transducers together with automatic meteorological station analyzed groundwater and weather conditions. Obtained monitoring and field investigations data provided parameters for LEM and FEM slope stability analysis. They enabled prediction and control of landslide behaviour before, during and after stabilization or partly stabilization works. In May 2010 after the maximum precipitation (100mm/3hours) the rates of observed displacements accelerated to over 11cm in a few days and damaged few standard inclinometer installations. However permanent control of the road area was possible by continuous inclinometer installations. Comprehensive

  10. Normalized Elution Time Prediction Utility

    Energy Science and Technology Software Center (ESTSC)

    2011-02-17

    This program is used to compute the predicted normalized elution time (NET) for a list of peptide sequences. It includes the Kangas/Petritis neural network trained model, the Krokhin hydrophobicity model, and the Mant hydrophobicity model. In addition, it can compute the predicted strong cation exchange (SCX) fraction (on a 0 to 1 scale) in which a given peptide will appear.

  11. 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.

  12. Prediction of Therapy Tumor-Absorbed Dose Estimates in I-131 Radioimmunotherapy Using Tracer Data Via a Mixed-Model Fit to Time Activity

    PubMed Central

    Koral, Kenneth F.; Avram, Anca M.; Kaminski, Mark S.; Dewaraja, Yuni K.

    2012-01-01

    Abstract Background For individualized treatment planning in radioimmunotherapy (RIT), correlations must be established between tracer-predicted and therapy-delivered absorbed doses. The focus of this work was to investigate this correlation for tumors. Methods The study analyzed 57 tumors in 19 follicular lymphoma patients treated with I-131 tositumomab and imaged with SPECT/CT multiple times after tracer and therapy administrations. Instead of the typical least-squares fit to a single tumor's measured time-activity data, estimation was accomplished via a biexponential mixed model in which the curves from multiple subjects were jointly estimated. The tumor-absorbed dose estimates were determined by patient-specific Monte Carlo calculation. Results The mixed model gave realistic tumor time-activity fits that showed the expected uptake and clearance phases even with noisy data or missing time points. Correlation between tracer and therapy tumor-residence times (r=0.98; p<0.0001) and correlation between tracer-predicted and therapy-delivered mean tumor-absorbed doses (r=0.86; p<0.0001) were very high. The predicted and delivered absorbed doses were within±25% (or within±75 cGy) for 80% of tumors. Conclusions The mixed-model approach is feasible for fitting tumor time-activity data in RIT treatment planning when individual least-squares fitting is not possible due to inadequate sampling points. The good correlation between predicted and delivered tumor doses demonstrates the potential of using a pretherapy tracer study for tumor dosimetry-based treatment planning in RIT. PMID:22947086

  13. An activation-repolarization time metric to predict localized regions of high susceptibility to re-entry

    PubMed Central

    Child, Nicholas; Bishop, Martin J.; Hanson, Ben; Coronel, Ruben; Opthof, Tobias; Bourkens, Bastiaan; Walton, Richard; Efimov, Igor; Bostock, Julian; Hill, Yolanda; Rinaldi, Christopher A; Razavi, Reza; Gill, Jaswinder; Taggart, Peter

    2015-01-01

    Background Initiation of re-entrant ventricular tachycardia (VT) involves complex interactions between activation and repolarization wavefronts. Recent experimental work has identified the time interval between S2 repolarization proximal to a line of functional block and the activation at the adjacent distal side, as a critical determinant of re-entry. Objective We hypothesized: (1) an algorithm could be developed which would generate a spatial map of this interval (designated the “re-entry vulnerability index”-RVI); (2) that this would accurately identify a pathway of re-entry as well as rotor formation in animal experiments and in a computational model; and, (3) that it would be possible to generate an RVI map in humans during routine clinical procedures and co-register with anatomical and electrophysiological features. Methods and Results An algorithm was developed which sampled all points on a multielectrode grid and calculated RVI between all pairs of electrodes within a given radius. The algorithm successfully identified the spatial region with increased susceptibility to re-entry in an established Langendorff pig heart model and the site of re-entry and rotor formation in an optically mapped sheep heart model and corresponding computational simulations. The feasibility of RVI mapping was evaluated during a clinical procedure by co-registering with the anatomy and physiology in a patient undergoing a VT ablation. Conclusions We developed an algorithm to calculate a re-entry vulnerability index from intervals between local repolarization and activation times at all adjacent points over a multielectrode grid. The algorithm accurately identified the region of re-entry in two animal models of functional re-entry. The possibility of clinical application was demonstrated in a patient with VT. PMID:25863160

  14. Solar activity predicted with artificial intelligence

    NASA Astrophysics Data System (ADS)

    Lundstedt, Henrik

    The variability of solar activity has been described as a non-linear chaotic dynamic system. AI methods are therefore especially suitable for modelling and predicting solar activity. Many indicators of the solar activity have been used, such as sunspot numbers, F 10.7 cm solar radio flux, X-ray flux, and magnetic field data. Artificial neural networks have also been used by many authors to predict solar cycle activity. Such predictions will be discussed. A new attempt to predict the solar activity using SOHO/MDI high-time resolution solar magnetic field data is discussed. The purpose of this new attempt is to be able to predict episodic events and to predict occurrence of coronal mass ejections. These predictions will be a part of the Lund Space Weather Model.

  15. Expansion tube test time predictions

    NASA Technical Reports Server (NTRS)

    Gourlay, Christopher M.

    1988-01-01

    The interaction of an interface between two gases and strong expansion is investigated and the effect on flow in an expansion tube is examined. Two mechanisms for the unsteady Pitot-pressure fluctuations found in the test section of an expansion tube are proposed. The first mechanism depends on the Rayleigh-Taylor instability of the driver-test gas interface in the presence of a strong expansion. The second mechanism depends on the reflection of the strong expansion from the interface. Predictions compare favorably with experimental results. The theory is expected to be independent of the absolute values of the initial expansion tube filling pressures.

  16. 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

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

    PubMed

    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

  18. 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.

  19. 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.

  20. 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.

  1. Time prediction model of subway transfer.

    PubMed

    Zhou, Yuyang; Yao, Lin; Gong, Yi; Chen, Yanyan

    2016-01-01

    Walking time prediction aims to deduce waiting time and travel time for passengers and provide a quantitative basis for the subway schedule management. This model is founded based on transfer passenger flow and type of pedestrian facilities. Chaoyangmen station in Beijing was taken as the learning set to obtain the relationship between transfer walking speed and passenger volume. The sectional passenger volume of different facilities was calculated related to the transfer passage classification. Model parameters were computed by curve fitting with respect to various pedestrian facilities. The testing set contained four transfer stations with large passenger volume. It is validated that the established model is effective and practical. The proposed model offers a real-time prediction method with good applicability. It can provide transfer scheme reference for passengers, meanwhile, improve the scheduling and management of the subway operation. PMID:26835224

  2. 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

  3. Social support and physical activity as moderators of life stress in predicting baseline depression and change in depression over time in the Women’s Health Initiative

    PubMed Central

    Uebelacker, Lisa A.; Eaton, Charles B.; Weisberg, Risa; Sands, Megan; Williams, Carla; Calhounm, Darren; Manson, JoAnn E.; Denburg, Natalie L.; Taylor, Teletia

    2013-01-01

    Purpose To determine whether social support and/or physical activity buffer the association between stressors and increasing risk of depression symptoms at baseline and at 3 year follow-up. Methods This is a secondary analysis of data from the Women’s Health Initiative Observational Study. 91,912 community-dwelling post-menopausal women participated in this prospective cohort study. Depression symptoms were measured at baseline and 3 years later; social support, physical activity, and stressors were measured at baseline. Results Stressors at baseline, including verbal abuse, physical abuse, caregiving, social strain, negative life events, financial stress, low income, acute pain, and a greater number of chronic medical conditions, were all associated with higher levels of depression symptoms at baseline and new onset elevated symptoms at three year follow-up. Social support and physical activity were associated with lower levels of depressive symptoms. Contrary to expectation, more social support at baseline strengthened the association between concurrent depression and physical abuse, social strain, caregiving, and low income. Similarly, more social support at baseline increased the association between financial stress, income, and pain on new-onset depression 3 years later. Physical activity similarly moderated the effect of caregiving, income, and pain on depression symptoms at baseline. Conclusion Stressors, social support, and physical activity showed predicted main effect associations with depression. Multiplicative interactions were small in magnitude and in the opposite direction of what was expected. PMID:23644722

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Viscoelastic behavior and life-time predictions

    NASA Technical Reports Server (NTRS)

    Dillard, D. A.; Brinson, H. F.

    1985-01-01

    Fiber reinforced plastics were considered for many structural applications in automotive, aerospace and other industries. A major concern was and remains the failure modes associated with the polymer matrix which serves to bind the fibers together and transfer the load through connections, from fiber to fiber and ply to ply. An accelerated characterization procedure for prediction of delayed failures was developed. This method utilizes time-temperature-stress-moisture superposition principles in conjunction with laminated plate theory. Because failures are inherently nonlinear, the testing and analytic modeling for both moduli and strength is based upon nonlinear viscoelastic concepts.

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

    PubMed Central

    Nazerfard, Ehsan; Cook, Diane J.

    2014-01-01

    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. PMID:25937847

  10. Time-dependence in mixture toxicity prediction

    PubMed Central

    Dawson, Douglas A.; Allen, Erin M.G.; Allen, Joshua L.; Baumann, Hannah J.; Bensinger, Heather M.; Genco, Nicole; Guinn, Daphne; Hull, Michael W.; Il'Giovine, Zachary J.; Kaminski, Chelsea M.; Peyton, Jennifer R.; Schultz, T. Wayne; Pöch, Gerald

    2014-01-01

    The value of time-dependent toxicity (TDT) data in predicting mixture toxicity was examined. Single chemical (A and B) and mixture (A + B) toxicity tests using Microtox® were conducted with inhibition of bioluminescence (Vibrio fischeri) being quantified after 15, 30 and 45-min of exposure. Single chemical and mixture tests for 25 sham (A1:A2) and 125 true (A:B) combinations had a minimum of seven duplicated concentrations with a duplicated control treatment for each test. Concentration/response (x/y) data were fitted to sigmoid curves using the five-parameter logistic minus one parameter (5PL-1P) function, from which slope, EC25, EC50, EC75, asymmetry, maximum effect, and r2 values were obtained for each chemical and mixture at each exposure duration. Toxicity data were used to calculate percentage-based TDT values for each individual chemical and mixture of each combination. Predicted TDT values for each mixture were calculated by averaging the TDT values of the individual components and regressed against the observed TDT values obtained in testing, resulting in strong correlations for both sham (r2 = 0.989, n = 25) and true mixtures (r2 = 0.944, n = 125). Additionally, regression analyses confirmed that observed mixture TDT values calculated for the 50% effect level were somewhat better correlated with predicted mixture TDT values than at the 25 and 75% effect levels. Single chemical and mixture TDT values were classified into five levels in order to discern trends. The results suggested that the ability to predict mixture TDT by averaging the TDT of the single agents was modestly reduced when one agent of the combination had a positive TDT value and the other had a minimal or negative TDT value. PMID:25446331

  11. 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

  12. Real Time Seismic Prediction while Drilling

    NASA Astrophysics Data System (ADS)

    Schilling, F. R.; Bohlen, T.; Edelmann, T.; Kassel, A.; Heim, A.; Gehring, M.; Lüth, S.; Giese, R.; Jaksch, K.; Rechlin, A.; Kopf, M.; Stahlmann, J.; Gattermann, J.; Bruns, B.

    2009-12-01

    Efficient and safe drilling is a prerequisite to enhance the mobility of people and goods, to improve the traffic as well as utility infrastructure of growing megacities, and to ensure the growing energy demand while building geothermal and in hydroelectric power plants. Construction within the underground is often building within the unknown. An enhanced risk potential for people and the underground building may arise if drilling enters fracture zones, karsts, brittle rocks, mixed solid and soft rocks, caves, or anthropogenic obstacles. Knowing about the material behavior ahead of the drilling allows reducing the risk during drilling and construction operation. In drilling operations direct observations from boreholes can be complemented with geophysical investigations. In this presentation we focus on “real time” seismic prediction while drilling which is seen as a prerequisite while using geophysical methods in modern drilling operations. In solid rocks P- and S-wave velocity, refraction and reflection as well as seismic wave attenuation can be used for the interpretation of structures ahead of the drilling. An Integrated Seismic Imaging System (ISIS) for exploration ahead of a construction is used, where a pneumatic hammer or a magnetostrictive vibration source generate repetitive signals behind the tunneling machine. Tube waves are generated which travel along the tunnel to the working face. There the tube waves are converted to mainly S- but also P-Waves which interact with the formation ahead of the heading face. The reflected or refracted waves travel back to the working front are converted back to tube waves and recorded using three-component geophones which are fit into the tips of anchor rods. In near real time, the ISIS software allows for an integrated 3D imaging and interpretation of the observed data, geological and geotechnical parameters. Fracture zones, heterogeneities, and variations in the rock properties can be revealed during the drilling

  13. Resource Selection Using Execution and Queue Wait Time Predictions

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

    We developed techniques to predict application execution times for instance-based learning with an average error of 33% of average run time. We developed techniques to predict queue wait times that included a simulation of scheduling algorithms and execution time predictions. We implemented these techniques for the NAS Origin cluster.

  14. 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.

  15. Limits of Predictability of Solar Activity

    NASA Astrophysics Data System (ADS)

    Kremliovsky, M. N.

    1995-07-01

    The study of a nonlinear chaotic map of 11-year cycle maxima evolution recently derived from observations is presented with the purpose of predicting the features of the long-term variability of solar activity. It is stressed that dynamical forecast is limited by the Lyapunov time and a statistical approach can be justified due to the ergodic properties of the chaotic evolution. The Gleissberg variation is described as a chaotic walk and its distribution over length is shown to be broad. The global minima are identified as laminar slots of temporal intermittency and their typical distribution over length is also given. We note that a long sunspot cycle can be used as a precursor of the global minimum and a close sequence of global minima (once in approximately 1500 2000 years) may be responsible for the climatic changes (Little Ice Ages).

  16. Prediction and monitoring of volcanic activities

    SciTech Connect

    Sudradjat, A.

    1986-07-01

    This paper summarizes the state of the art for predicting and monitoring volcanic activities, and it emphasizes the experience obtained by the Volcanological Survey Indonesia for active volcanoes. The limited available funds, the large number of active volcanoes to monitor, and the high population density of the volcanic area are the main problems encountered. Seven methods of volcano monitoring are applied to the active volcanoes of Indonesia: seismicity, ground deformation, gravity and magnetic studies, self-potential studies, petrochemistry, gas monitoring, and visual observation. Seismic monitoring augmented by gas monitoring has proven to be effective, particularly for predicting individual eruptions at the after-initial phase. However, the success of the prediction depends on the characteristics of each volcano. In general, the initial eruption phase is the most difficult phenomenon to predict. The preparation of hazard maps and the continuous awareness of the volcanic eruption are the most practical ways to mitigate volcanic danger.

  17. Neural predictive control for active buffet alleviation

    NASA Astrophysics Data System (ADS)

    Pado, Lawrence E.; Lichtenwalner, Peter F.; Liguore, Salvatore L.; Drouin, Donald

    1998-06-01

    The adaptive neural control of aeroelastic response (ANCAR) and the affordable loads and dynamics independent research and development (IRAD) programs at the Boeing Company jointly examined using neural network based active control technology for alleviating undesirable vibration and aeroelastic response in a scale model aircraft vertical tail. The potential benefits of adaptive control includes reducing aeroelastic response associated with buffet and atmospheric turbulence, increasing flutter margins, and reducing response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and thus loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Wind tunnel tests were undertaken on a rigid 15% scale aircraft in Boeing's mini-speed wind tunnel, which is used for testing at very low air speeds up to 80 mph. The model included a dynamically scaled flexible fail consisting of an aluminum spar with balsa wood cross sections with a hydraulically powered rudder. Neural predictive control was used to actuate the vertical tail rudder in response to strain gauge feedback to alleviate buffeting effects. First mode RMS strain reduction of 50% was achieved. The neural predictive control system was developed and implemented by the Boeing Company to provide an intelligent, adaptive control architecture for smart structures applications with automated synthesis, self-optimization, real-time adaptation, nonlinear control, and fault tolerance capabilities. It is designed to solve complex control problems though a process of automated synthesis, eliminating costly control design and surpassing it in many instances by accounting for real world non-linearities.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. ISOL Yield Predictions from Holdup-Time Measurements

    SciTech Connect

    Spejewski, Eugene H.; Carter, H Kennon; Mervin, Brenden T.; Prettyman, Emily S.; Kronenberg, Andreas; Stracener, Daniel W

    2008-01-01

    A formalism based on a simple model is derived to predict ISOL yields for all isotopes of a given element based on a holdup-time measurement of a single isotope of that element. Model predictions, based on parameters obtained from holdup-time measurements, are compared to independently-measured experimental values.

  3. 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...

  4. Prediction of the shock arrival time with SEP observations

    NASA Astrophysics Data System (ADS)

    Qin, G.; Zhang, M.; Rassoul, H. K.

    2009-09-01

    Real-time prediction of the arrival times at Earth of shocks is very important for space weather research. Recently, various models for shock propagation are used to forecast the shock arriving times (SATs) with information of initial coronal shock and flare from near real-time radio and X-ray data. In this paper, we add the use of solar energetic particles (SEP) observation to improve the shock arrival time (SAT) prediction. High-energy SEPs originating from flares move to the Earth much faster than the shocks related to the same flares. We develop an SAT prediction model by combining a well-known shock propagation model, STOA, and the analysis of SEPs detected at Earth. We demonstrate that the SAT predictions are improved by the new model with the help of 38-53 keV electron SEP observations. In particular, the correct prediction to false alarm ratio is improved significantly.

  5. On-time clinical phenotype prediction based on narrative reports

    PubMed Central

    Bejan, Cosmin A.; Vanderwende, Lucy; Evans, Heather L.; Wurfel, Mark M.; Yetisgen-Yildiz, Meliha

    2013-01-01

    In this paper we describe a natural language processing system which is able to predict whether or not a patient exhibits a specific phenotype using the information extracted from the narrative reports associated with the patient. Furthermore, the phenotypic annotations from our report dataset were performed at the report level which allows us to perform the prediction of the clinical phenotype at any point in time during the patient hospitalization period. Our experiments indicate that an important factor in achieving better results for this problem is to determine how much information to extract from the patient reports in the time interval between the patient admission time and the current prediction time. PMID:24551325

  6. 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) ...

  7. Predicting the Time Course of Individual Objects with MEG.

    PubMed

    Clarke, Alex; Devereux, Barry J; Randall, Billi; Tyler, Lorraine K

    2015-10-01

    To respond appropriately to objects, we must process visual inputs rapidly and assign them meaning. This involves highly dynamic, interactive neural processes through which information accumulates and cognitive operations are resolved across multiple time scales. However, there is currently no model of object recognition which provides an integrated account of how visual and semantic information emerge over time; therefore, it remains unknown how and when semantic representations are evoked from visual inputs. Here, we test whether a model of individual objects--based on combining the HMax computational model of vision with semantic-feature information--can account for and predict time-varying neural activity recorded with magnetoencephalography. We show that combining HMax and semantic properties provides a better account of neural object representations compared with the HMax alone, both through model fit and classification performance. Our results show that modeling and classifying individual objects is significantly improved by adding semantic-feature information beyond ∼200 ms. These results provide important insights into the functional properties of visual processing across time. PMID:25209607

  8. 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.

  9. 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

  10. 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…

  11. Nonstationary time series prediction combined with slow feature analysis

    NASA Astrophysics Data System (ADS)

    Wang, G.; Chen, X.

    2015-07-01

    Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

  12. Solar activity forecast: Spectral analysis and neurofuzzy prediction

    NASA Astrophysics Data System (ADS)

    Gholipour, Ali; Lucas, Caro; Araabi, Babak N.; Shafiee, Masoud

    2005-04-01

    Active research in the last two decades indicates that the physical precursor and solar dynamo techniques are preferred as practical tools for long-term prediction of solar activity. But why should we omit more than 23 cycles of solar activity history, and just use empirical methods or simple autoregressive methods on the basis of observations for the latest eight cycles? In this article, a method based on spectral analysis and neurofuzzy modeling is proposed that is capable of issuing very accurate long-term prediction of sunspot number time series. A locally linear neurofuzzy model is optimized for each of the principal components obtained from singular spectrum analysis, and the multi-step predicted values are recombined to make the sunspot number time series. The proposed method is used for solar cycles 22 and 23 and the results are remarkably good in comparison to the predictions made by solar dynamo and precursor methods. An early prediction of the maximum smoothed international sunspot number for cycle 24 is 145 in 2011 2012.

  13. 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.

  14. 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.

  15. Predicting the time derivative of local magnetic perturbations

    NASA Astrophysics Data System (ADS)

    Tóth, Gábor; Meng, Xing; Gombosi, Tamas I.; Rastätter, Lutz

    2014-01-01

    Some of the potentially most destructive effects of severe space weather storms are caused by the geomagnetically induced currents. Geomagnetically induced currents (GICs) can cause failures of electric transformers and result in widespread blackouts. GICs are induced by the time variability of the magnetic field and are closely related to the time derivative of the local magnetic field perturbation. Predicting dB/dt is rather challenging, since the local magnetic perturbations and their time derivatives are both highly fluctuating quantities, especially during geomagnetic storms. The currently available first principles-based and empirical models cannot predict the detailed minute-scale or even faster time variation of the local magnetic field. On the other hand, Pulkkinen et al. (2013) demonstrated recently that several models can predict with positive skill scores whether the horizontal component of dB/dt at a given magnetometer station will exceed some threshold value in a 20 min time interval. In this paper we investigate if one can improve the efficiency of the prediction further. We find that the Space Weather Modeling Framework, the best performing among the five models compared by Pulkkinen et al. (2013), shows significantly better skill scores in predicting the magnetic perturbation than predicting its time derivative, especially for large deviations. We also find that there is a strong correlation between the magnitude of dB/dt and the magnitude of the horizontal magnetic perturbation itself. Combining these two results one can devise an algorithm that gives better skill scores for predicting dB/dt exceeding various thresholds in 20 min time intervals than the direct approach.

  16. 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.

  17. 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.

  18. The effect of word predictability on reading time is logarithmic.

    PubMed

    Smith, Nathaniel J; Levy, Roger

    2013-09-01

    It is well known that real-time human language processing is highly incremental and context-driven, and that the strength of a comprehender's expectation for each word encountered is a key determinant of the difficulty of integrating that word into the preceding context. In reading, this differential difficulty is largely manifested in the amount of time taken to read each word. While numerous studies over the past thirty years have shown expectation-based effects on reading times driven by lexical, syntactic, semantic, pragmatic, and other information sources, there has been little progress in establishing the quantitative relationship between expectation (or prediction) and reading times. Here, by combining a state-of-the-art computational language model, two large behavioral data-sets, and non-parametric statistical techniques, we establish for the first time the quantitative form of this relationship, finding that it is logarithmic over six orders of magnitude in estimated predictability. This result is problematic for a number of established models of eye movement control in reading, but lends partial support to an optimal perceptual discrimination account of word recognition. We also present a novel model in which language processing is highly incremental well below the level of the individual word, and show that it predicts both the shape and time-course of this effect. At a more general level, this result provides challenges for both anticipatory processing and semantic integration accounts of lexical predictability effects. And finally, this result provides evidence that comprehenders are highly sensitive to relative differences in predictability - even for differences between highly unpredictable words - and thus helps bring theoretical unity to our understanding of the role of prediction at multiple levels of linguistic structure in real-time language comprehension. PMID:23747651

  19. 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.

  20. Cortical activity patterns predict speech discrimination ability

    PubMed Central

    Engineer, Crystal T; Perez, Claudia A; Chen, YeTing H; Carraway, Ryan S; Reed, Amanda C; Shetake, Jai A; Jakkamsetti, Vikram; Chang, Kevin Q; Kilgard, Michael P

    2010-01-01

    Neural activity in the cerebral cortex can explain many aspects of sensory perception. Extensive psychophysical and neurophysiological studies of visual motion and vibrotactile processing show that the firing rate of cortical neurons averaged across 50–500 ms is well correlated with discrimination ability. In this study, we tested the hypothesis that primary auditory cortex (A1) neurons use temporal precision on the order of 1–10 ms to represent speech sounds shifted into the rat hearing range. Neural discrimination was highly correlated with behavioral performance on 11 consonant-discrimination tasks when spike timing was preserved and was not correlated when spike timing was eliminated. This result suggests that spike timing contributes to the auditory cortex representation of consonant sounds. PMID:18425123

  1. Predicting mining activity with parallel genetic algorithms

    USGS Publications Warehouse

    Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.

    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.

  2. 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

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

    PubMed

    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

  4. 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).

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

    PubMed

    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). PMID:27300890

  6. 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…

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

    PubMed

    Huang, Yu-Li; Hanauer, David A

    2016-05-01

    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. PMID:27142954

  8. Echoed time series predictions, neural networks and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Conway, A.

    This work aims to illustrate a potentially serious and previously unrecognised problem in using Neural Networks (NNs), and possibly other techniques, to predict Time Series (TS). It also demonstrates how a new training scheme using a genetic algorithm can alleviate this problem. Although it is already established that NNs can predict TS such as Sunspot Number (SSN) with reasonable success, the accuracy of these predictions is often judged solely by an RMS or related error. The use of this type of error overlooks the presence of what we have termed echoing, where the NN outputs its most recent input as its prediction. Therefore, a method of detecting echoed predictions is introduced, called time-shifting. Reasons for the presence of echo are discussed and then related to the choice of TS sampling. Finally, a new specially designed training scheme is described, which is a hybrid of a genetic algorithm search and back propagation. With this method we have successfully trained NNs to predict without any echo.

  9. 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.

  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. 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.

  12. Broadband trailing edge noise predictions in the time domain

    NASA Astrophysics Data System (ADS)

    Casper, J.; Farassat, F.

    2004-03-01

    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 Williams-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. In the present work, Formulation 1B is used to calculate the farfield noise radiated from the trailing edge of a NACA 0012 airfoil in a low Mach number flow, 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.

  13. A Comparison of CTAS and Airline Time of Arrival Predictions

    NASA Technical Reports Server (NTRS)

    Heere, Karen R.; Zelenka, Richard E.; Hsu, Rose Y.

    1999-01-01

    A statistically-based comparison of aircraft times of arrival between Center/TRACON Automation System (CTAS) air traffic control scheduling and airline predictions is presented. CTAS is found to provide much improved values, forming the foundation for airline operational improvements, as observed during an airline field trial of a CTAS display.

  14. 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. PMID:24365536

  15. 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.

  16. Predicting activity approach based on new atoms similarity kernel function.

    PubMed

    Abu El-Atta, Ahmed H; Moussa, M I; Hassanien, Aboul Ella

    2015-07-01

    Drug design is a high cost and long term process. To reduce time and costs for drugs discoveries, new techniques are needed. Chemoinformatics field implements the informational techniques and computer science like machine learning and graph theory to discover the chemical compounds properties, such as toxicity or biological activity. This is done through analyzing their molecular structure (molecular graph). To overcome this problem there is an increasing need for algorithms to analyze and classify graph data to predict the activity of molecules. Kernels methods provide a powerful framework which combines machine learning with graph theory techniques. These kernels methods have led to impressive performance results in many several chemoinformatics problems like biological activity prediction. This paper presents a new approach based on kernel functions to solve activity prediction problem for chemical compounds. First we encode all atoms depending on their neighbors then we use these codes to find a relationship between those atoms each other. Then we use relation between different atoms to find similarity between chemical compounds. The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods. PMID:26117822

  17. Australian Tropical Cyclone Activity: Interannual Prediction and Climate Change

    NASA Astrophysics Data System (ADS)

    Nicholls, N.

    2014-12-01

    It is 35 years since it was first demonstrated that interannual variations in seasonal Australian region tropical cyclone (TC) activity could be predicted using simple indices of the El Niño - Southern Oscillation (ENSO). That demonstration (Nicholls, 1979), which was surprising and unexpected at the time, relied on only 25 years of data (1950-1975), but its later confirmation eventually led to the introduction of operational seasonal tropical cyclone activity. It is worth examining how well the ENSO-TC relationship has performed, over the period since 1975. Changes in observational technology, and even how a tropical cyclone is defined, have affected the empirical relationships between ENSO and seasonal activity, and ways to overcome this in forecasting seasonal activity will be discussed. Such changes also complicate the investigation of long-term trends in cyclone activity. The early work linked cyclone activity to local sea surface temperature thereby leading to the expectation that global warming would result in an increase in cyclone activity. But studies in the 1990s (eg., Nicholls et al., 1998) suggested that such an increase in activity was not occurring, neither in the Australian region nor elsewhere. Trends in Australian tropical cyclone activity will be discussed, and the confounding influence of factors such as changes in observational technologies will be examined. Nicholls, N. 1979. A possible method for predicting seasonal tropical cyclone activity in the Australian region. Mon. Weath. Rev., 107, 1221-1224 Nicholls, N., Landsea, C., and Gill, J., 1998. Recent trends in Australian region tropical cyclone activity. Meteorology and Atmospheric Physics, 65, 197-205.

  18. 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

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

    PubMed

    Broersen, Robin; 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

  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. Vocal Activity, Time Pressure and Interpersonal Judgments.

    ERIC Educational Resources Information Center

    Daly, John A.; Lashbrook, William B.

    This study examined the effects of differential time pressures on small group members' rankings of one another based on vocal activity. Vocal activity was operationalized as observed frequency of interaction. Time pressure was manipulated by allowing either six minutes or no time limit on a group problem-solving task. Main effects were…

  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.

    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. PMID:26066231

  4. Predicting travel time and dispersion in rivers and streams

    USGS Publications Warehouse

    Jobson, H.E.

    1997-01-01

    The possibility of a contaminant being accidentally or intentionally spilled in a river is a constant concern to those using the water. Methods are developed to estimate: (1) the velocity of a contaminant in a river; (2) the rate of attenuation of the peak concentration of a conservative contaminant; and (3) the time required for a contaminant plume to pass a point. The methods are based on data collected by the U.S. Geological Survey in almost a hundred different rivers representing a wide range of sizes, slopes, and geomorphic types. Although the accuracy of the predictions can be greatly increased by performing time-of-travel studies, the emphasis of this paper is on providing methods for making estimates where few data are available. It is shown that the unit-peak concentration is well correlated with travel time and that the travel time of the leading edge averages 89% of the travel time of the peak concentration.

  5. 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

  6. 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. PMID:25170618

  7. 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

  8. 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.

  9. 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. PMID:25157950

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

    PubMed

    Diersch, Nadine; Jones, Alex L; Cross, Emily S

    2016-01-01

    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. PMID:26503586

  11. Time series prediction using a rational fraction neural networks

    SciTech Connect

    Lee, K.; Lee, Y.C.; Barnes, C.; Aldrich, C.H.; Kindel, J.

    1988-01-01

    An efficient neural network based on a rational fraction representation has been trained to perform time series prediction. The network is a generalization of the Volterra-Wiener network while still retaining the computational efficiency of the latter. Because of the second order convergent nature of the learning algorithm, the rational net is computationally far more efficient than multilayer networks. The rational fractional representation is, however, more restrictive than the multilayer networks.

  12. On time delay estimation from a sparse linear prediction perspective.

    PubMed

    He, Hongsen; Yang, Tao; Chen, Jingdong

    2015-02-01

    This paper proposes a sparse linear prediction based algorithm to estimate time difference of arrival. This algorithm unifies the cross correlation method without prewhitening and that with prewhitening via an ℓ2/ℓ1 optimization process, which is solved by an augmented Lagrangian alternating direction method. It also forms a set of time delay estimators that make a tradeoff between prewhitening and non-prewhitening through adjusting a regularization parameter. The effectiveness of the proposed algorithm is demonstrated in noisy and reverberant environments. PMID:25698037

  13. Real-time linear predictive analysis of speech using multimicroprocessors

    SciTech Connect

    Seethardman, S.; Radhakrishnan, T.; Suen, C.Y.

    1982-01-01

    Many applications of linear predictive coding (often known as LPC) of speech signals require a system capable of performing the complete LPC analysis in real time. This paper describes a pipeline network consisting of several general purpose microprocessors, primarily suitable for complete LPC analysis of a 10-pole model with a sampling frequency of 10 khz and a frame rate of 100 hz in real time. The proposed system is different from the previous systems, which either employed special purpose hardware or produced an analysis at a lower frame rate. 27 references.

  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. 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.

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

    NASA Technical Reports Server (NTRS)

    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. 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

  18. Chaos time series prediction based on membrane optimization algorithms.

    PubMed

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

    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-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases.

    PubMed

    Peterson, A Townsend; Martínez-Campos, Carmen; Nakazawa, Yoshinori; Martínez-Meyer, Enrique

    2005-09-01

    Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk. PMID:15979656

  20. Real-time prediction of seismic ground motion (I) : real-time estimation of seismic wavefield using data assimilation technique and time evolutional prediction using Kirchhoff integral

    NASA Astrophysics Data System (ADS)

    Hoshiba, M.

    2013-05-01

    In this presentation, I propose a new approach for real-time prediction of seismic ground motion which is applicable to Earthquake Early Waning (EEW), in which hypocentral location and magnitude are not required. . Many methods of EEW are based on a network method in which hypocenter and magnitude (source parameters) are quickly determined, and then the ground motions are predicted, and warnings are issued depending on the strength of the predicted ground motion. In this method, it is necessary to determine the hypocenter and magnitude at first, and error of the source parameters leads directly to the error of the prediction. It is not easy to take the effects of rupture directivity and source extent into account, and it is impossible to fully reproduce the current wavefield from the interpreted source parameters. Time evolutional prediction is a method in which future wavefield is iteratively predicted from the wavefield at the certain time, that is u(x, t+Δt)=P(u(x, t)), where u is the wave motion at location x at lapse time t, and P is the prediction operator. Future wave motion, u(x, t+Δt), is predicted from the distribution of the current wave motion u(x, t) using P. For P, finite difference technique or boundary integral equation method, such as Kirchhoff integral, is used. Kirchhoff integral is qualitatively approximated by Huygens principle. The real time monitoring of wavefield are important for this method, but it is possible to predict ground motion without a hypocentral location and magnitude. In the time evolutional prediction, determination of detailed distribution of current wavefield is an important key, so that dense seismic observation network is required. Data assimilation is a technique to produce artificially denser network, which is widely used for numerical weather forecast and oceanography. Distribution of current wave motion is estimated from not only the current real observation of u(xi, t) where xi is the location of the i-th site, but

  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. Predicting aquifer response time for application in catchment modeling.

    PubMed

    Walker, Glen R; Gilfedder, Mat; Dawes, Warrick R; Rassam, David W

    2015-01-01

    It is well established that changes in catchment land use can lead to significant impacts on water resources. Where land-use changes increase evapotranspiration there is a resultant decrease in groundwater recharge, which in turn decreases groundwater discharge to streams. The response time of changes in groundwater discharge to a change in recharge is a key aspect of predicting impacts of land-use change on catchment water yield. Predicting these impacts across the large catchments relevant to water resource planning can require the estimation of groundwater response times from hundreds of aquifers. At this scale, detailed site-specific measured data are often absent, and available spatial data are limited. While numerical models can be applied, there is little advantage if there are no detailed data to parameterize them. Simple analytical methods are useful in this situation, as they allow the variability in groundwater response to be incorporated into catchment hydrological models, with minimal modeling overhead. This paper describes an analytical model which has been developed to capture some of the features of real, sloping aquifer systems. The derived groundwater response timescale can be used to parameterize a groundwater discharge function, allowing groundwater response to be predicted in relation to different broad catchment characteristics at a level of complexity which matches the available data. The results from the analytical model are compared to published field data and numerical model results, and provide an approach with broad application to inform water resource planning in other large, data-scarce catchments. PMID:24842053

  3. Predicting chaotic time series with a partial model

    NASA Astrophysics Data System (ADS)

    Hamilton, Franz; Berry, Tyrus; Sauer, Timothy

    2015-07-01

    Methods for forecasting time series are a critical aspect of the understanding and control of complex networks. When the model of the network is unknown, nonparametric methods for prediction have been developed, based on concepts of attractor reconstruction pioneered by Takens and others. In this Rapid Communication we consider how to make use of a subset of the system equations, if they are known, to improve the predictive capability of forecasting methods. A counterintuitive implication of the results is that knowledge of the evolution equation of even one variable, if known, can improve forecasting of all variables. The method is illustrated on data from the Lorenz attractor and from a small network with chaotic dynamics.

  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. 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.

  6. Real-time prediction of earthquake ground motion: time evolutional prediction using data assimilation and real-time correction of site amplification factors

    NASA Astrophysics Data System (ADS)

    Hoshiba, M.

    2012-12-01

    In this presentation, I propose a new approach for real-time prediction of seismic ground motion which is applicable to Earthquake Early Waning (EEW). Many methods of EEW are based on a network method in which hypocenter and magnitude (source parameters) are quickly determined (that is, interpretation of current wavefield), and then the ground motions are predicted, and warnings are issued depending on the strength of the predicted ground motion. In this method, though we can predict ground motions using a few parameters (location of hypocenter, magnitude, site factors) at any points, it is necessary to determine the hypocenter and magnitude at first, and error of the source parameters leads directly to the error of the prediction. It is not easy to take the effects of rupture directivity and source extent into account, and it is impossible to fully reproduce the current wavefield from the interpreted source parameters. In general, wave motion is predictable when boundary condition and initial condition are given. Time evolutional prediction is a method based on this approach using the current wavefield as an initial condition, that is u(x, t+Δt)=H(u(x, t)), where u is the wave motion at location x at lapse time t, and H is the prediction operator. Future wave motion, u(x, t+Δt), is predicted from the distribution of the current wave motion u(x, t) using H. For H, finite difference technique or boundary integral equation method, such as Kirchhoff integral, is used. In the time evolutional prediction, determination of detailed distribution of current wave motion is a key, so that dense seismic observation network is required. Data assimilation is a technique to produce artificially denser network, which is widely used for numerical weather prediction and oceanography. Distribution of current wave motion is estimated from not only the current real observation of u(x, t), but also the prediction of one step before, H(u(x, t-Δt)). Combination of them produces denser

  7. 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.

  8. Cortical activity patterns predict robust speech discrimination ability in noise

    PubMed Central

    Shetake, Jai A.; Wolf, Jordan T.; Cheung, Ryan J.; Engineer, Crystal T.; Ram, Satyananda K.; Kilgard, Michael P.

    2012-01-01

    The neural mechanisms that support speech discrimination in noisy conditions are poorly understood. In quiet conditions, spike timing information appears to be used in the discrimination of speech sounds. In this study, we evaluated the hypothesis that spike timing is also used to distinguish between speech sounds in noisy conditions that significantly degrade neural responses to speech sounds. We tested speech sound discrimination in rats and recorded primary auditory cortex (A1) responses to speech sounds in background noise of different intensities and spectral compositions. Our behavioral results indicate that rats, like humans, are able to accurately discriminate consonant sounds even in the presence of background noise that is as loud as the speech signal. Our neural recordings confirm that speech sounds evoke degraded but detectable responses in noise. Finally, we developed a novel neural classifier that mimics behavioral discrimination. The classifier discriminates between speech sounds by comparing the A1 spatiotemporal activity patterns evoked on single trials with the average spatiotemporal patterns evoked by known sounds. Unlike classifiers in most previous studies, this classifier is not provided with the stimulus onset time. Neural activity analyzed with the use of relative spike timing was well correlated with behavioral speech discrimination in quiet and in noise. Spike timing information integrated over longer intervals was required to accurately predict rat behavioral speech discrimination in noisy conditions. The similarity of neural and behavioral discrimination of speech in noise suggests that humans and rats may employ similar brain mechanisms to solve this problem. PMID:22098331

  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. PMID:24815284

  10. 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...

  11. 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.

  12. Real-Time Prediction of Neurally Mediated Syncope.

    PubMed

    Couceiro, R; Carvalho, P; Paiva, R P; Muehlsteff, J; Henriques, J; Eickholt, C; Brinkmeyer, C; Kelm, M; Meyer, C

    2016-03-01

    Neurally mediated syncope (NMS) patients suffer from sudden loss of consciousness, which is associated with a high rate of falls and hospitalization. NMS negatively impacts a subject's quality of life and is a growing cost issue in our aging society, as its incidence increases with age. In this paper, we present a solution for prediction of NMS, which is based on the analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG) alone. Several parameters extracted from ECG and PPG, associated with reflectory mechanisms underlying NMS in previous publications, were combined in a single algorithm to detect impending syncope. The proposed algorithm was evaluated in a population of 43 subjects. The feature selection, distance metric selection, and optimal threshold were performed in a subset of 30 patients, while the remaining data from 13 patients were used to test the final solution. Additionally, a leave-one-out cross-validation scheme was also used to evaluate the performance of the proposed algorithm yielding the following results: sensitivity (SE)--95.2%; specificity (SP)--95.4%; positive predictive value (PPV)--90.9%; false-positive rate per hour (FPRh)-0.14 h(-1), and prediction time (aPTime)--116.4 s. PMID:25769176

  13. 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

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

    PubMed

    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 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

  17. 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.

  18. Dynamical recurrent neural networks--towards environmental time series prediction.

    PubMed

    Aussem, A; Murtagh, F; Sarazin, M

    1995-06-01

    Dynamical Recurrent Neural Networks (DRNN) (Aussem 1995a) are a class of fully recurrent networks obtained by modeling synapses as autoregressive filters. By virtue of their internal dynamic, these networks approximate the underlying law governing the time series by a system of nonlinear difference equations of internal variables. They therefore provide history-sensitive forecasts without having to be explicitly fed with external memory. The model is trained by a local and recursive error propagation algorithm called temporal-recurrent-backpropagation. The efficiency of the procedure benefits from the exponential decay of the gradient terms backpropagated through the adjoint network. We assess the predictive ability of the DRNN model with meterological and astronomical time series recorded around the candidate observation sites for the future VLT telescope. The hope is that reliable environmental forecasts provided with the model will allow the modern telescopes to be preset, a few hours in advance, in the most suited instrumental mode. In this perspective, the model is first appraised on precipitation measurements with traditional nonlinear AR and ARMA techniques using feedforward networks. Then we tackle a complex problem, namely the prediction of astronomical seeing, known to be a very erratic time series. A fuzzy coding approach is used to reduce the complexity of the underlying laws governing the seeing. Then, a fuzzy correspondence analysis is carried out to explore the internal relationships in the data. Based on a carefully selected set of meteorological variables at the same time-point, a nonlinear multiple regression, termed nowcasting (Murtagh et al. 1993, 1995), is carried out on the fuzzily coded seeing records. The DRNN is shown to outperform the fuzzy k-nearest neighbors method. PMID:7496587

  19. 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.

  20. 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.

  1. Toward the Real-Time Tsunami Parameters Prediction

    NASA Astrophysics Data System (ADS)

    Lavrentyev, Mikhail; Romanenko, Alexey; Marchuk, Andrey

    2013-04-01

    Today, a wide well-developed system of deep ocean tsunami detectors operates over the Pacific. Direct measurements of tsunami-wave time series are available. However, tsunami-warning systems fail to predict basic parameters of tsunami waves on time. Dozens examples could be provided. In our view, the lack of computational power is the main reason of these failures. At the same time, modern computer technologies such as, GPU (graphic processing unit) and FPGA (field programmable gates array), can dramatically improve data processing performance, which may enhance timely tsunami-warning prediction. Thus, it is possible to address the challenge of real-time tsunami forecasting for selected geo regions. We propose to use three new techniques in the existing tsunami warning systems to achieve real-time calculation of tsunami wave parameters. First of all, measurement system (DART buoys location, e.g.) should be optimized (both in terms of wave arriving time and amplitude parameter). The corresponding software application exists today and is ready for use [1]. We consider the example of the coastal line of Japan. Numerical tests show that optimal installation of only 4 DART buoys (accounting the existing sea bed cable) will reduce the tsunami wave detection time to only 10 min after an underwater earthquake. Secondly, as was shown by this paper authors, the use of GPU/FPGA technologies accelerates the execution of the MOST (method of splitting tsunami) code by 100 times [2]. Therefore, tsunami wave propagation over the ocean area 2000*2000 km (wave propagation simulation: time step 10 sec, recording each 4th spatial point and 4th time step) could be calculated at: 3 sec with 4' mesh 50 sec with 1' mesh 5 min with 0.5' mesh The algorithm to switch from coarse mesh to the fine grain one is also available. Finally, we propose the new algorithm for tsunami source parameters determination by real-time processing the time series, obtained at DART. It is possible to approximate

  2. Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times.

    PubMed

    Mollica, Luca; Theret, Isabelle; Antoine, Mathias; Perron-Sierra, Françoise; Charton, Yves; Fourquez, Jean-Marie; Wierzbicki, Michel; Boutin, Jean A; Ferry, Gilles; Decherchi, Sergio; Bottegoni, Giovanni; Ducrot, Pierre; Cavalli, Andrea

    2016-08-11

    Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times. PMID:27391254

  3. 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.

  4. 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

  5. Real-Time WINDMI Predictions of Geomagnetic Storm and Substorms

    NASA Astrophysics Data System (ADS)

    Mays, M. L.; Horton, W.; Spencer, E.; Kozyra, J. U.

    2008-12-01

    Real-Time WINMDI is plasma physics-based, nonlinear dynamical model of the coupled solar WIND Magentosphere-Ionosphere system. Using upstream solar wind particle and field data, a system of nonlinear ordinary differential equations is solved numerically to describe the energy transfer from the solar wind to the magnetosphere-ionosphere system. The physics model WINMDI divides the incoming power into energy stored in multiple regions of M-I system and has been verified on GEM storm data in Spencer et al. (2007). The system of nonlinear ordinary differential equations, which describes energy transfer into, and between dominant components of the nightside magnetosphere and ionosphere, is solved numerically to determine the state of each component. The low-dimensional model characterizes the energy stored in the ring current and the region 1 field-aligned current which are use to compute model Dst and AL values. Real-time solar wind plasma parameters, available from ACE, are downloaded every 10 minutes to compute the input solar wind driving voltage for the model. Real-Time WINDMI computes model Dst and AL values about 1-2 hours before index data is available at the Kyoto WDC Quicklook website. Results are shown on the Real-Time WINDMI website. We present statistics for Real-Time WINDMI performance from 2006 to present and also compare the results for different input driving voltages. We plan to compare the database of Real-Time WINDMI Dst predictions with other ring current models which contain different loss and energization processes. The work is supported by NSF grant ATM-0638480.

  6. 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.…

  7. 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

  8. Real-Time Monitoring of Active Landslides

    USGS Publications Warehouse

    Reid, Mark E.; LaHusen, Richard G.; Ellis, William L.

    1999-01-01

    Landslides threaten lives and property in every State in the Nation. To reduce the risk from active landslides, the U.S. Geological Survey (USGS) develops and uses real-time landslide monitoring systems. Monitoring can detect early indications of rapid, catastrophic movement. Up-to-the-minute or real-time monitoring provides immediate notification of landslide activity, potentially saving lives and property. Continuous information from real-time monitoring also provides a better understanding of landslide behavior, enabling engineers to create more effective designs for halting landslide movement.

  9. Automatic measurement of voice onset time using discriminative structured prediction.

    PubMed

    Sonderegger, Morgan; Keshet, Joseph

    2012-12-01

    A discriminative large-margin algorithm for automatic measurement of voice onset time (VOT) is described, considered as a case of predicting structured output from speech. Manually labeled data are used to train a function that takes as input a speech segment of an arbitrary length containing a voiceless stop, and outputs its VOT. The function is explicitly trained to minimize the difference between predicted and manually measured VOT; it operates on a set of acoustic feature functions designed based on spectral and temporal cues used by human VOT annotators. The algorithm is applied to initial voiceless stops from four corpora, representing different types of speech. Using several evaluation methods, the algorithm's performance is near human intertranscriber reliability, and compares favorably with previous work. Furthermore, the algorithm's performance is minimally affected by training and testing on different corpora, and remains essentially constant as the amount of training data is reduced to 50-250 manually labeled examples, demonstrating the method's practical applicability to new datasets. PMID:23231126

  10. 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

    2016-01-01

    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

  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. 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

  13. 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.

  14. 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

  15. 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

  16. 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

  17. A prediction of geomagnetic activity for solar cycle 23

    NASA Astrophysics Data System (ADS)

    Cliver, E. W.; Ling, A. G.; Wise, J. E.; Lanzerotti, L. J.

    1999-04-01

    Using a database of 13 solar cycles of geomagnetic aa data, we obtained correlations between cycle averages of geomagnetic activity (and sunspot number) and the numbers of days with disturbance levels above certain aa thresholds. We then used a precursor-type relation to predict an average aa index of 23.1 nT for cycle 23 and inserted this average aa value into the above correlations to forecast the integral size distribution of geomagnetic activity for the new cycle. The predicted size distribution is similar to that observed for cycles 21 and 22 but most closely resembles that of solar cycle 18 (1944-1954), which was slightly smaller than cycles 21 and 22. Our prediction agrees reasonably well with the ``climatology-based'' forecast made by the intergovernmental panel tasked to predict geomagnetic activity for the coming solar cycle and is significantly different from their ``precursor-based'' prediction.

  18. 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.

  19. 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...

  20. PREDICTING PREFERENTIAL ADSORPTION OF ORGANICS BY ACTIVATED CARBON

    EPA Science Inventory

    Preferential adsorption of organic compounds onto activated carbon from dilute aqueous solutions was studied to develop a comprehensive theoretical basis for predicting adsorption of multicomponent solutes. The research program investigates why some solutes are strong adsorbers, ...

  1. 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…

  2. Prediction Activities at NASA's Global Modeling and Assimilation Office

    NASA Technical Reports Server (NTRS)

    Schubert, Siegfried

    2010-01-01

    The Global Modeling and Assimilation Office (GMAO) is a core NASA resource for the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems that are used for addressing the weather and climate research questions identified in NASA's science mission. In fact, the GMAO is actively engaged in addressing one of NASA's science mission s key questions concerning how well transient climate variations can be understood and predicted. At weather time scales the GMAO is developing ultra-high resolution global climate models capable of resolving high impact weather systems such as hurricanes. The ability to resolve the detailed characteristics of weather systems within a global framework greatly facilitates addressing fundamental questions concerning the link between weather and climate variability. At sub-seasonal time scales, the GMAO is engaged in research and development to improve the use of land information (especially soil moisture), and in the improved representation and initialization of various sub-seasonal atmospheric variability (such as the MJO) that evolves on time scales longer than weather and involves exchanges with both the land and ocean The GMAO has a long history of development for advancing the seasonal-to-interannual (S-I) prediction problem using an older version of the coupled atmosphere-ocean general circulation model (AOGCM). This includes the development of an Ensemble Kalman Filter (EnKF) to facilitate the multivariate assimilation of ocean surface altimetry, and an EnKF developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The importance of decadal variability, especially that associated with long-term droughts is well recognized by the

  3. 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

  4. Recent and Past Musical Activity Predicts Cognitive Aging Variability: Direct Comparison with General Lifestyle Activities

    PubMed Central

    Hanna-Pladdy, Brenda; Gajewski, Byron

    2012-01-01

    Studies evaluating the impact of modifiable lifestyle factors on cognition offer potential insights into sources of cognitive aging variability. Recently, we reported an association between extent of musical instrumental practice throughout the life span (greater than 10 years) on preserved cognitive functioning in advanced age. These findings raise the question of whether there are training-induced brain changes in musicians that can transfer to non-musical cognitive abilities to allow for compensation of age-related cognitive declines. However, because of the relationship between engagement in general lifestyle activities and preserved cognition, it remains unclear whether these findings are specifically driven by musical training or the types of individuals likely to engage in greater activities in general. The current study controlled for general activity level in evaluating cognition between musicians and nomusicians. Also, the timing of engagement (age of acquisition, past versus recent) was assessed in predictive models of successful cognitive aging. Seventy age and education matched older musicians (>10 years) and non-musicians (ages 59–80) were evaluated on neuropsychological tests and general lifestyle activities. Musicians scored higher on tests of phonemic fluency, verbal working memory, verbal immediate recall, visuospatial judgment, and motor dexterity, but did not differ in other general leisure activities. Partition analyses were conducted on significant cognitive measures to determine aspects of musical training predictive of enhanced cognition. The first partition analysis revealed education best predicted visuospatial functions in musicians, followed by recent musical engagement which offset low education. In the second partition analysis, early age of musical acquisition (<9 years) predicted enhanced verbal working memory in musicians, while analyses for other measures were not predictive. Recent and past musical activity, but not

  5. Multiplexed model predictive control for active vehicle suspensions

    NASA Astrophysics Data System (ADS)

    Hu, Yinlong; Chen, Michael Z. Q.; Hou, Zhongsheng

    2015-02-01

    Multiplexed model predictive control (MMPC) is a recently proposed efficient model predictive control (MPC) algorithm, which can effectively reduce the computational burden of the online optimisation in MPC implementation by updating the control inputs in an asynchronous manner. This paper investigates the application of MMPC in active vehicle suspension design. An MMPC controller integrated with soft constraints and a Kalman filter is proposed based on a full-car model. Ride comfort, roadholding and suspension deflection are considered in this paper, where ride comfort and roadholding are formulated as a quadratic cost function in terms of sprung mass accelerations and tyre deflections, while suspension deflection performance is formulated as a hard constraint. The saturation of the actuator force is also considered and formulated as a hard constraint as well. Numerical simulation is performed with respect to different choices of weighting factors, vehicle speeds and control horizons. The results show that the overall performance of ride comfort and roadholding can be improved significantly by employing MMPC and the average time taken by MMPC to solve the individual quadratic programming problem is considerably smaller than that of the conventional MPC, which effectively demonstrate the effectiveness of the proposed method.

  6. Patterns of Activity Revealed by a Time Lag Analysis of a Model Active Region

    NASA Astrophysics Data System (ADS)

    Bradshaw, Stephen; Viall, Nicholeen

    2016-05-01

    We investigate the global activity patterns predicted from a model active region heated by distributions of nanoflares that have a range of average frequencies. The activity patterns are manifested in time lag maps of narrow-band instrument channel pairs. We combine an extrapolated magnetic skeleton with hydrodynamic and forward modeling codes to create a model active region, and apply the time lag method to synthetic observations. Our aim is to recover some typical properties and patterns of activity observed in active regions. Our key findings are: 1. Cooling dominates the time lag signature and the time lags between the channel pairs are generally consistent with observed values. 2. Shorter coronal loops in the core cool more quickly than longer loops at the periphery. 3. All channel pairs show zero time lag when the line-of-sight passes through coronal loop foot-points. 4. There is strong evidence that plasma must be re-energized on a time scale comparable to the cooling timescale to reproduce the observed coronal activity, but it is likely that a relatively broad spectrum of heating frequencies operates across active regions. 5. Due to their highly dynamic nature, we find nanoflare trains produce zero time lags along entire flux tubes in our model active region that are seen between the same channel pairs in observed active regions.

  7. Seasonal variation in leisure time physical activity.

    PubMed

    Uitenbroek, D G

    1993-06-01

    In this paper seasonal variation in leisure time physical activity for exercise is studied and quantified with regard to several popular exercise activities and taking the respondents gender, occupational status, and age into consideration. The analysis concerns data collected by telephone in Scotland between January 1989 and March 1992. Data from 7,202 male and 9,284 female respondents is used in the analysis; cosinor analysis using GLIM is applied. Considerable seasonal variation was found affecting both outdoor and indoor activities. During the peak phase in July, 32% of the respondents reported exercising for at least 20 min three or more times during the previous week, in the winter period this decreased to 23%. Older respondents were found to exercise more later in the year and also showed seasonal variation to a larger extent than younger respondents. This is particularly so for those respondents who exercise at a relatively high frequency. PMID:8321115

  8. 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.

  9. Heat transfer model for predicting squib ignition times

    NASA Technical Reports Server (NTRS)

    Sernas, V.

    1974-01-01

    A squib ignition model based on transient heat condition from the hot bridgewire to the pyrotechnic is described. No Arrhenius-type chemical reaction is included. Instead, a thermal contact resistance is postulated to exist between the hot bridgewire and the pyrotechnic. Ignition is assumed to occur when a 2.5 micron layer of pyrotechnic next to the bridgewire reaches a characteristic ignition temperature for that pyrotechnic. This model was applied to the JPL squib, which uses a 50 micron (0.002-in.) diameter Tophet A bridgewire to ignite a boron, potassium perchlorate mix. A computer program was utilized that solves the transient heat condition problem with the boundary conditions stipulated by the model. The thermal contact conductance at the interface was determined by trial and error so that the experimentally determined ignition time for one firing condition would be properly predicted by the model. The agreement was quite good for tests run between -129 C and +93.3 C at current levels of 3.5 and 5 A. Axial heat conduction along the bridgewire is shown to be negligible.

  10. 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.

  11. Predicting Physical Activity in Arab American School Children

    ERIC Educational Resources Information Center

    Martin, Jeffrey J.; McCaughtry, Nate; Shen, Bo

    2008-01-01

    Theoretically grounded research on the determinants of Arab American children's physical activity is virtually nonexistent. Thus, the purpose of our investigation was to evaluate the ability of the theory of planned behavior (TPB) and social cognitive theory (SCT) to predict Arab American children's moderate-to-vigorous physical activity (MVPA).…

  12. 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

  13. Global cortical activity predicts shape of hand during grasping.

    PubMed

    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

  14. 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.

  15. Constructing realistic engrams: poststimulus activity of hippocampus and dorsal striatum predicts subsequent episodic memory.

    PubMed

    Ben-Yakov, Aya; Dudai, Yadin

    2011-06-15

    Encoding of real-life episodic memory commonly involves integration of information as the episode unfolds. Offline processing immediately following event offset is expected to play a role in encoding the episode into memory. In this study, we examined whether distinct human brain activity time-locked to the offset of short narrative audiovisual episodes could predict subsequent memory for the gist of the episodes. We found that a set of brain regions, most prominently the bilateral hippocampus and the bilateral caudate nucleus, exhibit memory-predictive activity time-locked to the stimulus offset. We propose that offline activity in these regions reflects registration to memory of integrated episodes. PMID:21677186

  16. Space Weather Prediction Error Bounding for Real-Time Ionospheric Threat Adaptation of GNSS Augmentation Systems

    NASA Astrophysics Data System (ADS)

    Lee, J.; Yoon, M.; Lee, J.

    2014-12-01

    Current Global Navigation Satellite Systems (GNSS) augmentation systems attempt to consider all possible ionospheric events in their correction computations of worst-case errors. This conservatism can be mitigated by subdividing anomalous conditions and using different values of ionospheric threat-model bounds for each class. A new concept of 'real-time ionospheric threat adaptation' that adjusts the threat model in real time instead of always using the same 'worst-case' model was introduced in my previous research. The concept utilizes predicted values of space weather indices for determining the corresponding threat model based on the pre-defined worst-case threat as a function of space weather indices. Since space weather prediction is not reliable due to prediction errors, prediction errors are needed to be bounded to the required level of integrity of the system being supported. The previous research performed prediction error bounding using disturbance, storm time (Dst) index. The distribution of Dst prediction error over the 15-year data was bounded by applying 'inflated-probability density function (pdf) Gaussian bounding'. Since the error distribution has thick and non-Gaussian tails, investigation on statistical distributions which properly describe heavy tails with less conservatism is required for the system performance. This paper suggests two potential approaches for improving space weather prediction error bounding. First, we suggest using different statistical models when fit the error distribution, such as the Laplacian distribution which has fat tails, and the folded Gaussian cumulative distribution function (cdf) distribution. Second approach is to bound the error distribution by segregating data based on the overall level of solar activity. Bounding errors using only solar minimum period data will have less uncertainty and it may allow the use of 'solar cycle prediction' provided by NASA when implementing to real-time threat adaptation. Lastly

  17. Brain monoamine oxidase A activity predicts trait aggression.

    PubMed

    Alia-Klein, Nelly; Goldstein, Rita Z; Kriplani, Aarti; Logan, Jean; Tomasi, Dardo; Williams, Benjamin; Telang, Frank; Shumay, Elena; Biegon, Anat; Craig, Ian W; Henn, Fritz; Wang, Gene-Jack; Volkow, Nora D; Fowler, Joanna S

    2008-05-01

    The genetic deletion of monoamine oxidase A (MAO A), an enzyme that breaks down the monoamine neurotransmitters norepinephrine, serotonin, and dopamine, produces aggressive phenotypes across species. Therefore, a common polymorphism in the MAO A gene (MAOA, Mendelian Inheritance in Men database number 309850, referred to as high or low based on transcription in non-neuronal cells) has been investigated in a number of externalizing behavioral and clinical phenotypes. These studies provide evidence linking the low MAOA genotype and violent behavior but only through interaction with severe environmental stressors during childhood. Here, we hypothesized that in healthy adult males the gene product of MAO A in the brain, rather than the gene per se, would be associated with regulating the concentration of brain amines involved in trait aggression. Brain MAO A activity was measured in vivo in healthy nonsmoking men with positron emission tomography using a radioligand specific for MAO A (clorgyline labeled with carbon 11). Trait aggression was measured with the multidimensional personality questionnaire (MPQ). Here we report for the first time that brain MAO A correlates inversely with the MPQ trait measure of aggression (but not with other personality traits) such that the lower the MAO A activity in cortical and subcortical brain regions, the higher the self-reported aggression (in both MAOA genotype groups) contributing to more than one-third of the variability. Because trait aggression is a measure used to predict antisocial behavior, these results underscore the relevance of MAO A as a neurochemical substrate of aberrant aggression. PMID:18463263

  18. Brain Monoamine Oxidase-A Activity Predicts Trait Aggression

    PubMed Central

    Alia-Klein, Nelly; Goldstein, Rita Z.; Kriplani, Aarti; Logan, Jean; Tomasi, Dardo; Williams, Benjamin; Telang, Frank; Shumay, Elena; Biegon, Anat; Craig, Ian W.; Henn, Fritz; Wang, Gene-Jack; Volkow, Nora D.; Fowler, Joanna S.

    2008-01-01

    The genetic deletion of monoamine oxidase A (MAO A, an enzyme which breaks down the monoamine neurotransmitters norepinephrine, serotonin and dopamine) produces aggressive phenotypes across species. Therefore, a common polymorphism in the MAO A gene (MAOA, MIM 309850, referred to as high or low based on transcription in non-neuronal cells) has been investigated in a number of externalizing behavioral and clinical phenotypes. These studies provide evidence linking the low MAOA genotype and violent behavior but only through interaction with severe environmental stressors during childhood. Here, we hypothesized that in healthy adult males the gene product of MAO A in the brain, rather than the gene per se, would be associated with regulating the concentration of brain amines involved in trait aggression. Brain MAO A activity was measured in-vivo in healthy non-smoking men with positron emission tomography using a radioligand specific for MAO A (clorgyline labeled with carbon 11). Trait aggression was measured with the Multidimensional Personality Questionnaire (MPQ). Here we report for the first time that brain MAO A correlates inversely with the MPQ trait measure of aggression (but not with other personality traits) such that the lower the MAO A activity in cortical and subcortical brain regions the higher the self-reported aggression (in both MAOA genotype groups) contributing to more than a third of the variability. Since trait aggression is a measure used to predict antisocial behavior, these results underscore the relevance of MAO A as a neurochemical substrate of aberrant aggression. PMID:18463263

  19. 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.

  20. 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

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

    PubMed

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

    2014-05-01

    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

  2. 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. PMID:26881271

  3. 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

  4. Prediction of Energy Expenditure and Physical Activity in Preschoolers

    PubMed Central

    Butte, Nancy F.; Wong, William W.; Lee, Jong Soo; Adolph, Anne L.; Puyau, Maurice R.; Zakeri, Issa F.

    2013-01-01

    Purpose 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) for prediction of EE using room calorimetry and doubly labeled water (DLW), and established accelerometry cut-points for PA levels. Methods Fifty preschoolers, mean±SD age 4.5±0.8 y, participated in room calorimetry for minute-by-minute measurements of EE, accelerometer counts (AC) (Actiheart and ActiGraph GT3X+) and HR (Actiheart). Free-living, 105 children, aged 4.6±0.9 years, completed the 7-d DLW procedure while wearing the devices. AC cut-points for PA levels were established using smoothing splines and receiver operating characteristic curves. Results Based on calorimetry, mean percent errors for EE were -2.9±10.8% and -1.1±7.4% for CSTS models, and -1.9±9.6 and 1.3±8.1% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. Based on DLW, mean percent errors were -0.5±9.7% and 4.1±8.5% for CSTS models and 3.2±10.1% and 7.5±10.0% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. Applying activity EE thresholds, final accelerometer cut-points were determined: 41, 449, and 1,297 cpm for Actiheart x-axis; 820, 3,908, and 6,112 cpm for ActiGraph vector magnitude; and 240, 2,120, and 4,450 cpm for ActiGraph x-axis for sedentary/light, light/moderate, and moderate/vigorous PA (MVPA). Based on confusion matrices, correctly classified rates were 81–83% for sedentary PA, 58–64% for light PA and 62–73% for MVPA. Conclusion The lack of bias and acceptable limits of agreement affirm the validity of the CSTS and MARS models for the prediction of EE in preschool-aged children. Accelerometer cut-points are satisfactory for classification of sedentary, light and moderate-vigorous levels of PA in preschoolers

  5. Synthesis, characterization, theoretical prediction of activities and evaluation of biological activities of some sulfacetamide based hydroxytriazenes.

    PubMed

    Agarwal, Shilpa; Baroliya, Prabhat K; Bhargava, Amit; Tripathi, I P; Goswami, A K

    2016-06-15

    Six new N [(4-aminophenyl)sulfonyl]acetamide based hydroxytriazenes have been synthesized and characterized using elemental analysis, IR, 1H NMR, 13C NMR and MASS spectral analysis. Further, their theoretical predictions for probable activities have been taken using PASS (Prediction of Activity Spectra for Substance). Although a number of activities have been predicted but specifically anti-inflammatory, antiradical, anti-diabetic activities have been experimentally validated which proves that theoretical predictions agree with the experimental results. The object of the Letter is to establish Computer Aided Drug Design (CADD) using our compounds. PMID:27136718

  6. Prediction of fine-tuned promoter activity from DNA sequence

    PubMed Central

    Siwo, Geoffrey; Rider, Andrew; Tan, Asako; Pinapati, Richard; Emrich, Scott; Chawla, Nitesh; Ferdig, Michael

    2016-01-01

    The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring

  7. Structure based activity prediction of HIV-1 reverse transcriptase inhibitors.

    PubMed

    de Jonge, Marc R; Koymans, Lucien M H; Vinkers, H Maarten; Daeyaert, Frits F D; Heeres, Jan; Lewi, Paul J; Janssen, Paul A J

    2005-03-24

    We have developed a fast and robust computational method for prediction of antiviral activity in automated de novo design of HIV-1 reverse transcriptase inhibitors. This is a structure-based approach that uses a linear relation between activity and interaction energy with discrete orientation sampling and with localized interaction energy terms. The localization allows for the analysis of mutations of the protein target and for the separation of inhibition and a specific binding to the enzyme. We apply the method to the prediction of pIC(50) of HIV-1 reverse transcriptase inhibitors. The model predicts the activity of an arbitrary compound with a q(2) of 0.681 and an average absolute error of 0.66 log value, and it is fast enough to be used in high-throughput computational applications. PMID:15771460

  8. Subgenual cingulate cortical activity predicts the efficacy of electroconvulsive therapy.

    PubMed

    Argyelan, M; Lencz, T; Kaliora, S; Sarpal, D K; Weissman, N; Kingsley, P B; Malhotra, A K; Petrides, G

    2016-01-01

    Electroconvulsive therapy (ECT) is the most effective treatment for depression, yet its mechanism of action is unknown. Our goal was to investigate the neurobiological underpinnings of ECT response using longitudinally collected resting-state functional magnetic resonance imaging (rs-fMRI) in 16 patients with treatment-resistant depression and 10 healthy controls. Patients received bifrontal ECT 3 times a week under general anesthesia. We acquired rs-fMRI at three time points: at baseline, after the 1st ECT administration and after the course of the ECT treatment; depression was assessed with the Hamilton Depression Rating Scale (HAM-D). The primary measure derived from rs-fMRI was fractional amplitude of low frequency fluctuation (fALFF), which provides an unbiased voxel-wise estimation of brain activity. We also conducted seed-based functional connectivity analysis based on our primary findings. We compared treatment-related changes in HAM-D scores with pre- and post-treatment fALFF and connectivity measures. Subcallosal cingulate cortex (SCC) demonstrated higher BOLD signal fluctuations (fALFF) at baseline in depressed patients, and SCC fALFF decreased over the course of treatment. The baseline level of fALFF of SCC predicted response to ECT. In addition, connectivity of SCC with bilateral hippocampus, bilateral temporal pole, and ventromedial prefrontal cortex was significantly reduced over the course of treatment. These results suggest that the antidepressant effect of ECT may be mediated by downregulation of SCC activity and connectivity. SCC function may serve as an important biomarker of target engagement in the development of novel therapies for depression that is resistant to treatment with standard medications. PMID:27115120

  9. Subgenual cingulate cortical activity predicts the efficacy of electroconvulsive therapy

    PubMed Central

    Argyelan, M; Lencz, T; Kaliora, S; Sarpal, D K; Weissman, N; Kingsley, P B; Malhotra, A K; Petrides, G

    2016-01-01

    Electroconvulsive therapy (ECT) is the most effective treatment for depression, yet its mechanism of action is unknown. Our goal was to investigate the neurobiological underpinnings of ECT response using longitudinally collected resting-state functional magnetic resonance imaging (rs-fMRI) in 16 patients with treatment-resistant depression and 10 healthy controls. Patients received bifrontal ECT 3 times a week under general anesthesia. We acquired rs-fMRI at three time points: at baseline, after the 1st ECT administration and after the course of the ECT treatment; depression was assessed with the Hamilton Depression Rating Scale (HAM-D). The primary measure derived from rs-fMRI was fractional amplitude of low frequency fluctuation (fALFF), which provides an unbiased voxel-wise estimation of brain activity. We also conducted seed-based functional connectivity analysis based on our primary findings. We compared treatment-related changes in HAM-D scores with pre- and post-treatment fALFF and connectivity measures. Subcallosal cingulate cortex (SCC) demonstrated higher BOLD signal fluctuations (fALFF) at baseline in depressed patients, and SCC fALFF decreased over the course of treatment. The baseline level of fALFF of SCC predicted response to ECT. In addition, connectivity of SCC with bilateral hippocampus, bilateral temporal pole, and ventromedial prefrontal cortex was significantly reduced over the course of treatment. These results suggest that the antidepressant effect of ECT may be mediated by downregulation of SCC activity and connectivity. SCC function may serve as an important biomarker of target engagement in the development of novel therapies for depression that is resistant to treatment with standard medications. PMID:27115120

  10. A neural network model for olfactory glomerular activity prediction

    NASA Astrophysics Data System (ADS)

    Soh, Zu; Tsuji, Toshio; Takiguchi, Noboru; Ohtake, Hisao

    2012-12-01

    Recently, the importance of odors and methods for their evaluation have seen increased emphasis, especially in the fragrance and food industries. Although odors can be characterized by their odorant components, their chemical information cannot be directly related to the flavors we perceive. Biological research has revealed that neuronal activity related to glomeruli (which form part of the olfactory system) is closely connected to odor qualities. Here we report on a neural network model of the olfactory system that can predict glomerular activity from odorant molecule structures. We also report on the learning and prediction ability of the proposed model.

  11. Age-related differences in predictive response timing in children: evidence from regularly relative to irregularly paced reaction time performance.

    PubMed

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

    2012-08-01

    Predictive timing refers to the anticipation and precise timing of planned motor responses. This study was performed to investigate children's predictive response timing abilities while accounting for confounding age-related effects of motor speed. Indices of predictive timing were evaluated for their contributions in motor skill proficiency as well. Eighty typically developing children in 4 age groups (5-6, 7-8, 9-10 and 11-12 years) performed a visuomotor reaction time (RT) test. Differences in speed and anticipatory responding at regularly relative to irregularly paced stimuli were evaluated as indices of predictive timing. Also, explicit timing and motor tests (M-ABC-2, VMI tracing, and KTK jumping) were administered. Significant faster responding for regularly versus irregularly paced stimuli was found from the ages of 9-10 years on. Better anticipatory responding behavior for regular in contrast with irregular stimuli was found to be present already at 7-8 years. Overall, predictive timing abilities increased across the 4 age groups. Also, inter-individual differences in the speed indices of predictive timing contributed to predicting VMI tracing and KTK jumping outcomes when controlling for age and overall motor response speed. In conclusion, predictive motor timing abilities increase during age 5 to 12 and correlate with motor skill performance. PMID:22494922

  12. 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.

  13. Stock price change rate prediction by utilizing social network activities.

    PubMed

    Deng, Shangkun; 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

  14. 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

  15. Real-time emissions from construction equipment compared with model predictions.

    PubMed

    Heidari, Bardia; Marr, Linsey C

    2015-02-01

    The construction industry is a large source of greenhouse gases and other air pollutants. Measuring and monitoring real-time emissions will provide practitioners with information to assess environmental impacts and improve the sustainability of construction. We employed a portable emission measurement system (PEMS) for real-time measurement of carbon dioxide (CO), nitrogen oxides (NOx), hydrocarbon, and carbon monoxide (CO) emissions from construction equipment to derive emission rates (mass of pollutant emitted per unit time) and emission factors (mass of pollutant emitted per unit volume of fuel consumed) under real-world operating conditions. Measurements were compared with emissions predicted by methodologies used in three models: NONROAD2008, OFFROAD2011, and a modal statistical model. Measured emission rates agreed with model predictions for some pieces of equipment but were up to 100 times lower for others. Much of the difference was driven by lower fuel consumption rates than predicted. Emission factors during idling and hauling were significantly different from each other and from those of other moving activities, such as digging and dumping. It appears that operating conditions introduce considerable variability in emission factors. Results of this research will aid researchers and practitioners in improving current emission estimation techniques, frameworks, and databases. PMID:25947047

  16. 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.

  17. 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.

  18. 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…

  19. A Heat Unit Model for Predicting Blackberry Flowering Time

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Spring temperature fluctuations are often the cause of crop loss in blackberry. The maximum sensitivity to cold temperatures occurs at or near bloom, when temperatures only a few degrees below freezing will damage the pistils. The ability to predict bloom date and identify genotypes that are consi...

  20. 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.

  1. Dynamic Divisive Normalization Predicts Time-Varying Value Coding in Decision-Related Circuits

    PubMed Central

    LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W.

    2014-01-01

    Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. PMID:25429145

  2. Dynamic divisive normalization predicts time-varying value coding in decision-related circuits.

    PubMed

    Louie, Kenway; LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W

    2014-11-26

    Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. PMID:25429145

  3. 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.

  4. Predicting Work Activities with Divergent Thinking Tests: A Longitudinal Study

    ERIC Educational Resources Information Center

    Clapham, Maria M.; Cowdery, Edwina M.; King, Kelly E.; Montang, Melissa A.

    2005-01-01

    This study examined whether divergent thinking test scores obtained from engineering students during college predicted creative work activities fifteen years later. Results showed that a subscore of the "Owens Creativity Test", which assesses divergent thinking about mechanical objects, correlated significantly with self-ratings of creative work…

  5. 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…

  6. Biomarkers in Coronary Artery Bypass Surgery: Ready for Prime Time and Outcome Prediction?

    PubMed Central

    Parolari, Alessandro; Poggio, Paolo; Myasoedova, Veronika; Songia, Paola; Bonalumi, Giorgia; Pilozzi, Alberto; Pacini, Davide; Alamanni, Francesco; Tremoli, Elena

    2016-01-01

    Coronary artery bypass surgery (CABG) is still one of the most frequently performed surgical procedures all over the world. The results of this procedure have been constantly improved over the years with low perioperative mortality rates, with relatively low complication rates. To further improve these outstanding results, the clinicians focused their attention at biomarkers as outcome predictors. Although biological testing for disease prediction has already been discussed many times, the role of biomarkers in outcome prediction after CABG is still controversial. In this article, we reviewed the current knowledge regarding the role of genetic and dynamic biomarkers and their possible association with the occurrence of adverse clinical outcomes after CABG. We also took into consideration that the molecular pathway activation and the possible imbalance may affect hard outcomes and graft patency. We analyzed biomarkers classified in two different categories depending on their possibility to change over time: genetic markers and dynamic markers. Moreover, we evaluated these markers by dividing them, into sub-categories, such as inflammation, hemostasis, renin–angiotensin, endothelial function, and other pathways. We showed that biomarkers might be associated with unfavorable outcomes after surgery, and in some cases improved outcome prediction. However, the identification of a specific panel of biomarkers or of some algorithms including biomarkers is still in an early developmental phase. Finally, larger studies are needed to analyze broad panel of biomarkers with the specific aim to evaluate the prediction of hard outcomes and graft patency. PMID:26779491

  7. Timing of food intake predicts weight loss effectiveness

    Technology Transfer Automated Retrieval System (TEKTRAN)

    There is emerging literature demonstrating a relationship between the timing of feeding and weight regulation in animals. However, whether the timing of food intake influences the success of a weight-loss diet in humans is unknown. To evaluate the role of food timing in weight-loss effectiveness in ...

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

    PubMed

    Mochizuki, Koji; Takayama, Kozo

    2016-07-01

    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. PMID:26559666

  9. 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. PMID:22379700

  10. Individual differences in activation of the parental care motivational system: assessment, prediction, and implications.

    PubMed

    Buckels, Erin E; Beall, Alec T; Hofer, Marlise K; Lin, Eden Y; Zhou, Zenan; Schaller, Mark

    2015-03-01

    We report on the development, validation, and utility of a measure assessing individual differences in activation of the parental care motivational system: The Parental Care and Tenderness (PCAT) questionnaire. Results from 1,608 adults (including parents and nonparents) show that the 25-item PCAT measure has high internal consistency, high test-retest reliability, high construct validity, and unique predictive utility. Among parents, it predicted self-child identity overlap and caring child-rearing attitudes; among nonparents, it predicted desire to have children. PCAT scores predicted the intensity of tender emotions aroused by infants, and also predicted the amount of time individuals chose look at infant (but not adult) faces. PCAT scores uniquely predicted additional outcomes in the realm of social perception, including mate preferences, moral judgments, and trait inferences about baby-faced adults. Practical and conceptual implications are discussed. PMID:25559194

  11. 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

  12. Prediction of leisure-time walking: an integration of social cognitive, perceived environmental, and personality factors

    PubMed Central

    Rhodes, Ryan E; Courneya, Kerry S; Blanchard, Chris M; Plotnikoff, Ronald C

    2007-01-01

    Background Walking is the primary focus of population-based physical activity initiatives but a theoretical understanding of this behaviour is still elusive. The purpose of this study was to integrate personality, the perceived environment, and planning into a theory of planned behaviour (TPB) framework to predict leisure-time walking. Methods Participants were a random sample (N = 358) of Canadian adults who completed measures of the TPB, planning, perceived neighbourhood environment, and personality at Time 1 and self-reported walking behaviour two months later. Results Analyses using structural equation modelling provided evidence that leisure-time walking is largely predicted by intention (standardized effect = .42) with an additional independent contribution from proximity to neighbourhood retail shops (standardized effect = .18). Intention, in turn, was predicted by attitudes toward walking and perceived behavioural control. Effects of perceived neighbourhood aesthetics and walking infrastructure on walking were mediated through attitudes and intention. Moderated regression analysis showed that the intention-walking relationship was moderated by conscientiousness and proximity to neighbourhood recreation facilities but not planning. Conclusion Overall, walking behaviour is theoretically complex but may best be addressed at a population level by facilitating strong intentions in a receptive environment even though individual differences may persist. PMID:17974022

  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. PMID:27019753

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

    PubMed

    McCowan, Luke S C; 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

  15. 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

  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. PMID:24465462

  17. Predicting Active Users' Personality Based on Micro-Blogging Behaviors

    PubMed Central

    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 845 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. 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. PMID:24465462

  18. 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…

  19. 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.'

  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 h) 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 was 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. Site specific water compositions, including time-integrated dissolved metal concentrations determined from DGT, a competitive, multiple-toxicant biotic ligand model, and the Windemere Humic Aqueous Model Version 6.0 (WHAM VI) were used to determine the equilibrium 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. 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

  2. An ultrasound personal locator for time-activity assessment.

    PubMed

    Allen-Piccolo, Gian; Rogers, Jamesine V; Edwards, Rufus; Clark, Michael C; Allen, T Tracy; Ruiz-Mercado, Ilse; Shields, Kyra N; Canuz, Eduardo; Smith, Kirk R

    2009-01-01

    The UC Berkeley Time-Activity Monitoring System (UCB-TAMS) was developed to measure time-activity in exposure studies. The system consists of small, light, inexpensive battery-operated 40-kHz ultrasound transmitters (tags) worn by participants and an ultrasound receiver (locator) attached to a datalogger fixed in an indoor location. Presence or absence of participants is monitored by distinguishing the unique ultrasound ID of each tag. Efficacy tests in rural households of highland Guatemala showed the system to be comparable to the gold-standard time-activity measure of direct observation by researchers, with an accuracy of predicting time-weighted averages of 90-95%, minute-by-minute accuracy of 80-85%, and sensitivity/specificity values of 86-89%/71-74% for one-minute readings on children 3-8 years-old. Additional controlled tests in modern buildings and in rural Guatemalan homes confirmed the performance of the system with the presence of other ultrasound sources, with multiple tags, covered by clothing, and in other non-ideal circumstances. PMID:19496478

  3. Prediction of Probabilistic Sleep Distributions Following Travel Across Multiple Time Zones

    PubMed Central

    Darwent, David; Dawson, Drew; Roach, Greg D.

    2010-01-01

    Study Objectives: To parameterize and validate a model to estimate average sleep times for long-haul aviation pilots during layovers following travel across multiple time zones. The model equations were based on a weighted distribution of domicile- and local-time sleepers, and included algorithms to account for sleep loss and circadian re-synchronization. Design: Sleep times were collected from participants under normal commercial operating conditions using diaries and wrist activity monitors. Participants: Participants included a total of 306 long-haul pilots (113 captains, 120 first officers, and 73 second officers). Measurement and Results: The model was parameterized based on the average sleep/wake times observed during international flight patterns from Australia to London and Los Angeles (global R2 = 0.72). The parameterized model was validated against the average sleep/wake times observed during flight patterns from Australia to London (r2 = 0.85), Los Angeles (r2 = 0.79), New York (r2 = 0.80), and Johannesburg (r2 = 0.73). Goodness-of-fit was poorer when the parameterized model equations were used to predict the variance across the sleep/wake cycles of individual pilots (R2 = 0.42, 0.35, 0.31, and 0.28 for the validation flight patterns, respectively), in part because of substantial inter-individual variability in sleep timing and duration. Conclusions: It is possible to estimate average sleep times during layovers in international patterns with a reasonable degree of accuracy. Models of this type could form the basis of a stand-alone application to estimate the likelihood that a given duty schedule provides pilots, on average, with an adequate opportunity to sleep. Citation: Darwent D; Dawson D; Roach GD. Prediction of probabilistic sleep distributions following travel across multiple time zones. SLEEP 2010;33(2):185-195. PMID:20175402

  4. PREDICTION OF AGRICULTURAL WORKER SAFETY REENTRY TIMES FOR ORGANOPHOSPHATE INSECTICIDES

    EPA Science Inventory

    Concepts and current methods of determining worker safety reentry times are reviewed. Comparison of human monitoring studies, factors in a worker reentry episode and exposure estimation methods illustrate the advantages of estimation methods. Research needs for worker reentry tim...

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

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

    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.

  6. 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.

  7. Compulsivity predicts fronto striatal activation in severely anorectic individuals.

    PubMed

    Rothemund, Y; Buchwald, C; Georgiewa, P; Bohner, G; Bauknecht, H-C; Ballmaier, M; Klapp, B F; Klingebiel, R

    2011-12-01

    Anorexia nervosa is a severe illness and shows one of the highest death rates among psychiatric or psychosomatic diseases. However, despite several lines of research, the etiology of this disease is still unknown. One of those features is the rigidity of behaviors, for example, controlling of weight and pursuing of thinness, that often meets the criteria for obsessive-compulsive behavior. In this study, it was investigated whether the clinical feature of compulsivity in anorexia nervosa patients relates to regional brain activation. Using functional magnetic resonance imaging, 12 severely anorectic women were compared to 12 normal-weight female individuals following a cue-reactivity paradigm. Cues comprised food cues of high and low calorie content as well as eating-related utensils. Voxel-based morphometric analysis indicated significantly overall reduced gray matter volume and significantly increased cerebrospinal fluids in anorexia nervosa (AN) patients, which was controlled for in subsequent analyses. Following the high-calorie stimulation, AN patients activated the right caudate body and right precuneus, whereas control subjects did not show significant regional activations. In both other conditions, low-calorie foods and eating utensils, regional brain activations did not survive FDR thresholds. During the high-calorie condition, compulsivity, that is, the subscore "obsessive thoughts," predicted activation of the superior frontal gyrus [Brodmann areas (BA) 10], inferior frontal gyrus, anterior cingulate cortex (BA 32), cingulate gyrus (BA 24), caudate body, cuneus, pre- and postcentral gyrus. The subscore "compulsive acts" correlated with activation of the claustrum during the high-calorie condition and predicted a number of deactivations of frontal and temporal regions. We conclude that in severely anorectic individuals, the degree of compulsivity predicts activation and deactivation of the fronto-striatal pathway. PMID:21952129

  8. An evaluation of rise time characterization and prediction methods

    NASA Technical Reports Server (NTRS)

    Robinson, Leick D.

    1994-01-01

    One common method of extrapolating sonic boom waveforms from aircraft to ground is to calculate the nonlinear distortion, and then add a rise time to each shock by a simple empirical rule. One common rule is the '3 over P' rule which calculates the rise time in milliseconds as three divided by the shock amplitude in psf. This rule was compared with the results of ZEPHYRUS, a comprehensive algorithm which calculates sonic boom propagation and extrapolation with the combined effects of nonlinearity, attenuation, dispersion, geometric spreading, and refraction in a stratified atmosphere. It is shown there that the simple empirical rule considerably overestimates the rise time estimate. In addition, the empirical rule does not account for variations in the rise time due to humidity variation or propagation history. It is also demonstrated that the rise time is only an approximate indicator of perceived loudness. Three waveforms with identical characteristics (shock placement, amplitude, and rise time), but with different shock shapes, are shown to give different calculated loudness. This paper is based in part on work performed at the Applied Research Laboratories, the University of Texas at Austin, and supported by NASA Langley.

  9. 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

  10. 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).…

  11. Toward the Prediction of FBPase Inhibitory Activity Using Chemoinformatic Methods

    PubMed Central

    Hao, Ming; Zhang, Shuwei; Qiu, Jieshan

    2012-01-01

    Currently, Chemoinformatic methods are used to perform the prediction for FBPase inhibitory activity. A genetic algorithm-random forest coupled method (GA-RF) was proposed to predict fructose 1,6-bisphosphatase (FBPase) inhibitors to treat type 2 diabetes mellitus using the Mold2 molecular descriptors. A data set of 126 oxazole and thiazole analogs was used to derive the GA-RF model, yielding the significant non-cross-validated correlation coefficient r2ncv and cross-validated r2cv values of 0.96 and 0.67 for the training set, respectively. The statistically significant model was validated by a test set of 64 compounds, producing the prediction correlation coefficient r2pred of 0.90. More importantly, the building GA-RF model also passed through various criteria suggested by Tropsha and Roy with r2o and r2m values of 0.90 and 0.83, respectively. In order to compare with the GA-RF model, a pure RF model developed based on the full descriptors was performed as well for the same data set. The resulting GA-RF model with significantly internal and external prediction capacities is beneficial to the prediction of potential oxazole and thiazole series of FBPase inhibitors prior to chemical synthesis in drug discovery programs. PMID:22837677

  12. 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.

  13. Posture and Activity Recognition and Energy Expenditure Prediction in a Wearable Platform

    PubMed Central

    Sazonov, Edward; Hegde, Nagaraj; Browning, Raymond C.; Melanson, Edward L.; Sazonova, Nadezhda A.

    2015-01-01

    The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here we describe the use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time recognition of various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we compare use of Support Vector Machines (SVM), Multinomial Logistic Discrimination (MLD), and Multi-Layer Perceptrons (MLP) for posture and activity classification followed by activity-branched EE estimation. The algorithms were validated using data from 15 subjects who performed up to 15 different activities of daily living during a four-hour stay in a room calorimeter. MLD and MLP demonstrated activity classification accuracy virtually identical to SVM (~95%), while reducing the running time and the memory requirements by a factor of >103. Comparison of perminute EE estimation using activity-branched models resulted in accurate EE prediction (RMSE=0.78 kcal/min for SVM and MLD activity classification, 0.77 kcal/min for MLP, vs. RMSE of 0.75 kcal/min for manual annotation). These results suggest that low-power computational algorithms can be successfully used for real-time physical activity monitoring and EE prediction on a wearable platform. PMID:26011870

  14. 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…

  15. 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.…

  16. Emerging Object Representations in the Visual System Predict Reaction Times for Categorization

    PubMed Central

    Ritchie, J. Brendan; Tovar, David A.; Carlson, Thomas A.

    2015-01-01

    Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or “brain decoding”, methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of object categories that underlie our capacity for rapid recognition. Shortly after stimulus onset, object exemplars cluster by category in a high-dimensional activation space in the brain. In this emerging activation space, the decodability of exemplar category varies over time, reflecting the brain’s transformation of visual inputs into coherent category representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. When viewed in the context of a time varying neural signal, the optimal time to “read out” category information is when category representations in the brain are most decodable. Here, we show that the distance from a decision boundary through activation space, as measured using MEG decoding methods, correlates with reaction times for visual categorization during the period of peak decodability. Our results suggest that the brain begins to read out information about exemplar category at the optimal time for use in choice behaviour, and support the hypothesis that the structure of the representation for objects in the visual system is partially constitutive of the decision process in recognition. PMID:26107634

  17. Prefrontal Activity Predicts Monkeys' Decisions During an Auditory Category Task

    PubMed Central

    Lee, Jung H.; Russ, Brian E.; Orr, Lauren E.; Cohen, Yale E.

    2009-01-01

    The neural correlates that relate auditory categorization to aspects of goal-directed behavior, such as decision-making, are not well understood. Since the prefrontal cortex (PFC) plays an important role in executive function and the categorization of auditory objects, we hypothesized that neural activity in the PFC should predict an animal's behavioral reports (decisions) during a category task. To test this hypothesis, we tested PFC activity that was recorded while monkeys categorized human spoken words (Russ et al., 2008b). We found that activity in the ventrolateral PFC, on average, correlated best with the monkeys' choices than with the auditory stimuli. This finding demonstrates a direct link between PFC activity and behavioral choices during a non-spatial auditory task. PMID:19587846

  18. Real time prediction of sea level anomaly data with the Prognocean system - comparison of results obtained using different prediction techniques

    NASA Astrophysics Data System (ADS)

    Mizinski, Bartlomiej; Niedzielski, Tomasz; Kosek, Wieslaw

    2013-04-01

    Prognocean is a near-real time modeling and prediction system elaborated and based at University of Wroclaw, Poland. It operates on gridded Sea Level Anomaly (SLA) data obtained from the Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO), France. The data acquisition flow from AVISO to Prognocean is entirely automatic and is implemented in Python. The core of the system - including data pre-processing, modeling, prediction, validation and visualization procedures - is composed of a series of R scripts that are interrelated and work at three levels of generalization. The objective of the work presented here is to show the results of our numerical experiment that have been carried out since early 2012. Four prediction models have been implemented to date: (1) extrapolation of polynomial-harmonic model and the extrapolation of polynomial-harmonic model with (2) autoregressive model, (3) threshold autoregressive model and (4) autocovariance procedure. Although the presentation is limited to four models and their predictive skills, Prognocean consists of modules and hence new techniques may be plugged in at any time. In this paper, the comparison of the results into forecasting sea level anomaly maps is presented. Along with sample predictions, with various lead times up to two weeks, we present and discuss a set of root mean square prediction error maps computed in real time after the observations have been available. We identified areas where linear prediction models reveal considerable errors, which may indicate a non-linear mode of sea level change. In addition, we have identified an agreement between the spatial pattern of large prediction errors and the spatial occurrence of key mesoscale ocean eddies.

  19. 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.

  20. 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. PMID:26320866

  1. 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.

  2. Relative Unisensory Strength and Timing Predict Their Multisensory Product

    PubMed Central

    Pluta, Scott R.; Stein, Barry E.; Rowland, Benjamin A.

    2015-01-01

    Understanding the principles by which the brain combines information from different senses provides us with insight into the computational strategies used to maximize their utility. Prior studies of the superior colliculus (SC) neuron as a model suggest that the relative timing with which sensory cues appear is an important factor in this context. Cross-modal cues that are near-simultaneous are likely to be derived from the same event, and the neural inputs they generate are integrated more strongly than those from cues that are temporally displaced from one another. However, the present results from studies of cat SC neurons show that this “temporal principle” of multisensory integration is more nuanced than previously thought and reveal that the integration of temporally displaced sensory responses is also highly dependent on the relative efficacies with which they drive their common target neuron. Larger multisensory responses were achieved when stronger responses were advanced in time relative to weaker responses. This new temporal principle of integration suggests an inhibitory mechanism that better accounts for the sensitivity of the multisensory product to differences in the timing of cross-modal cues than do earlier mechanistic hypotheses based on response onset alignment or response overlap. PMID:25834047

  3. 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. PMID:23990938

  4. Can vesicle size distributions predict eruption intensity during volcanic activity?

    NASA Astrophysics Data System (ADS)

    LaRue, A.; Baker, D. R.; Polacci, M.; Allard, P.; Sodini, N.

    2013-06-01

    We studied three-dimensional (3-D) vesicle size distributions by X-ray microtomography in scoria collected during the relatively quiescent Phase II of the 2010 eruption at Eyjafjallajökull volcano, Iceland. Our goal was to compare the vesicle size distributions (VSDs) measured in these samples with those found in Stromboli volcano, Italy. Stromboli was chosen because its VSDs are well-characterized and show a correlation with eruption intensity: typical Strombolian activity produces VSDs with power-law exponents near 1, whereas larger and more energetic Vulcanian-type explosions and Plinian eruptions produce VSDs with power-law exponents near 1.5. The hypothesis to be tested was whether or not the samples studied in this work would contain VSDs similar to normal Strombolian products, display higher power-law exponents, or be described by exponential functions. Before making this comparison we tested the hypothesis that the phreatomagmatic nature of the Eyjafjallajökull eruption might have a significant effect on the VSDs. We performed 1 atm bubble-growth experiments in which the samples were inundated with water and compared them to similar, control, experiments without water inundation. No significant differences between the VSDs of the two sets of experiments were found, and the hypothesis is not supported by the experimental evidence; therefore, VSDs of magmatic and phreatomagmatic eruptions can be directly compared. The Phase II Eyjafjallajökull VSDs are described by power law exponents of ~ 0.8, typical of normal Strombolian eruptions. The comparable VSDs and behavior of Phase II of the Eyjafjallajökull 2010 eruption to Stromboli are interpreted to be a reflection of similar conduit systems in both volcanoes that are being constantly fed by the ascent of deep magma that mixes with resident magma at shallow depths. Such behavior implies that continued activity during Phase II of the Eyjafjallajökull eruption could be expected and would have been predicted

  5. 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.

  6. 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.

  7. 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.

  8. When univariate model-free time series prediction is better than multivariate

    NASA Astrophysics Data System (ADS)

    Chayama, Masayoshi; Hirata, Yoshito

    2016-07-01

    The delay coordinate method is known to be a practically useful technique for reconstructing the states of an observed system. While this method is theoretically supported by Takens' embedding theorem concerning observations of a scalar time series, we can extend the method to include a multivariate time series. It is often assumed that a better prediction can be obtained using a multivariate time series than by using a scalar time series. However, multivariate time series contains various types of information, and it may be difficult to extract information that is useful for predicting the states. Thus, univariate prediction may sometimes be superior to multivariate prediction. Here, we compare univariate model-free time series predictions with multivariate ones, and demonstrate that univariate model-free prediction is better than multivariate one when the prediction steps are small, while multivariate prediction performs better when the prediction steps become larger. We show the validity of the former finding by using artificial datasets generated from the Lorenz 96 models and a real solar irradiance dataset. The results indicate that it is possible to determine which method is the best choice by considering how far into the future we want to predict.

  9. Resource assurance predicts specialist and generalist bee activity in drought

    PubMed Central

    Minckley, Robert L.; Roulston, T'ai H.; Williams, Neal M.

    2013-01-01

    Many short-lived desert organisms remain in diapause during drought. Theoretically, the cues desert species use to continue diapause through drought should differ depending on the availability of critical resources, but the unpredictability and infrequent occurrence of climate extremes and reduced insect activity during such events make empirical tests of this prediction difficult. An intensive study of a diverse bee–plant community through a drought event found that bee specialists of a drought-sensitive host plant were absent in the drought year in contrast to generalist bees and to specialist bees of a drought-insensitive host plant. Different responses of bee species to drought indicate that the diapause cues used by bee species allow them to reliably predict host availability. Species composition of the bee community in drought shifted towards mostly generalist species. However, we predict that more frequent and extended drought, predicted by climate change models for southwest North America, will result in bee communities that are species-poor and dominated by specialist species, as found today in the most arid desert region of North America. PMID:23536593

  10. Near Real Time MISR Wind Observations for Numerical Weather Prediction

    NASA Astrophysics Data System (ADS)

    Mueller, K. J.; Protack, S.; Rheingans, B. E.; Hansen, E. G.; Jovanovic, V. M.; Baker, N.; Liu, J.; Val, S.

    2014-12-01

    The Multi-angle Imaging SpectroRadiometer (MISR) project, in association with the NASA Langley Atmospheric Science Data Center (ASDC), has this year adapted its original production software to generate near-real time (NRT) cloud-motion winds as well as radiance imagery from all nine MISR cameras. These products are made publicly available at the ASDC with a latency of less than 3 hours. Launched aboard the sun-synchronous Terra platform in 1999, the MISR instrument continues to acquire near-global, 275 m resolution, multi-angle imagery. During a single 7 minute overpass of any given area, MISR retrieves the stereoscopic height and horizontal motion of clouds from the multi-angle data, yielding meso-scale near-instantaneous wind vectors. The ongoing 15-year record of MISR height-resolved winds at 17.6 km resolution has been validated against independent data sources. Low-level winds dominate the sampling, and agree to within ±3 ms-1 of collocated GOES and other observations. Low-level wind observations are of particular interest to weather forecasting, where there is a dearth of observations suitable for assimilation, in part due to reliability concerns associated with winds whose heights are assigned by the infrared brightness temperature technique. MISR cloud heights, on the other hand, are generated from stereophotogrammetric pattern matching of visible radiances. MISR winds also address data gaps in the latitude bands between geostationary satellite coverage and polar orbiting instruments that obtain winds from multiple overpasses (e.g. MODIS). Observational impact studies conducted by the Naval Research Laboratory (NRL) and by the German Weather Service (Deutscher Wetterdienst) have both demonstrated forecast improvements when assimilating MISR winds. An impact assessment using the GEOS-5 system is currently in progress. To benefit air quality forecasts, the MISR project is currently investigating the feasibility of generating near-real time aerosol products.